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![Main aspetcs of Clouds (from [3]).](profile/Domenico-Talia/publication/220866350/figure/fig1/AS:926182090481668@1597830426453/Main-aspetcs-of-Clouds-from-3.png)
![Figure 1. Main aspetcs of Clouds (from [3]).](profile/Domenico-Talia/publication/220866350/figure/fig1/AS:926182090481668@1597830426453/Main-aspetcs-of-Clouds-from-3_Q320.jpg)
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... key characteristics of Clouds are: On-demand selfservice, ubiquitous network access, location independent resource pooling, rapid elasticity, and pay per use. Fig. 1 summarizes the main aspects of Cloud computing system both from the technical side and the business side [3]. The delivery models of Clouds are very important because they define three different types of Cloud computing ...
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Citations
... Cloud computing services and Multi-agent integration can solve many problems in a real-time environment (RTE). Major areas of research in cloud computing are virtualization, energy efficiency, data storage, and the efficient use of the computing infrastructure [8]. Multi-agent systems can perform more efficiently by utilizing cloud computing power and storage capacity. ...
... In [7], cloud computing-related issues and challenges are given. In [8], cloud computing architecture and its modules were discussed. It described the use of cloud computing in applications with parallel and distributed environments. ...
... illustrates how an intelligent agent works. The smart agents are organized to function autonomously in a changeable environment [13]. ...
... Multi-agent systems are a set of autonomous agents interacting in a common environment in which, they cooperate to accomplish a given task. By the joint use of cloud computing and multi-agent systems, many common problems can be distinguished and lot of qualities can be determined [22]. According to [23], the number of studies that can be found on the state of the art relating Cloud Computing with agent technology is actually quite low. ...
Several Cloud services may be composed in order to respond quickly to the needs of users. Unfortunately, when running such a service some faults may occur. The outcome of fault control is a big challenge. In this paper, the authors propose a new approach based back recovery and multi-agent planning methods. The proposed architecture based MAS (Multi-Agent System) is composed of two main types of Agents : a Composition Manager Agent (CMA) and a Supervisor Agent (SA). The role of the CMA is to create a set of plans as an oriented graph where the nodes are the Cloud services and the valued arcs represent the composition order of these services. This agent saves checkpoints (nodes) in a stable memory so that there are at least one possible path. However, the SA ensures that the running plan is working properly; otherwise, it informs the CMA to select another sub-plan. Experimental results show the performance and effectiveness of the proposed approach.
... As the quantity of sent multi-agent applications increments, further and better involvement in the innovation is acquired, empowering a solid assessment of the field from a more reasonable viewpoint. Specifically, questions identifying with how the hypothesis of multi-agent frameworks impacts on training, and how the down to earth improvement itself contrasts and different advances, can be replied in light of an uplifted degree of development [28]. Given the strains among theoreticians and professionals in registering all in all, not to mention their altercations in AI or multi-agent frameworks specifically, the conversation on specialist frameworks and applications was both fiery and eager. ...
Cloud computing that follows service-oriented architecture is useful for intelligent agent or multi-agent system (MAS) communication. Their use in representation and construction, parallel, and published applications is identified here and shows similarities, contrasts, and potential combinations between cloud computing and multi-agent structures. Long execution complex structure with clever applications works with MAS to showcase cloud computing. The assembling of interfaces within MAS that requires reliable scattering systems and cloud computing systems that require programs with clever, enthusiastic, versatile, and independent behavior can be current systems and applications. The engineering of a system consisting of MAS that primarily focuses on the materials of cost transactions between cloud users and providers is planned to mitigate the disadvantages of both cloud clients and cloud providers and exploit the full potential of cloud computing. As it turns out, as innovation develops and solves increasingly complex applications, the need for an integrated framework of multiple operators communicating in peer-to-peer mode is becoming clear. Central to the design and operation of such MAS is the focus of a problem and research question that has long been tested by all communities. Arrange it like a cloud environment.KeywordsAgentCloud computingConsensusMulti-agent system (MAS)
... AI can compute the level of heteroscedastic uncertainty [165,172], and uncertainty exists with outputs of NNs. Several works apply AI to improve the performance of fog and cloud systems [173][174][175][176]. ...
The rapid growth of the cloud industry has increased challenges in the proper governance of the cloud infrastructure. Many intelligent systems have been developing, considering uncertainties in the cloud. Intelligent approaches with the consideration of uncertainties bring optimal management with higher profitability. Uncertainties of different levels and different types exist in various domains of cloud computing. This survey aims to discuss all types of uncertainties and their effect on different components of cloud computing. The article first presents the concept of uncertainty and its quantification. A vast number of uncertain events influence the cloud, as it is connected with the entire world through the internet. Five major uncertain parameters are identified, which are directly affected by numerous uncertain events and affect the performance of the cloud. Notable events affecting major uncertain parameters are also described. Besides, we present notable uncertainty-aware research works in cloud computing. A hype curve on uncertainty-aware approaches in the cloud is also presented to visualize current conditions and future possibilities. We expect the inauguration of numerous uncertainty-aware intelligent systems in cloud management over time. This article may provide a deeper understanding of managing cloud resources with uncertainties efficiently to future cloud researchers.
... Intelligent agents are incorporated to operate autonomously in a changing environment. The intelligent agent also has the following main features: [10]. ...
A grid is a system that can manage and organize services and resources that spread amongst different control domains, employ interfaces and protocols, and offer a high quality of services. The integration of Multi-Agent Systems (MAS) with a grid environment significantly affects grid performance. MAS is considered a suitable solution for open systems that modify frequently. Grid offers a wide range of resources for its users, and some of these resources might not be used or utilized for some time before any new jobs come to the grid for processing. Usually, these resources are accumulated in massive data centers to cater to the grid users’ growing demand. This accumulated will lead to consuming a considerable amount of electricity for their operation. The ever-increasing usage of grid computing has led to an increase in electrical energy usage by massive servers in their data centers. In this paper, we have proposed an automated system composed of modern agents that can be used the power wastage resulted from inactive servers inside the data centers for a specific amount of time. The proposed technique depends on switching inactive virtual and physical machines to lower power positions (Sleep/Wakeup or switched off) while still preserving customers’ performance requirements. The Automated system has been tested and evaluated using the Jade tools. The results show that the newly proposed method can reduce power wastage for inactive grid resources.
... There have been several works that aim at leveraging AI techniques to improve the performance of fog and cloud systems [144][145] [147][148] [149]. Different works focus on optimum scheduling policies for cloud, virtualization algorithms, distribution systems among others. ...
Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies’ interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesized to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing.
... Intelligent agents are integrated to run independently in changing environment. The following are Some of the main features of intelligent agents: [11]. ...
... Despite the fact that there are various contrasts between cloud computing and multi-agent systems, both are two distributed computing frameworks, in this manner a few common issues can be distinguished and a lot more qualities can be determined by the joined utilization of cloud computing and multi-agents systems [26]. There have been a number of attempts in using agent technology in solving problems that exist in cloud computing. ...
... Ontology relationships were used by Talia [26] for the development of a multi agent based system in finding the equivalence between queries from the user and resources available from different service providers. The system utilizes agent approach due to their being autonomous and highly mobile for effective communication in a distributed environment. ...
... A concept based search using similarity reasoning from the ontology relationships of the concepts in the cloud ontology was adopted by the search engine. The average waiting time for a search was optimized as a result of multiple agents employed [26]. The possibility of finding a perfect match was enhanced due to the use of ontology. ...
Accessing the advantages of cloud computing requires that a prospective user has proper access to trustworthy cloud services. It is a strenuous and laborious task to find resources and services in a heterogeneous network such as cloud environment. The cloud comput-ing paradigm being a form of distributed system with a complex collection of computing resources from different domains with different regulatory policies but having a lot of values could enhance the mode of computing. However, a monolithic approach to cloud service discovery cannot help the necessities of cloud environment efficiently. This study put forward a distributive approach for finding sincere cloud services with the use of Multi-Agents System for ensuring intelligent cloud service discovery from trusted pro-viders. Experiments were carried out in the study using CloudAnalyst and the results indicated that extending the frontiers MAS approach into cloud service discovery by way of integrating trust into the process improves the quality of service in respect of response time and scalability. A further comparative analysis of the Multi-Agents System approach for cloud service discovery to monolithic approach showed that Multi-Agents System approach is highly efficient, and highly flexible for trustworthy cloud service discovery.
... Numerous methods and techniques for CSC have been proposed in the literature [3][4][5]. The use of multi-agent technology is becoming one of the most important topics in distributed and autonomous decentralised environments such as cloud computing and attempts to design and develop multi-agent systems for supporting cloud service discovery, service negotiation and service composition are increasing in number [12,20,21,137]. The intensifying competition on offering computing services over clouds is multiplying the number of similar-function services with distinct QoS attributes from different providers. The need for atomic services that work with each other successfully is essential because of the existence of such complex, varied and similarly functioning services [7]. ...
Context:
In recent years, various complex and similar-function cloud services with different quality of service features are offered in services pool. Such diversity of services complicates the efficient selection of atomic services and the automatic provision of composited ones.
Objective:
We aim to shed light on the significance of agent-based decision-making methods for boosting cloud services' composition and modelling such complex systems using petri net (PN) techniques.
Method:
In this paper, we report a survey of relevant and related literature focusing on agent-based petri net decision-making modelling for cloud service composition (CSC). We analyse the surveyed works and explain their strengths and weaknesses.
Results:
We found how these technologies and techniques can be well-suited for CSC requirements in overcoming the most important documented challenges. Aside from agent technologies for boosting CSC, multi-criteria decision-making (MCDM) methods are suitable for ensuring service selection for composition operatively because they match end-user requirements with the trade-off criteria that cloud services are characterized with and are well-suited for the agent-based architecture. Furthermore, PNs are appropriate for supporting the modelling of such systems as groups of independent and loosely coupled modules.
Conclusion:
We suggest using a couple of agent-based problem-solving techniques to efficiently composite singular services automatically. This utilization must be supported with an integrated and hybrid MCDM methodology to facilitate the ranking and selection of compromise solutions. The proposed solutions will be impressive if the whole process is systematically and mathematically formulated by PNs. This survey can be used by researchers aiming to join this line of research for further achievement.
