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Automated Negotiation for Ensuring Composite Service Requirements in Cloud Computing
Abstract
Cloud consumers need services that, in addition to meeting their business requirements, provide them with a certain level of quality of service (QoS). On the other hand, cloud providers desire to sale services corresponding to their preferences. In this situation, cloud computing service negotiation (CCSN) can be used to establish an agreement among trading parties with conflicting preferences. The CCSN allows consumer and provider negotiate together automatically on negotiable issues that are important for both trading parties. It aims to provide maximum utility for the parties in possible shortest time. In this paper, we propose a CCSN which provides simple or composite services to consumers. We introduce strategies that negotiators can choose from. We carry out some simulations to compare the performance of the strategies. Analysis of the results of simulations shows that our recommended strategy is more efficient in terms of negotiator's utility and the number of rounds spent on negotiation to reach an agreement than the others. The contributions of the proposed CCSN can be summarized as follows: (1) design and simulation of a new negotiation strategy which aims to maximize utility for both trading parties and increase the speed in reaching an agreement, (2) proposing a process for aggregating the results of negotiations on simple task requirements to ensure end-to-end composite service requirements.
... It offers solutions, support, and development environments over the internet for software, platform, and infrastructure. Cloud Computing, including the internet of things, fog computing, and edge computing, has recently been responsible for developing new business trends and hastened the scientific research pace [7][8][9]. The term "Software as a Service" (SaaS), "Platform as a Service" (PaaS), and "Infrastructure as a Service" (IaaS) refers to the act of providing customers with applications that ought to be accommodated by a cloud service provider (CSP) and delivered to them via the internet [1,3]. ...
... IANA can provide services including negotiating, decision-making, resource management, cooperating with other interlinked cloud service providers (also known as inter-cloud), knowledge acquisition, behaviour analysis, monitoring, and maximizing success rate in the shortest possible time. IANA ensures that user requests for cloud services are met within the parameters of minimum reaction time, maximum availability, reliable server, and services offered at the lowest possible cost [8,24]. ...
... IANA focuses on the negotiation cycle, the negotiation matrix, and performance measures in the process of negotiations [5,[22][23][24]26]. Figure 3 depicts a one-to-one negotiating process in which consumers and CSP use different tools to analyze the SLA [2,7,8]. The consumer puts SLA requirements into the SLA generator and submits requirements to the CSP. ...
With the help of an intelligent agent, a cloud service provider (CSP) offers various services and resources to their customers over the internet. Consumers and CSPs in the cloud market are in a constant state of negotiation over their individual needs. Competition among CSPs is fierce because of the enormous number of CSPs in the market. Intelligent negotiation agents are highly demanding as a timesaving and success rate-enhancing tool. While negotiating, multiple attributes such as price, resources, reliability, CSP performance, utility value, and many more are considered. There’s increased false negotiations, faulty servers, and exorbitant fees are all issues arising because of the forever changing CSP market. An intelligent and automated negotiation agent (IANA) understands the conduct of the opponent and the needs of both parties. This need of users and CSP information is utilized in negotiations to find the best possible delivery of services between the customer and the CSP. As a result of the proposed design, negotiations are expedited, and there is a higher success rate and better oversight of service delivery.
... Some CSPs have substantial functioning properties, while others have more flexible payment options. Some CSPs give more service management services, while others provide exceptional dependability and strong SLAs [6,9,[11][12][13]. Furthermore, CSPs often offer the same services with varying performance and functionality and variable pricing. ...
... For example, article [15] proposed a cloud computing framework consisting of users, service agents, and resource providers, in which resource agents aid in collaboration between cloud users and cloud service providers. Similarly, a concept proposed by the author is known as the cloud services agent intermediary, which is responsible for connecting users and cloud service providers through the auction mechanism [9,13,37,38]. Discussed methods allow clouds to operate in a market-oriented manner. Still, many challenges remain in developing an efficient cloud service market, particularly in meeting users' quality of service requirements [15,39,40]. ...
... After the recommendation process, a user and CSP might negotiate to agree on essential bar-gained attributes from the standpoints of both trading parties. When incorporating utility considerations in the hybrid environment, the author's [13] automated negotiation method displays good negotiating speed under a limited set of circumstances. In another paper [12], the negotiation three-phase process is discussed with a recommendation strategy. ...
As the field of information technology expands, there is a huge need for cloud service providers (CSP). CSP’s vast solutions and services support Cloud, IoT, Fog, and Edge computing. In today’s competitive cloud market, customer satisfaction is critical more than ever. CSP and consumer satisfaction with service level agreement (SLA) fulfillment have always been given more attention. As a result of signing SLA and CSP agreements to supply resources in high demand, customers are now experiencing issues with resource delivery. Cloud and heterogeneous environments necessitate an intelligent recommender and negotiation agent model (IRNAM) to handle responsibilities in the current system. The Recommender system recommends CSP as per users’ priorities, which eases the filtration process. The negotiation process provided by IRNAM ensures that users’ choices are prioritized with maximum jobs to CSP. IRNAM keeps track of the most critical metrics and can reach decisions quickly and for the best possible deal. It uses an analytical concession algorithm that analyzes consumer and CSP choices to find a reliable, secure server with the simplest solution. The negotiation process uses user’s and CSP choice metrics, performance factors, evaluation measures, and success factors in the best execution time to decide. IRNAM provides a flexible and valuable way for selecting CSP and negotiating for services on the user’s terms while considering CSP satisfaction.
... The negotiation strategy can be optimized either in the context of pre-request optimization or long-term optimization [7,8]. In the context of pre-request optimization, the broker strategy can be a model for the optimization of the utility (profit) function under several quality constraints. ...
... Eq. (8) shows that the probability distribution with residual ...
... The trade-off behaviour is aggressive and rational in nature. It exhibits zero degree of concession during the multi-issue and counterproposal generation process as expressed in Eq. (8). ...
Optimization of the broker negotiation strategy is one of the challenging issues observed in the cloud-based e-commerce negotiation framework. This strategy can be optimized either in the context of pre-request optimization or long-term optimization. Most researchers have focused on the context of pre-request optimization using various utility functions (such as time, opportunity, and competition). In the context of long-term optimization, the current state-of-the-art negotiation strategies can increase the utility value and success rate of the parties to some extent, but they do not guarantee minimization of the negotiation states (rounds) involved during the negotiation process. In addition, the existing strategies cannot react to the stochastic, rational, emotional, and unknown behaviour of the opponents due to their deterministic behaviour, which may lead to a negotiation break-off between parties. To overcome such limitations, a novel stochastic behavioural learning negotiation (SBLN) strategy is proposed to further maximize the utility value and success rate.
... Therefore, cloud providers with diverse and heterogeneous resources can be grouped together and share their resources with each other to scale up their resource pools and contribute to an integrated solution for improved competitiveness [2,11], which would provide the customer-tailored dynamic composition of cloud services to Page 2 of 26 Li et al. Journal of Cloud Computing (2022) 11:16 satisfy customers with the special quality of service (QoS) requirements [8,12]. However, the intercloud model raises more challenges than the single cloud model in the market, because the intercloud model is a larger-scale distributed and interconnected system composed of individual cloud consumers and providers. ...
... (13) rather than Eq. (12). ...
The evolving intercloud enables idle resources to be traded among cloud providers to facilitate utilization optimization and to improve the cost-effectiveness of the service for cloud consumers. However, several challenges are raised for this multi-tier dynamic market, in which cloud providers not only compete for consumer requests but also cooperate with each other. To establish a healthier and more efficient intercloud ecosystem, in this paper a multi-tier agent-based fuzzy constraint-directed negotiation (AFCN) model for a fully distributed negotiation environment without a broker to coordinate the negotiation process is proposed. The novelty of AFCN is the use of a fuzzy membership function to represent imprecise preferences of the agent, which not only reveals the opponent’s behavior preference but can also specify the possibilities prescribing the extent to which the feasible solutions are suitable for the agent’s behavior. Moreover, this information can guide each tier of negotiation to generate a more favorable proposal. Thus, the multi-tier AFCN can improve the negotiation performance and the integrated solution capacity in the intercloud. The experimental results demonstrate that the proposed multi-tier AFCN model outperforms other agent negotiation models and demonstrates the efficiency and scalability of the intercloud in terms of the level of satisfaction, the ratio of successful negotiation, the average revenue of the cloud provider, and the buying price of the unit cloud resource.
... A mixed-strategy ANS for a CCSN is recommended as a strategy in [31]. In this study, a new negotiation strategy was designed and simulated aiming to maximize utility for both trading parties while the speed in reaching an agreement is increased. ...
The integration of Distributed Energy Resources utilizing Renewable Energy Sources, Energy Storage Systems, and Information and Communication Technologies is transforming traditional energy systems into adaptable, flexible, and sustainable systems, with the Smart Grid concept playing a pivotal role. This paper surveys intelligent techniques and methods applied in various markets and applications, particularly focusing on their potential adaptation for negotiation processes in Smart Grid contexts. The negotiation mechanisms, crucial for prosumers who engage in real-time transactions, are analyzed with a focus on fuzzy logic tools, specifically q-Rung Orthopair Fuzzy Sets. These tools are evaluated for their capability to handle negotiation tasks and Multi-Criteria Decision-Making problems. The paper proposes a negotiation schema for healthcare buildings, especially hospitals, given their significant environmental impact, providing insight for future research.
... The study presented in [27] considers two agents negotiating over four issues which are price, response time, reliability, and availability. However, the reliability and availability (as qualitative issues) are dealt with as a probability while we consider the fuzzy similarity when generating offers for qualitative issues. ...
Automated negotiation is an efficient approach for interaction in multi-agent systems in which agents exchange offers and counteroffers to conclude an agreement. This paper addresses the problem of offer formulation during the interaction between buyer and seller software agents for the purpose of reaching an agreement over quantitative and qualitative issues at once. In order to improve the outcome of the negotiation process, a hybrid negotiation method is presented and verified. Offer formulation is based on fuzzy similarity and preference-based methods. The preference-based mechanism is used for quantitative issues, while the fuzzy similarity technique is used for qualitative issues. The preference-based mechanism takes into account the preferences of the opponent when generating offers; the agent makes greater concessions on the issues which the opponent prefers more. The fuzzy-similarity method formulates an offer that considers offering a deal that is more similar to the one received by the opponent during the last round of negotiation. The experiments consists of two parts. The first part compares the hybrid strategy with the basic one. The findings reveal that the hybrid strategy is better in all performance measures, namely, utility rate, agreement rate, and Nash product rate. The second part of the experimental work compares four mechanisms of offer generating mechanisms: basic, preference-based, fuzzy similarity, and hybrid. The results show that the hybrid negotiation strategy performs equal or better that other negotiation strategies. More details can be found in the paper.
... In another discussion, related to cloud computing mentioned that from the consumer's point of view, they want their business needs to be met and provided with the best quality service. Meanwhile, from the cloud provider side, they want to sell services that suit their preferences [10]. And the last is [11] which uses the multi classifier voting method. ...
The Requirement Engineering phase, where all requests and software requirements of the user and the client are delivered, understood and agreed upon. However, often the developers are just too focused on implementing the software, even though the Requirements Engineering phase is a phase that can have a big impact. The impact is not only on the final product but also on the development process itself. In this study, the authors conducted software development negotiation of software requirements as a medium for stakeholders to negotiate the requirements of software products. In the negotiation system, the author will provide a means of decision support or group decision support system that a method of resolving conflicts. The main objectives of this work are twofold: 1) to assist the negotiation process between stakeholders and 2) to improvement quality software after negotiation. The workings of the E-Voting method are by giving choices to each sub-specification that has been chosen by stakeholders. We will select the choice that has the highest number of votes as a specification. We used prototyping as a method of developing a system life cycle because prototyping is very open to improvements that might occur after it releases the prototype version system. The results of evaluations show that the system has a high success rate based on 3 dimensions of testing, Performance (80.5%), Usability (78.5%), and User Satisfaction (78%).
... Researchers [2] surveyed the need for devising a technique for resolving the Cloud service negotiation challenge that is well aligned with the needs of both users and the providers. The work in [3] presents a method for aggregating the outcomes of simple task requirement in order to ensure end-to-end composite service requirements. The research in [24] provides a novel agent-based technique to negotiate permission for users and services to exchange private data. ...
Cloud computing provisions and allocates resources, in advance or real-time, to dynamic applications planned for execution. This is a challenging task as the Cloud-Service-Providers (CSPs) may not have sufficient resources at all times to satisfy the resource requests of the Cloud-Service-Users (CSUs). Further, the CSPs and CSUs have conflicting interests and may have different utilities. Service-Level-Agreement (SLA) negotiations among CSPs and CSUs can address these limitations. User Agents (UAs) negotiate for resources on behalf of the CSUs and help reduce the overall costs for the CSUs and enhance the resource utilization for the CSPs. This research proposes a broker-based mediation framework to optimize the SLA negotiation strategies between UAs and CSPs in Cloud environment. The impact of the proposed framework on utility, negotiation time, and request satisfaction are evaluated. The empirical results show that these strategies favor cooperative negotiation and achieve significantly higher utilities, higher satisfaction, and faster negotiation speed for all the entities involved in the negotiation.
... Similarly, a provider could rent unused resources to compensate for the cost of maintaining them for more benefit [7]. Therefore, cloud providers with diverse and heterogeneous resources can be grouped together and share their resources with each other to scale up their resource pools and contribute to an integrated solution for improved competitiveness [2,11], which would provide the customer-tailored dynamic composition of cloud services to satisfy customers with the special quality of service (QoS) requirements [8,12]. ...
The evolving intercloud enables idle resources to be traded among cloud providers to facilitate optimizing utilization and to improve the cost-effectiveness of service for cloud consumers. However, several challenges are raised for this multi-tier dynamic market, where cloud providers not only compete for consumer requests but also cooperate with each other. To establish a healthier and more efficient intercloud ecosystem, this paper proposed a multi-tier agent-based fuzzy constraint-directed negotiation (AFCN) model for a fully distributed negotiation environment without a broker to coordinate the negotiation process. The novelty of AFCN is the use of a fuzzy membership function to represent imprecise preferences of the agent, which not only reveals the opponent’s behavior preference but can also specify the possibilities prescribing the extent to which the feasible solutions are suitable for the agent’s behavior. Moreover, this information can pass and guide each tier of negotiation to generate a more favorable proposal. Thus, the multi-tier AFCN can not only improve the performance of negotiation, but also enforce global consistency to improve the integrated solution capacity in the intercloud. The experimental results demonstrate that the proposed multi-tier AFCN model outperforms other agent negotiation models and gives full play to the efficiency and scalability of the intercloud in terms of the level of satisfaction, the ratio of successful negotiation, the average revenue of the cloud provider, and the buying price of the unit cloud resource.
... Similarly, a provider could rent unused resources to compensate for the cost of maintaining them for more benefit [7]. Therefore, cloud providers with diverse and heterogeneous resources can be grouped together and share their resources with each other to scale up their resource pools and contribute to an integrated solution for improved competitiveness [2,11], which would provide the customer-tailored dynamic composition of cloud services to satisfy customers with the special quality of service (QoS) requirements [8,12]. ...
The evolving intercloud enables idle resources to be traded among cloud providers to facilitate optimizing utilization and to improve the cost-effectiveness of service for cloud consumers. However, several challenges are raised for this multi-tier dynamic market, where cloud providers not only compete for consumer requests but also cooperate with each other. To establish a healthier and more efficient intercloud ecosystem, this paper proposed a multi-tier agent-based fuzzy constraint-directed negotiation (AFCN) model for a fully distributed negotiation environment without a broker to coordinate the negotiation process. The novelty of AFCN is the use of a fuzzy membership function to represent imprecise preferences of the agent, which not only reveals the opponent’s behavior preference but can also specify the possibilities prescribing the extent to which the feasible solutions are suitable for the agent’s behavior. Moreover, this information can pass and guide each tier of negotiation to generate a more favorable proposal. Thus, the multi-tier AFCN can not only improve the performance of negotiation, but also enforce global consistency to improve the integrated solution capacity in the intercloud. The experimental results demonstrate that the proposed multi-tier AFCN model outperforms other agent negotiation models and gives full play to the efficiency and scalability of the intercloud in terms of the level of satisfaction, the ratio of successful negotiation, the average revenue of the cloud provider, and the buying price of the unit cloud resource.
... The services are required by cloud consumers for providing them with a certain level of QoS in addition to meeting their business requirements. A cloud computing service negotiation(CCSN) is proposed for providing simple or composite services and strategies to consumers [52]. It proposed a process for aggregation of the results of negotiations on simple task requirements for ensuring endto-end service requirements. ...
Quality of Experience (QoE) is defined as the measure of end-user satisfaction with the service. Existing works addressing QoE-management rely on a binary vision of end-user satisfaction. This vision has been criticized by the growing empirical evidence showing that QoE is rather a degree. This article aims to go beyond the binary vision and propose a QoE management mechanism. We propose a one-to-many negotiation mechanism allowing the provider to undertake satisfaction management: to meet fine-grained user QoE goals, while still minimizing the costs. This problem is formulated as an optimization problem, for which a linear model is proposed. For reference, a generic linear program solver is used to find the optimal solution, and an alternative heuristic algorithm is devised to improve the responsiveness when the system has to scale up with a fast-growing number of users. Both are implemented and experimentally evaluated against state-of-the-art one-to-many negotiation frameworks.
... Cloud computing has been unanimously accepted as the technological solution for different problems in various sectors including businesses, healthcare, factory, farm [1]. With the growth of cloud technologies, cloud-native computing is rising as the de-facto paradigm with Microservices Architecture (MSA)-based service composition for agility and efficiency [2]. ...
As cloud-native computing is becoming the de-facto paradigm in the cloud field, Microservices Architecture has attracted attention from industries and researchers for agility and efficiency. Moreover, with the popularity of the IoT in the context of edge computing, cloud-native applications that utilize geographically-distributed multiple resources are emerging. In line with this trend, there is an increasing demand for microservices placement that selectively use optimal resources. However, optimal microservices placement is a significant challenge because microservices are dynamic and complex, depending on diversified workloads. Besides, generalizing workloads' characteristics consisting of complex microservices is realistically challenging. Thus, microservices deployment with mathematically structured algorithms based on simulation is less practical. As an alternative, a microservices placement framework is required that can reflect the characteristics of workloads derived from empirical profiling. Therefore, in this research work, we propose a refinement framework for profiling-based microservices placement to identify and respond to workload characteristics in a practical way. To achieve this goal, we perform profiling experiments with selected workloads to derive delicate resource requirements. Then, we perform microservices placement with a greedy-based heuristic algorithm that considers application performance by using resource requirements derived from the profiled results. Finally, we verify the proposed concept by comparing the experimental results that use our work and those that don't.
As the field of information technology expands, there is a huge need for cloud service providers (CSP). CSP’s vast solutions and services support Cloud, IoT, Fog, and Edge computing. In today’s competitive cloud market, customer satisfaction is critical more than ever. CSP and consumer satisfaction with service level agreement (SLA) fulfilment have always been given more attention. As a result of signing SLA and CSP agreements to supply resources in high demand, customers are now experiencing issues with resource delivery. Cloud and heterogeneous environments necessitate an intelligent recommender and negotiation agent model (IRNAM) to handle responsibilities in the current system. The Recommender system recommends CSP as per users’ priorities, which eases the filtration process. The negotiation process provided by IRNAM ensures that users’ choices are prioritized with maximum jobs to CSP. IRNAM keeps track of the most critical metrics and can reach decisions quickly and for the best possible deal. It uses an analytical concession algorithm that analyzes consumer and CSP choices to find a reliable, secure server with the simplest solution. The negotiation process uses user’s and CSP choice metrics, performance factors, evaluation measures, and success factors in the best execution time to decide. IRNAM provides a flexible and valuable way for selecting CSP and negotiating for services on the user’s terms while considering CSP satisfaction.
Each cloud service provider provides only an interface of its own cloud infrastructure for enabling clients to use its cloud resources. However, there is a number of difficulties for cloud providers to ensure proper functioning. One of the main problems of a cloud provider is the lack of resources to support a huge number of on‐demand resources provisioning. Thus, resources cannot be distributed among different cloud providers since the federation is not the basic operation of the cloud provider. The most efficient way to overcome this problem is to extend the interface's cloud provider with an automatic negotiation to dynamically form the best agreement between the different cloud providers based on the service level agreement. In this article, we propose an extension for the Open Cloud Computing Interface which is the standardized interface for the cloud computing to support the automatic negotiation between the different cloud providers. To prove the efficiency and the effectiveness of our approach, we implement a prototype to evaluate the key presented in this article.
In this article, we present a theoretical framework for a secure smart architecture based on futuristic edge resource allocation with a key focus on the task off-loading and intelligent enabling technologies. This article aims to highlight the growing need of embedded off-loading standards in the context of the Internet of Everything (IoE), fog, and cloud paradigms enabled technologies in the overall design framework of smart architecture. Efficient task off-loading strategies perform a vital role in mobility, heterogeneity, data and energy management, load balancing, establishing security mechanisms, reducing the propagation delay and information processing. The motive behind presenting such an embedded intelligent and secure architecture is to address the growing concerns of upcoming smart networks and its sub-network based on different futuristic scenarios, applications and devices. Furthermore, we discuss the off-loading criteria and enabling technologies, in view of the new advancement and critical requirements with rising demands of up-coming intelligence-based secure off-loading frameworks, in order to meet the requirements of the secure smart environment. Finally, through our discussion, we develop the specific research questions and explain its motivations in order to design and evaluate the off-loading criteria and its associated enabling technologies to accommodate the secure technological advancements and resource allocation in the sixth generation (6G) wireless communication era.
Virtualized data centers usually consist of heterogeneous servers which have different specifications (performance). Though there usually exist unused heterogeneous servers in such data centers, conventional DVFS (Dynamic Voltage and Frequency Scaling)-based DTM (Dynamic Thermal Management) techniques do not exploit the unused servers to cool down hot servers. In this paper, we propose a novel DTM technique which adaptively exploits external computing resources (unused servers with different performance) as well as internal computing resources (unused CPU cores in the server) available in heterogeneous data centers. Additionally, we also propose to consider locations of the servers when migrating VMs (Virtual Machines) among servers in a rack, which has a large impact on the on-chip temperatures and performance due to the heat conduction; when VMs run on the two closest servers in the rack, the ambient temperature of servers is up to 6.2 degrees higher, compared to the case where VMs run on the two farthest servers, so that on-chip temperature more rapidly increases causing up to 13.5% of performance degradation due to more frequent thermal throttling. When the temperature of a CPU core in a server exceeds a pre-defined thermal threshold, our proposed technique estimates the impact of VM migrations on performance (e.g., performance degradation due to the physical machine migrations and/or core migrations of VMs). Depending on the estimated performance impact of VM migrations, our technique adaptively employs the following three methods: 1) a method that migrates a VM to another distant server with different performance, 2) a method that migrates VMs among CPU cores in the server, and 3) a DVFS-based method. In our experiments, our proposed technique improves performance by 15.1% and saves system-wide EDP by 22.9%, on average, compared to a state-of-the-art DVFS-based DTM technique, satisfying thermal constraints.
This paper investigates a mobile edge computing environment for video analysis tasks where edge nodes provide their computation capacities to process the computation intensive tasks submitted by end users. First, we introduce a Cloudlet Assisted Cooperative Task Assignment (CACTA) system that organizes edge nodes that are geographically close to an end user into a cluster to collaboratively work on the user’s tasks. It is challenging for the system to find an optimal strategy that assigns workload to edge nodes to meet the user’s optimization goal. To address the challenge, this paper proposes multiple algorithms for different situations. Firstly, considering the situation that historical data cannot be obtained, a multi-round allocation algorithm based on EMA prediction is proposed, and the experimental results prove the efficiency and necessity of multiple rounds of transmission. To address the second case of obtaining historical data, this paper introduces a prediction-based dynamic task assignment algorithm that assigns workload to edge nodes in each time slot based on the prediction of their capacities/costs and an empirical optimal allocation strategy which is learned from an offline optimal solution from historical data. Experimental results demonstrate that our proposed algorithm achieves significantly higher performance than several other algorithms, and especially its performance is very close to that of an offline optimal solution. Finally, we propose an online task assignment algorithm based on Q-learning, which uses the model-free Q-learning algorithm to actively learn the allocation strategy of the system, and the experimental results verify the superiority and effectiveness of this algorithm.
If last decade viewed computational services as a utilitythen surely this decade has transformed computation into a commodity. Computation is now progressively integrated into the physical networks in a seamless way that enables cyber-physical systems (CPS) and the Internet of Things (IoT) meet their latency requirements. Similar to the concept of “platform as a service” or “software as a service”, both cloudlets and fog computing have found their own use cases. Edge devices (that we call end or user devices for disambiguation) play the role of personal computers, dedicated to a user and to a set of correlated applications. In this new scenario, the boundaries between the network node, the sensor, and the actuator are blurring, driven primarily by the computation power of IoT nodes like single board computers and the smartphones. The bigger data generated in this type of networks needs clever, scalable, and possibly decentralized computing solutions that can scale independently as required. Any node can be seen as part of a graph, with the capacity to serve as a computing or network router node, or both. Complex applications can possibly be distributed over this graph or network of nodes to improve the overall performance like the amount of data processed over time. In this paper, we identify this new computing paradigm that we call Social Dispersed Computing, analyzing key themes in it that includes a new outlook on its relation to agent based applications. We architect this new paradigm by providing supportive application examples that include next generation electrical energy distribution networks, next generation mobility services for transportation, and applications for distributed analysis and identification of non-recurring traffic congestion in cities. The paper analyzes the existing computing paradigms (e.g., cloud, fog, edge, mobile edge, social, etc.), solving the ambiguity of their definitions; and analyzes and discusses the relevant foundational software technologies, the remaining challenges, and research opportunities.
Service acceptability rate and user satisfaction are becoming key factors to avoid client churn and secure the success of any Software as a Service (SaaS) provider. Nevertheless, the provider must also accommodate fluctuating workloads and minimize the cost it pays to rent resources from the cloud. To address these contradicting concerns, most of existing works carry out resource management unilaterally by the provider. Consequently, end-user preferences and her subjective acceptability of the service are mostly ignored. In order to assess user satisfaction and service acceptability recent studies in the domain of Quality of Experience (QoE) recommend providers to use quantiles and percentile to gauge user service acceptability precisely. In this article we propose an elastic, load-spike proof, and adaptive one-to-many negotiation mechanism to improve the service acceptability of an open SaaS provider. Based on quantile estimation of service acceptability rate and a learned model of the user negotiation strategy, this mechanism adjusts the provider negotiation process in order to guarantee the desired service acceptability rate while meeting the budget limits of the provider and accommodating workload fluctuations. The proposed mechanism is implemented and its results are examined and analyzed.
In E-commerce, numbers of transactions are increasing day by day in B2B and B2C trade. Online negotiation is possible because of automated negotiation. Autonomous entities such as agents could help in these situations. Providing an offer which has maximum utilities for both trading parties into possible shortest time is the main aim of this work. Proposed model applies issue trade-offs strategy in which multiple issues are traded-offs against one another to maximize participant satisfaction. To make trade-offs the model applies a fuzzy system approach. The automated negotiation model in this article has a process without offer generating and exchanging between buyer and supplier agents to explore time-consuming negotiation process in earlier researches. Mediator component searches an optimal offer that satisfies buyer and supplier requirements. The system will utilize fuzzy inference systems to automate negotiation process and considers two effective factors in the negotiation process: requirements and preferences. Requirements are qualitative or quantitative values which the participants assign to issues for negotiation. Preferences of the participants are priorities assigned to the issues. These values express an importance measure of the issues from a participant perspective. Analytic hierarchy process (AHP) is used to get preferences of the issues. Proposed model applies different Fuzzy Inference System (FIS) schemes for qualitative and quantitative negotiation issues to enhance the satisfaction level of buyers and suppliers. Experimental results show that how the model fulfills the main aim of our work.
In order to make reservations for cloud services, consumers and providers need to work on Service Level Agreements (SLAs) to achieve negotiation. Since cloud computing represents a category of distributed system, negotiation should be established between service providers and consumers in a faster and reliable manner. Automated Negotiation is such a process that is closely associated with Multi-agent Systems. We propose A Negotiation system using fuzzy ontology is addressed by us in this research work where the cloud service specification and consumer requirements with vagueness are stored on cloud ontology. Here, each party needs to choose its own agent in order for negotiation. Agents have both consumer’s and provider’s details and their hard and soft preferences for a particular service. Agents need to negotiate on the basis of a set of Quality of Service (QoS) parameters like duration, availability, price etc. as required by the user. On completion of the negotiation process, user receives a feedback from the agent regarding the probability of negotiation. This negotiation framework is dynamic and efficient in nature.
In cloud e-commerce application, building an automated negotiation strategy by understanding the uncertain information of the opponent preferences, utilities, and tactics is highly challenging. The key issue is to analyse and predict the uncertain behaviour of the opponent tactics to suggest the appropriate counter tactics that can reach maximum consensus. To handle such uncertain information, negotiation strategies follow several tactics with and without learning ability. Strategies without learning ability are restricted to negotiate with the opponent having only deterministic behaviour. To overcome this problem most researchers exploited the negotiation strategies with fixed learning ability using Bayesian learning, neural network learning, and genetic tactics. These tactics can learn the opponent’s behaviour and cannot guarantee to generate suitable counter-offer for all offers submitted by the opponent cloud service provider. This limitation motivates to propose a novel Adaptive Probabilistic Behavioural Learning System for managing the opponent having unpredictable random behaviours. The proposed Adaptive Probabilistic Behavioural Learning System contains a Behavioural Inference Engine to analyse the sequence of negotiation offer received by the broker for effectively learning the opponent’s behaviour over several stages of negotiation process. It also formulates the multi-stage Markov decision problem to suggest the broker with appropriate counter-offer behavioural tactics generation based on the adaptive probabilistic decision taken over the corresponding negotiation stage. Therefore, this research work can outperform the existing fixed behavioural learning tactics and hence maximize the utility value and success rate of negotiating parties without any break-off.
One of the major issues in emerging cloud management system needs the efficient service level agreement negotiation framework, with an optimal negotiation strategy. Most researchers focus mainly on the atomic service negotiation model, with the assistance of the Agent Controller in the broker part to reduce the total negotiation time, and communication overhead to some extent. This research focuses mainly on composite service negotiation, to further minimize both the total negotiation time and communication overhead through the pre-request optimization of broker strategy. The main objective of this research work is to introduce an Automated Dynamic Service Level Agreement Negotiation Framework (ADSLANF), which consists of an Intelligent Third-party Broker for composite service negotiation between the consumer and the service provider. A broker consists of an Intelligent Third-party Broker Agent, Agent Controller and Additional Agent Controller for managing and controlling its negotiation strategy. The Intelligent third-party broker agent manages the composite service by assigning its atomic services to multiple Agent Controllers. Using the Additional Agent Controllers, the Agent Controllers manage the concurrent negotiation with multiple service providers. In this process, the total negotiation time value is reduced partially. Further, the negotiation strategy is optimized in two stages, viz., Classified Similarity Matching (CSM) approach, and the Truncated Negotiation Group Gale Shapely Stable Matching (TNGGSSM) approach, to minimize the communication overhead.
This paper describes a new agenda based approach which facilitates multi-issue negotiation process between service consumer and service provider over the quality of service (QoS) requirements in cloud services. We assume that the order of agenda (issues) has impact on the negotiation outcome, but a group of service consumers and providers may have conflicting preferences over the importance of issues. In a multiple-issue negotiation process it is difficult to reach an optimal outcome when their preferences and the relative importance of their associated issues are not known to each other. The proposed agenda based preference ordering approach helps consumers and providers to reach a consensus over the issues and to construct a common preference sequence to improve the efficiency of the issue-by-issue negotiation outcome. Consequently, a co-evolutionary negotiation model based on the result of preference ordering approach is introduced for the agents to negotiate and reach an agreement, if there is any. The contribution of the research is a new mechanism that is able to formulate issue sequence along with a co-evolutionary negotiation approach that can effectively facilitate negotiation over QoS issues in cloud computing. Finally, a case study is provided to illustrate the proposed approach.
It is difficult to select a composite service with the lowest actual executing cost using the existing methods for QoS-aware service composition in cloud computing. By analyzing the dynamic execution process of composite service with state transition matrix, this paper proposes a new QoS aware optimal service composition method. In view of the effect of composite services reliability on the composite service performance, the method regards the cost averaged for one time of successful execution of a composite as its actual executing cost, and then selects the composite services with the aim of minimizing the composite service execution cost. The simulation result shows that the proposed method is superior to other methods in execution cost.
Since participants in a Cloud may be independent bodies, some mechanisms are necessary for resolving the different preferences to establish a service-level agreement (SLA) for Cloud service reservations. Whereas there are some mechanisms for supporting SLA negotiation, there is little or no negotiation support involving price, time slot, and QoS issues concurrently for a Cloud service reservation. For the concurrent price, timeslot, and QoS negotiation, a tradeoff algorithm to generate and evaluate a proposal which consists of price, timeslot, and QoS proposals is necessary. The contribution of this work is designing a multi-issue negotiation mechanism to facilitate 1) concurrent price, time slot, and QoS negotiations including the design of QoS utility functions and 2) adaptive and similarity-based trade-off proposals for price, time slots, and level of QoS issues. The tradeoff algorithm referred to as “adaptive burst mode” is especially designed to increase negotiation speed, total utility, and to reduce computational load for evaluating proposals by adaptively generating concurrent set of proposals. The empirical results obtained from simulations carried out using an agent-based testbed suggest that using the negotiation mechanism, (i) a consumer and a provider agent have a mutually satisfying agreement on price, time slot, and QoS issues in terms of the aggregated utility and (ii) the fastest negotiation speed with (iii) comparatively lower number of evaluated proposals in a negotiation.
Virtualized Service models are now emerging and redefining the way information technology is delivered to end users. Current research is focused on spe-cific pieces like service discovery, composition etc. There is no holistic view of what would constitute a lifecycle of virtualized services delivered on a cloud environment. In this paper, we propose an integrated methodology that covers the entire service lifecycle. We have divided the IT service lifecycle on the cloud into five phases of requirements, discovery, negotiation, composition, and consumption. We describe each phase and it's sub-phases in detail along with the information that needs to flow between them. We also propose some key metrics that should be tracked for each phase.
Automated negotiation is a key form of interaction in systems that are composed of multiple autonomous agents. The aim of such interactions is to reach agreements through an iterative process of making offers. The content of such proposals are, however, a function of the strategy of the agents. Here we present a strategy called the trade-off strategy where multiple negotiation decision variables are traded-off against one another (e.g., paying a higher price in order to obtain an earlier delivery date or waiting longer in order to obtain a higher quality service). Such a strategy is commonly known to increase the social welfare of agents. Yet, to date, most computational work in this area has ignored the issue of trade-offs, instead aiming to increase social welfare through mechanism design. The aim of this paper is to develop a heuristic computational model of the trade-off strategy and show that it can lead to an increased social welfare of the system. A novel linear algorithm is presented that enables software agents to make trade-offs for multi-dimensional goods for the problem of distributed resource allocation. Our algorithm is motivated by a number of real-world negotiation applications that we have developed and can operate in the presence of varying degrees of uncertainty. Moreover, we show that on average the total time used by the algorithm is linearly proportional to the number of negotiation issues under consideration. This formal analysis is complemented by an empirical evaluation that highlights the operational effectiveness of the algorithm in a range of negotiation scenarios. The algorithm itself operates by using the notion of fuzzy similarity to approximate the preference structure of the other negotiator and then uses a hill-climbing technique to explore the space of possible trade-offs for the one that is most likely to be acceptable.
When making reservations for Cloud services, consumers and providers need to establish service-level agreements through negotiation. Whereas it is essential for both a consumer and a provider to reach an agreement on the price of a service and when to use the service, to date, there is little or no negotiation support for both price and time-slot negotiations (PTNs) for Cloud service reservations. This paper presents a multi-issue negotiation mechanism to facilitate the following: 1) PTNs between Cloud agents and 2) tradeoff between price and time-slot utilities. Unlike many existing negotiation mechanisms in which a negotiation agent can only make one proposal at a time, agents in this work are designed to concurrently make multiple proposals in a negotiation round that generate the same aggregated utility, differing only in terms of individual price and time-slot utilities. Another novelty of this work is formulating a novel time-slot utility function that characterizes preferences for different time slots. These ideas are implemented in an agent-based Cloud testbed. Using the testbed, experiments were carried out to compare this work with related approaches. Empirical results show that PTN agents reach faster agreements and achieve higher utilities than other related approaches. A case study was carried out to demonstrate the application of the PTN mechanism for pricing Cloud resources.
Bolstering resource coallocation is essential for realizing the Grid vision, because computationally intensive applications often require multiple computing resources from different administrative domains. Given that resource providers and consumers may have different requirements, successfully obtaining commitments through concurrent negotiations with multiple resource providers to simultaneously access several resources is a very challenging task for consumers. The impetus of this paper is that it is one of the earliest works that consider a concurrent negotiation mechanism for Grid resource coallocation. The concurrent negotiation mechanism is designed for 1) managing (de)commitment of contracts through one-to-many negotiations and 2) coordination of multiple concurrent one-to-many negotiations between a consumer and multiple resource providers. The novel contributions of this paper are devising 1) a utility-oriented coordination (UOC) strategy, 2) three classes of commitment management strategies (CMSs) for concurrent negotiation, and 3) the negotiation protocols of consumers and providers. Implementing these ideas in a testbed, three series of experiments were carried out in a variety of settings to compare the following: 1) the CMSs in this paper with the work of others in a single one-to-many negotiation environment for one resource where decommitment is allowed for both provider and consumer agents; 2) the performance of the three classes of CMSs in different resource market types; and 3) the UOC strategy with the work of others [e.g., the patient coordination strategy (PCS )] for coordinating multiple concurrent negotiations. Empirical results show the following: 1) the UOC strategy achieved higher utility, faster negotiation speed, and higher success rates than PCS for different resource market types; and 2) the CMS in this paper achieved higher final utility than the CMS in other works. Additionally, the properties of the three classes of CMSs in different kinds of resource markets are also verified.
While evaluation of many e-negotiation agents are carried out through empirical studies, this work supplements and complements existing literature by analyzing the problem of designing market-driven agents (MDAs) in terms of equilibrium points and stable strategies. MDAs are negotiation agents designed to make prudent compromises taking into account factors such as time preference, outside option, and rivalry. This work shows that 1) in a given market situation, an MDA negotiates optimally because it makes minimally sufficient concession, and 2) by modeling negotiation of MDAs as a game gamma of incomplete information, it is shown that the strategies adopted by MDAs are stable. In a bilateral negotiation, it is proven that the strategy pair of two MDAs forms a sequential equilibrium for gamma. In a multilateral negotiation, it is shown that the strategy profile of MDAs forms a market equilibrium for gamma.
Market-driven agents are negotiation agents that react to changing market situations by making adjustable rates of concession. This paper presents 1) the foundations for designing market-driven strategies of agents, 2) a testbed of market-driven agents, 3) experimental results in simulating the market-driven approach, and 4) theoretical analyses of agents' performance in extremely large markets. In determining the amount of concession for each trading cycle, market-driven agents in this research are guided by four mathematical functions of eagerness, remaining trading time, trading opportunity , and competition. At different stages of trading, agents may adopt different trading strategies, and make different rates of concession. Four classes of strategies with respect to remaining trading time are discussed. Trading opportunity is determined by considering: 1) number of trading partners, 2) spreads-differences in utilities between an agent and its trading partners, and 3) probability of completing a deal. While eagerness represents an agent's desire to trade, trading competition is determined by the probability that it is not considered as the most preferred trader by its trading partners. Experimental results and theoretical analyses showed that agents guided by market-driven strategies 1) react to changing market situations by making prudent and appropriate rates of concession, and 2) achieve trading outcomes that correspond to intuitions in real-life trading.
The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online business-to-business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different quality of service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming.
Negotiation is used when two or more parties with conflicting goals pursue their own interests. Service trading in cloud computing is a case in which automated negotiation system (ANS) can be used to achieve high satisfaction levels for both contract parties. An ANS allows the parties negotiate automatically on some parameters that are important for them. Multiple services are combined together to provide a composite service that delivers a value-added service to consumers. With the increasing tendency to use composite cloud services, there is a need for designing ANSs capable of fulfilling requirements of consumers of these services. Unlike existing ANSs which support negotiation of single service, our proposed ANS supports negotiation of composite service. This work also proposes a novel negotiation strategy to enhance satisfaction of both trading parties. Some simulations were carried out to evaluate the performance of proposed negotiation strategy in terms of negotiating agents’ utility and negotiation speed. Analysis of the results of simulations shows that our recommended strategy provides more performance than the others.
Today, service composition is emerging paradigm on the communication networks such as cloud environments, internet of things, wireless sensor network, and software‐defined network. The goal of service composition method is to provide the interactions between user requirements and smart objects of intelligent communication systems. There have been many efforts to use formal verification and behavioral modeling methods to evaluate the service composition mechanisms. Up to now, there is not a comprehensive analysis research on this topic. Therefore, this paper focuses on several formal verification approaches that are performed to confirm the service composition correctness in communication networks. The objective of this paper is to comprehensively categorize and examine current research techniques on formal verification of the service composition. This research analysis provides an overview of recent service composition approaches according to structural and functional properties. Comparison results show that most of the verification approaches in explanation of the service composition correctness are semantic‐aware approach with 43%. The most used verification method for the service composition is model checking with 69%. The process algebra is used 29%, and some theorem proving methods are applied in 9% of the investigated mechanism. Moreover, most widely used modeling tools are NuSMV (22%), SPIN (17%), CPN (12%), UPPAAL (12%), Event‐B (10%), and PAT (5%).
The main problem with the current technology solutions for Internet of Things (IoT) is located at the application level, in which there is still a lack of software components, frameworks, and tools which can help end-users to easily develop IoT applications and to manage the things or objects. These requirements lead to the use of the principles of Model-Driven Development (MDD) and Service-Oriented Architecture (SOA) for the construction of software applications, allowing the generation of models at different levels of abstraction, with the possibility of generating software implementation artifacts (code) on different platforms, as well as allow the decomposition of complex and monolithic systems into loosely coupled components. In this paper, we propose a methodology based on the MDD for solving the challenges in the IoT system developments. The methodology is composed of four phases with different levels of abstraction, viewpoint, granularity, and service-oriented. Furthermore, a set of MDD methods for the transformation of models using formal rules are proposed. We present a smart vehicle scenario in which the methodology and methods are implemented, allowing the generation of executable code as a component of a real-time system. In addition, an architecture for IoT systems composed of four layers is presented, which is based on the SOA approach. In this way, the methodology and the architecture allow the interoperability (from the perspective of models and software components) between heterogeneous devices to be guaranteed in multiple ways, establishing a bridge between the digital and physical world of the IoT domain.
Features of cloud computing services have created a significant trend of organizations opting for these services. In this situation, many consumers, who want a certain service, and many providers, who supply those services, form a competitive market. Consumers compete with each other to get a service with the desired functional and nonfunctional requirements. There is also a competition space among the providers to sell their services to the consumers. Since there is a dynamic competitive market and conflicting requirements of consumers and providers, it is essential that automated negotiation mechanisms be proposed to reach agreements and provide the maximum utility for both trading parties. To the best of our knowledge, no comprehensive study and review of cloud computing service negotiation (CCSN) exist as of now. In this article, we study the CCSN models. We investigate frameworks, techniques, protocols, and strategies proposed by the CCSN researches. Some questions will be proposed, and answers to the questions may help researchers design more efficient models in future works. Valuable innovations are explained and evaluated to find the answers to the questions. Statistics can specify some defects that must be addressed in future researches. The contributions can be summarized as follows: (1) reviewing CCSN works to understand open issues and challenges in providing negotiation systems in the cloud environments, (2) identifying the characteristics of an efficient CCSN that can provide the maximum utility for the trading parties, and (3) pointing out the defects of the current works to help researchers improve the CCSN systems in future researches.
Typically, cloud computing includes the provisioning of dynamically scalable and virtualized resources as services over the Internet. In the cloud environment, based on the user's needs, various types of services can be delivered that often must be composited to meet the user requests. Therefore, service composition is emerging as a universal technology in order to integrate distributed and heterogeneous services to combine and consolidate the cloud services. This idea focuses on the innovation of a new cloud service including previously existing services for cost and time reducing and efficiency improving. However, to the best of our knowledge, despite the importance of this matter in cloud environments, there doesn’t exist any comprehensive and systematic research and survey in this field. Therefore, the purpose of this paper is to survey the existing techniques and mechanisms which can be addressed in this domain. Briefly, the contributions of this paper are: (1) providing an overview of the existing challenges in a range of problem domains associated with cloud service composition, (2) providing an anatomy of some important techniques throughout scope of cloud service composition and (3) outlining key areas for the improvement of service composition methods in the future research.
Utilizing cloud-based services, consumers gain a high level of flexibility, but they cannot obtain individual Quality of Service guarantees or request service compositions according to their specific business needs. Therefore, appropriate mechanisms for an automated negotiation of Quality of Service parameters are required that do not only consider the individual business objectives and strategies of the negotiation partners involved, but do also account for the dependencies between the different services and service tiers in cloud computing. This enables enterprises to increase the quality and flexibility of their business processes and lays the foundation for market-based complex service provisioning. In this paper, we present one such negotiation approach and evaluate the application of different negotiation strategies.
Deploying an application to a cloud environment has recently become very trendy, since it offers many advantages such as improving reliability or scalability. These cloud environments provide a wide range of resources at different levels of functionality, which must be appropriately configured by stakeholders for the application to run properly. Handling this variability during the configuration and deployment stages is a complex and error-prone process, usually made in an ad hoc manner in existing solutions. In this paper, we propose a software product lines based approach to face these issues. Combined with a domain model used to select among cloud environments a suitable one, our approach supports stakeholders while configuring the selected cloud environment in a consistent way, and automates the deployment of such configurations through the generation of executable deployment scripts. To evaluate the soundness of the proposed approach, we conduct an experiment involving 10 participants with different levels of experience in cloud configuration and deployment. The experiment shows that using our approach significantly reduces time and most importantly, provides a reliable way to find a correct and suitable cloud configuration. Moreover, our empirical evaluation shows that our approach is effective and scalable to properly deal with a significant number of cloud environments.
Dynamic customers’ requirements and providers’ resources availability in the Cloud market make it inadequate static approaches to guarantee Quality of Service (QoS) levels and to define pricing. In this context, negotiation guided by dynamic information is a viable way to achieve high satisfaction levels for both contract parties. We propose to exploit capacity planning to support bilateral negotiation processes with the aim of optimizing the utility for service providers, by avoiding contracts that could incur in Service Level Agreements (SLAs) violations, keeping, at the same time, competitive prices. The proposed technique exploits a non-additive utility function defined in the region of acceptable SLA proposals, taking into account desired QoS and expected resources availability, costs and penalties. The experimental analysis shows the benefit of the proposed dynamic approach with respect to static ones in a scenario characterized by a set of customers and differentiated classes of applications provided by a cloud environment.
Internet of Things (IoT) allows connected objects to communicate via the Internet. IoT can benefit from the unlimited capabilities and resources of cloud computing. Also, when coupled with IoT, cloud computing can in turn deal with real world things in a more distributed and dynamic manner. As the cloud market becomes more open and competitive, Quality of Service (QoS) will be more important. However, cloud providers and cloud consumers have different, and sometimes opposite, preferences. If such a conflict occurs, a Service Level Agreement (SLA) cannot be reached without negotiation. A tradeoff negotiation approach can outperform a concession approach in terms of utility, but may incur more failures if information is incomplete. To balance utility and success rate, we propose a mixed approach for cloud service negotiation, which is based on the “game of chicken.” In particular, if one is uncertain about the strategy of its counterpart, it is best to mix concession and tradeoff strategies in negotiation. To evaluate the effectiveness of this approach, we conduct extensive simulations. Results show that a mixed negotiation approach can achieve a higher utility than a concession approach, while incurring fewer failures than a tradeoff approach.
During recent years cloud service providers have successfully provided reliable and flexible resources to cloud users. For example Amazon Elastic Block Store (Amazon EBS) and Simple Storage Service (Amazon S3) provides users storage in the cloud. Despite the tremendous efforts cloud service providers have devoted to the availability of their services, the interruption is still inevitable. Therefore just as an Internet service provider will not count on a single network provider, a cloud user should not depend on a single cloud service provider either. However, cloud service providers provide different levels of services. A more costly service is usually more reliable. As a result it is an important and challenging problem to choose among a set of service providers to fit one's need, which could be budget, failure probability, or the amount of data that can survive failure. The goal of this paper is to select cloud service providers in order to maximize the benefits with a given budget. The contributions of this paper include a mathematical formulation of the cloud service provider selection problem in which both the object functions and cost measurements are clearly defined, algorithms that selects among cloud storage providers to maximize the data survival probability or the amount of surviving data, subject to a fixed budget, and a series of experiments that demonstrate that the proposed algorithms are efficient enough to find optimal solutions in reasonable amount of time, using price and fail probability taken from real cloud providers.
The expanding Cloud computing services offer great opportunities for consumers to find the best service and best pricing, which however raises new challenges on how to select the best service out of the huge pool. It is time-consuming for consumers to collect the necessary information and analyze all service providers to make the decision. This is also a highly demanding task from a computational perspective, because the same computations may be conducted repeatedly by multiple consumers who have similar requirements. Therefore, in this paper, we propose a novel brokerage-based architecture in the Cloud, where the Cloud brokers is responsible for the service selection. In particular, we design a unique indexing technique for managing the information of a large number of Cloud service providers. We then develop efficient service selection algorithms that rank potential service providers and aggregate them if necessary. We prove the efficiency and effectiveness of our approach through an experimental study with the real and synthetic Cloud data.
In recent years, cloud computing becomes a new distributed computing approach, which improves efficiency of Internet computing. Therefore, a semantics Web service composition approach based on cloud computing is proposed. Service composition is analyzed by Bayesian decision in the cloud computing, which is built by a Bayesian decision of oriented-cloud semantics Web service, and a graph of semantics Web service discovery is defined to describe the method. Then according to the above, a service composition approach is established. Finally, A group of login service based on Amazon is employed to analyze the approach indicated that is feasible and effective.
A Service Level Agreement (SLA) is a legal contract between parties to ensure the Quality of Service (QoS) are provided the providers to the customers. A SLA negotiation between participants assists in defining the QoS requirements of critical service-based processes. However, the negotiation process for customers is a significant task particularly when there are multiple SaaS providers in the Cloud market, as service cost and quality are constantly changing and consumers have varying needs. Therefore, we propose a novel automated negotiation framework where a SaaS broker is utilized as the one-stop-shop for customers to achieve the required service efficiently when negotiating with multiple providers. The automated negotiation framework facilitates intelligent bilateral bargaining of SLAs between a SaaS broker and multiple providers to achieve different objectives for different participants. To maximize profit and improve customer satisfaction levels for the broker, we propose the design of counter offer generation strategies and decision making heuristics that take into account time, market constraints and trade-off between QoS parameters. Our negotiation heuristics are evaluated by extensive experimental studies of our framework using data from a real Cloud provider.
In real-life trading, relaxing decisions in the face of trading pressure is common. Similarly, in market-based grid resource allocation problem designing negotiator agents with the flexibility to relax their decision to (quickly) complete a deal in the face of intense Grid Market Pressure (GMP) is essential. To make this idea possible, we design Enhanced Market- and Behavior-driven Negotiation Agents (EMBDNAs) that adopt new fuzzy negotiation protocol. The protocol focuses on both (1) enhancing Rubinstein's sequential alternating offer protocol to handle multiple trading opportunities and market competition and (2) designing two new Fuzzy Grid Market Pressure Determination Systems (FGMPDSs) for both grid resource consumers and grid resource owners to guide negotiator agents in relaxing their bargaining terms under intense GMP to enhance their chance of successfully acquiring/leasing out resources. Implementing the idea in an agent-based testbed, an experiment for evaluating and comparing EMBDNA against EMDA (Enhanced Market-Driven Agent) and our previous work in name MBDNA (Market- and Behavior-driven Negotiation Agent) were carried out through stochastic simulations. While EMDA relaxes its bargaining term in the face of intense GMP by considering just two relaxation factors the MBDNA uses the same negotiation strategy as EMBDNA but does not relax its bargaining term in the face of intense GMP. The results show that adopting the new fuzzy negotiation protocol, EMBDNAs outperform MBDNAs and EMDAs.
Service composition in multi-Cloud environments must coordinate self-interested participants, automate service selection, (re)configure distributed services, and deal with incomplete information about Cloud providers and their services. This work proposes an agent-based approach to compose services in multi-Cloud environments for different types of Cloud services: one-time virtualized services, e.g., processing a rendering job, persistent virtualized services, e.g., infrastructure-as-a-service scenarios, vertical services, e.g., integrating homogenous services, and horizontal services, e.g., integrating heterogeneous services. Agents are endowed with a semi-recursive contract net protocol and service capability tables (information catalogs about Cloud participants) to compose services based on consumer requirements. Empirical results obtained from an agent-based testbed show that agents in this work can: successfully compose services to satisfy service requirements, autonomously select services based on dynamic fees, effectively cope with constantly changing consumers’ service needs that trigger updates, and compose services in multiple Clouds even with incomplete information about Cloud participants.
For Cloud services, their non-functional properties like availability, reliability and security are important differentiators. However, service consumers and service providers may conflict over non-functional properties. In fact, the conflicts can be resolved via automated negotiation, which is considered as the most flexible approach to procure products and services. In this paper, we propose tradeoff approaches for Cloud service negotiation, and compare them with concession ones. As opposed to concession ones, tradeoff approaches do not reduce one's utility, but still can create a proposal attractive to its opponent. Indeed, simulation results show that tradeoff approaches outperform concession ones in terms of both individual utility and social benefit. However, simulation results also demonstrate that tradeoff approaches under perform concession ones in terms of success rate.
The end-to-end QoS negotiation for service level agreement establishment for composite services involves compound multi-party negotiations in which the composite service provider concurrently negotiates with multiple candidates for each atomic service, selecting the one that best satisfies the atomic service QoS preferences while ensuring that the end-to-end QoS requirements are also fulfilled. In order to be able to negotiate with potential candidates, it is necessary to derive the atomic utility boundaries from the global utility boundary. Additionally, there has to be a mechanism for updating these boundaries in subsequent negotiation rounds based on the individual negotiation outcomes. In this paper, we propose an algorithm for the decomposition of global utility boundary into atomic service utility boundaries, and the surplus redistribution from successful negotiation outcomes among the remaining negotiations. The proposed mechanism is a practical approach to efficiently coordinate concurrent service negotiations within complex workflows, enabling the iterative and interactive adjustment of the negotiation boundaries for each atomic service in a composition based on the performance of other atomic negotiations. We demonstrate the feasibility of our approach by evaluating it with some popular negotiation strategies using the Specialized Property Search Scenario. Copyright © 2011 John Wiley & Sons, Ltd.
Agent-based Cloud computing is concerned with the design and development of software agents for bolstering Cloud service discovery, service negotiation and service composition. The significance of this work is introducing an agent-based paradigm for constructing software tools and testbeds for Cloud resource management. Novel contributions of this work include: 1) developing Cloudle: an agent-based search engine for Cloud service discovery, 2) showing that agent-based negotiation mechanisms can be effectively adopted for bolstering Cloud service negotiation and Cloud commerce, and 3) showing that agent-based cooperative problem-solving techniques can be effectively adopted for automating Cloud service composition. Cloudle consists of a service discovery agent that consults a Cloud ontology for determining the similarities between providers' service specifications and consumers' service requirements. To support Cloud commerce, this work devised a complex Cloud negotiation mechanism that supports parallel negotiation activities in interrelated markets. Empirical results show that using such mechanism, agents achieved high utilities and high success rates in negotiating for Cloud resources. To automate Cloud service composition, agents adopt the contract net protocol (CNP) and use acquaintance networks (AN). Empirical results show that using CNP and AN, agents can successfully compose Cloud services by autonomously selecting services.
To enable agents negotiate more efficiently in multilateral multi-issue cooperative negotiations in multi-agent based on e-commerce, a hybrid genetic algorithm (HGA) is presented and applied in the negotiation. After compare of 1000 times of experiments for four kinds of genetic algorithm, the result shows that standard genetic algorithm (SGA) averagely needs negotiation 185 times, genetic algorithm based on Metropolis rule (MGA) averagely needs 176 times, adaptive genetic algorithm (AGA) averagely needs 169 times, while the HGA averagely needs only 153 times. The HGA can gain optimal negotiation result more efficiently than the other three kinds of genetic algorithms in multi-lateral multi-issue cooperation negotiation.
We present a formal model of negotiation between autonomous agents. The purpose of the negotiation is to reach an agreement about the provision of a service by one agent for another. The model de nes a range of strategies and tactics that agents can employ to generate initial o ers, evaluate proposals and o er counter proposals. The model is based on computationally tractable assumptions, demonstrated in the domain of business process management and empirically evaluated. Keywords: Multi-agent systems, Negotiation, Business Process Management 1
Automated negotiation by autonomous agents has become increasingly important since the advent of e-marketplace. In this study, automated negotiation is viewed as a search process in which negotiators jointly search for a mutually acceptable contract in a multidimensional space formed by negotiable issues. This search is formulated as a multiple-objective decision making problem and is solved through an iterative process of generating offers by fuzzy inference systems. These fuzzy inference systems serve as a search heuristic and are formulated based on the strategy of issue trade-offs. Five experiments are conducted to evaluate the performance of the proposed automated negotiation algorithm.
In a Cloud computing environment, the dynamic configuration of a personalized collection of resources often requires Cloud
participants (consumers, brokers, and providers) to establish service-level agreements (SLAs) through negotiation. However, to date, state-of-the-art approaches in Cloud computing provides limited or no support for
dynamic SLAs negotiation. This position paper 1) presents the design of a complex Cloud negotiation mechanism that supports negotiation
activities in interrelated markets: a Cloud service market between consumer agents and broker agents, and multiple Cloud resource
markets between broker agents and provider agents, 2) specifies the negotiation protocols and strategies of consumer and broker
agents in a Cloud service market, and 3) presents the design of the contracting and coordination algorithms for the concurrent
negotiation activities between broker and provider agents in multiple Cloud resource markets. The complex Cloud negotiation
mechanism is designed to support complex negotiation activities in interrelated markets in which the negotiation outcomes
between broker and provider agents in a Cloud resource market can potentially influence the negotiation outcomes of broker
and consumer agents in a Cloud service market. To the best of the author’s knowledge, this work is the earliest proposal for
a complex Cloud negotiation mechanism.
With the advent of Cloud computing, there is a high potential for third-party solution providers such as composite service providers, aggregators or resellers to tie together services from different clouds to fulfill the pay-per-use demands of their customers. Customer satisfaction which is primarily based on the fulfillment of user-centric objectives is a crucial success factor to excel in such a service market. The clients’ requirements, if they change over time even after the desired solution composition, may result in a failure of this approach. On the other hand, business prospects expand with the possibility of reselling already designed solutions to different customers after the underlying services become available again. The service composition strategies must cope with the above-mentioned dynamic situations.
In this paper we address these challenges in context with the customer-driven service selection. We present a formal approach to map customer requirements onto functional and non-functional attributes of the services. We define a happiness measure to guarantee user satisfaction and devise a parallelizable service composition algorithm to maximize this happiness measure. We devise a heuristic approach based on historical information of service composition to rapidly react to changes in client requirements at design time and indicate run-time remedies such as for service failures. The heuristic algorithm is also useful to recompose similar solutions for different clients with matching requirements. Our algorithms are evaluated by the results of a simulation developed on the workflow tool Kepler coupled with a C++ implementation of the optimization algorithms.
Efficient management of service level agreements which specify mutually-agreed understandings and expectations of service provision has been a subject of research for a few years. A critical issue in this area is for service consumers and service providers to effectively achieve agreements on non-functional aspects of service provision, such as quality of service. However, this issue has not been well addressed, especially in the context of service composition provision which implies the establishment of a set of interrelated agreements on quality of service between the service consumer and multiple service providers offering various services in the composition. There is a lack of supporting frameworks and techniques to automatically and dynamically achieve agreements on quality of service constraints for individual services in a service composition, aiming at fulfilling composition’s end-to-end quality of service requirements.This paper reports the authors’ recent research in addressing this issue, using the agent technology. In this research, the service level agreements for a service composition are established through autonomous agent negotiation. To enable this, an innovative framework is proposed in which the service consumer is represented by a set of agents who negotiate quality of service constraints with the service providers for various services in the composition. This negotiation is well coordinated in order to achieve end-to-end quality of service requirements. Based on this framework, a new negotiation protocol is presented to support coordinated negotiation. A utility-function-based decision-making model is proposed based on which agents can proactively decide on the course of further actions. Moreover, this paper also contributes the novel design of the negotiation Web service on the service providers’ side for the purpose of interoperability. Finally, the prototype implementation for the purpose of proof-of-concept is discussed.