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Cloud Computing offers on-demand access to computational, infrastructure and data resources operated from a remote source. This novel technology has opened new ways of flexible resource provisions for businesses to manage IT applications and data responding to new demands from customers. In this chapter, we provide a general insight to the formatio...
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Citations
... When the IoT became popular, CloudSim was expanded to deliver demonstrating abilities for IoT structure mechanisms. The iFogSim [7] covers CloudSim to model IoT and fog settings through assessing reserve administration approach, latency, network congestion, energy usage, and cost are some of the metrics used. Zeng et al. [8] presented IOTSim, a tool that allows huge data processing to be replicated and used the MapReduce framework as well as a real-life example. ...
The paper can be found at my profile page:
https://research-portal.uws.ac.uk/en/publications/comparative-analysis-of-simulators-for-iot-applications-in-fogclo
... [5] proposed that many service providers bound by standard SLAs come together to form a federation where each service provider serves the client based on the end resources. [6] presented an innovative approach where various cloud providers are connected with each other to deliver a universal decentralized computing system based on open standards where, this union is lead by agreements and constraints in the interconnected web of infrastructure. For achieving QoS under variable workload in Federated cloud computing environment [7] proposed an approach which handles provisioning of application services in a scalable computing resources and network conditions. ...
In Cloud computing deduplication falls under IAAS model as it directly deals with the storage in the datacenters. Deduplication has been implemented at various levels such as file level, block level and chunk level as per the organizational requirements. In the due course of time, many companies have offered for providing better services and subsequently interoperability has braught many Cloud Service Providers (CSP) for scalable provisioning of services under variable workloads, resources and network conditions as hybrid clouds. This concept was very beneficial for customers as well as CSPs and hence it has further evolved and brought many CSPs, either private or public, under one group/alliance and referred it as Federated cloud. Despite of vast storage offered in federated cloud structure, much of it is exhausted with redundant copies of data which needs to be made distinct. In this paper, we have rendered an optimized deduplication strategy for federated cloud environment in which the domain of the storage in not limited to just one CSP but all the CSPs under one federation. It has been observed that the work pertaining to this is not available and in this optimized deduplication strategy we have used file level deduplication technique for the implementation of this approach.
... The concept of federation that exists in cloud computing is entirely different from the cloudlet computing. Currently, most of the literature covers different topics in cloud federation [16][17][18][19]. The detailed architecture of the federation provided by NIST [20,21] is also for cloud, where different cloud providers form a federation for resource sharing and a central broker that belongs to a third party acts as an intermediator. ...
A Cloud computing paradigm augments the limited resources of mobile devices resulting in increased distance, limited Internet bandwidth, and seamless connectivity challenges between a remote cloud and mobile devices. Cloudlet computing based solutions are widely used to address these challenges by bringing the computational facility closer to the user. The ever growing number of mobile devices, Internet of Things (IoT) sensors and Information Communication Technology (ICT) infrastructure used for smart cities demand more resources. The existing cloudlet based solutions are unable to manage the ever-increasing demand for power, storage, and computational resources, and therefore forward the resource extensive tasks to a remote cloud, limiting cloudlet computing benefits. We present the Cloudlet Federation for Resource Optimization (CFRO), a federated cloudlet model for resource optimization to address these resource scarcity challenges. The proposed model exerts the features of scalability, resource collaboration, and robustness. The underlying scheme for resource optimization has been modeled as a Nested Multi Objective Resource Optimization Problem (NMOROP) and a novel algorithm has been proposed to solve it. The detailed analysis and comparative results show that the proposed model offers improved performance and more resource elasticity as compared to the conventional cloudlet model.
... The proposed solution should guarantee the integrity. -The Privacy is necessary to protect the profile and driver's information and the vehicle localization against unauthorized access [8]. The privacy protection mechanisms are embedded in our security solution. ...
p>Vehicular Cloud Networks (VCN) is the network that ensures mobility and availability of resources allowing new services and applications like Network as a Service (NaaS), STorage as a Service (STaaS), Computation as a Service (CompaaS) and Cooperation as a Service (CaaS). In this paper, we propose a solution to secure the Vehicular Cloud Network (VCN). Our challenge in this work is to adapt the PKI architecture, which is mainly used in wired networks to be used in VCN. To propose a security solution for Vehicular Cloud Networks (VCN), our work is based on three steps; the first one is to make network architecture study, where we tried to highlight the main network components. The second step is to propose the security solution architecture. Finally, the last step is to program a test and validate the solution using a simulation.</p
... Cloud Federation refers to the unionization of software, infrastructure and platform services [10]. In federated Cloud one CSP helps to serve the requests of other CSPs [9]. ...
Abstract Cloud federation is an aggregation of services from different providers in a single pool supporting interoperability and resource migration. In federation, Services are assigned to the consumer’s service access pool as per their specific functional and associated Quality level requirements. The said assignment is based on the advertised features of services. Sometimes, the selected provider fails to provide the committed service or, it fails to fulfill the expected QoS level. As a result, the consumer is being deprived of getting the services at required quality levels, in spite of subscribing and paying. Re-federation i.e. the inclusion of new services from different providers in the resource pool is a solution. This costly and time consuming re-federation process harms the overall harmony, reputation and performance of the existing federation. In this paper, the necessary strategies to make a federation autonomic is proposed. It helps federation to work in a self-adaptive manner by delaying the re-federation process through replacement and negotiation mechanisms. This allows the federation to keep a balanced state in case of failures. The proposed methods are simulated and the claims are substantiated by the preliminary experimental outcomes.
... As the next step, cloud bursting techniques were developed to share resources of different clouds, then cloud federations were realized by interoperating formerly separate cloud systems. Overcoming interoperability issues of public cloud providers and various middleware implementations, the process of creating and managing cloud federations is clarified and applied [1]. There are various reasons to optimize resource management in such federations: to serve more users simultaneously, to increase quality of ser-vice, to gain higher profit from resource renting, or to reduce energy consumption or CO 2 emissions. ...
The Internet of Things (IoT) is the latest trend of the current ICT evolution, represented by a huge amount of powerful smart devices that have started to appear on the Internet. By responding to this new trend, many cloud providers have started to offer services for IoT management. Recent advances have already shown that cloud computing can be used to serve IoT needs by performing data generation, processing and visualization tasks. In this currently forming ecosystem, IoT system developers need to purchase, connect and configure these devices, and they also have to choose the right infrastructure provider offering the combination of protocols and data structures fitting their applications. In this paper, we propose a complex, semi-simulation environment that aims to provide a solution for these IoT challenges. Our main contribution is the the design of an Android-based, mobile IoT device simulator called MobIoTSim. We also propose a customizable cloud gateway to manage these devices by receiving, processing and visualizing sensor data coming from MobIoTSim. To be as close as possible to real world application, we created an IoT trace archive service called SUMMON, which can be used to gather real-world sensor data, which can be used by MobIoTSim. Finally, we demonstrate how to create IoT applications utilizing numerous IoT devices with this environment, and evaluate the device management scalability and responsiveness of its components.
... In our earlier works we introduced IoT modeling to a traditionally cloud simulator, then combined provider pricing schemes with IoT cloud management in DISSECT-CF [3] to enable cost-aware investigations. Since cloud federations [4] provide a wider range of capabilities to users, the next step in our research was to enable the usage of multiple cloud datacenters to serve certain IoT scenarios. ...
The Internet of Things (IoT) paradigm is closely coupled with cloud technologies , and the support for managing sensor data is one of the primary concerns of Cloud Computing. IoT-Cloud systems are widely used to manage sensors and different smart devices connected to the cloud, hence a large amount of data is generated by these things that need to be efficiently stored and processed. Simulation platforms have the advantage of enabling the investigation of complex systems without the need of purchasing and installing physical resources. In our previous work, we chose the DISSECT-CF simu-lator to model IoT-Cloud systems, and we also introduced provider pricing models to enable cost-aware policies for experimentation. The aim of this paper is to further extend the simulation capabilities of this tool by enabling multi-cloud resource management. In this paper we introduce four cloud selection strategies aimed to reduce application execution time and utilization costs. We detail our proposed method towards multi-cloud extension, and evaluate the defined strategies through scenarios of a meteorological application .
... In the past decade we experienced an evolution in Cloud Computing: the first clouds appeared in the form of a single virtualized datacenter, then broadened into a larger system of interconnected, multiple datacenters. As the next step, cloud bursting techniques were develop to share resources of different clouds, then cloud federations [20] were realized by interoperating formerly separate cloud systems. There were various reasons to optimize resource management in such federations: to serve more users simultaneously, to increase quality of service, to gain higher profit from resource renting, or to reduce energy consumption or CO2 emissions. ...
... Based on different architectures of Fog applications identified in Section 2, and the laws and policies of data protection applicable in Europe and North American regions, we provide a set of recommendations (extended and revised from [19] for Fog Computing) for organizations who develop, operate, and maintain Fog and cloud systems in order to be compliant to current legislations. ...
... Cloud computing has become a widespread and reliable solution over the past decade. Overcoming interoperability issues of public cloud providers and various middleware implementations, the process of creating and managing cloud federations is clarified and applied [Kertesz, 2014]. There are various reasons to optimize resource management in such federations: to serve more users simultaneously, to increase quality of service, to gain higher profit from resource renting, or to reduce energy consumption or CO2 emissions. ...
... The way inter-clouds are viewed and defined by standard bodies like the European Commission [20,21], the European network for Information Security Agency (ENISA) [22], the NIST [23,24] are related. The Cloud Computing Use Case Discussion Group [25], though adopts NIST models of brokering among multiple cloud providers, yet they do not specify guidelines regarding its implementation [26]. ...
... There has been a number of projects receiving significant funding and attention, [27] provide an extensive survey of these projects with a detailed taxonomical account from architectural and brokering perspectives. Another excellent review on this topic can be found in [26] wherein the authors delve on how standardization organizations view this domain and present the slightly varying nuances and goals of the aforementioned cloud projects. ...
Cloud computing has been the enabling technology for shifting mass scale computation and storage requirements from individually owned clients towards an on-demand and utility styled alternative that provides many services. However, cost of maintaining datacenters, keeping the environmental ramifications of data centers at check, providing affordable computation alternative to users still needs to be addressed in a wholesome manner. One of the most exciting and recent research areas in cloud computing has been cloud federations that can mitigate the aforesaid problems. The past decade has seen immense efforts towards interoperability of clouds leading to realistic cloud federations. Motivated by these advancements and equipped with available technologies, this paper presents a detailed account of a cooperative cloud market. It delineates trading mechanisms of such cloud markets, extent of coordination among market players with illustrative examples. It also presents a novel two-phase coordinated resource reservation and provisioning (CRRP) approach that allocates cloud resources to users to meet the goal of minimizing users’ cost. To that end, this paper proposes a novel Most Cost Effective Providers’ Resources First (MCEPRF) algorithm. The efficiency of the proposed algorithm has been tested using synthetic data and the simulation results presented herein demonstrate the superiority of the proposed approach over its non-coordinated counterparts.
