A broker based approach for cloud provider selection

Achar, Raghavendra; Thilagam, P. Santhi

doi:10.1109/icacci.2014.6968439

Cited by 16 publications

(7 citation statements)

References 20 publications

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“…Achar and Thilagam [9] presented a hybrid approach for ranking IaaS CPs using QoS measurements as selection criteria and using AHP and TOPSIS methods. The IaaS service selection comprises three steps, namely, 1) Identifying which criteria are appropriate to the request, i.e., identifying the essential PIs belonging to the SMI; 2) Assessing these criteria weights using the AHP method, and 3) Ranking each CP using TOPSIS [41].…”

Section: Hybridsmentioning

confidence: 99%

“…From the last decade, the CPS became a significant research challenge and several works have already been developed trying to solve it by using deterministic approaches, for example, Multi-criteria Decision Methods (MCDM) [4][5][6][7][8][9][10][11][12] Other works cope with metaheuristics to address CPS problem [13,14]. Deterministic, metaheuristic and hybrid methods, which combines deterministic ones, are well-used making them important to solve CPS problem according to state-of-the-art literature.…”

Section: Introductionmentioning

confidence: 99%

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Application of deterministic, stochastic, and hybrid methods for cloud provider selection

2022

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Cloud Computing popularization inspired the emergence of many new cloud service providers. The significant number of cloud providers available drives users to complex or even impractical choice of the most suitable one to satisfy his needs without automation. The Cloud Provider Selection (CPS) problem addresses that choice. Hence, this work presents a general approach for solving the CPS problem using as selection criteria performance indicators compliant with the Cloud Service Measurement Initiative Consortium - Service Measurement Index framework (CSMIC-SMI). To accomplish that, deterministic (CPS-Matching and CPS-DEA), stochastic (Evolutionary Algorithms: CPS-GA, CPS-BDE, and CPS-DDE), and hybrid (Matching-GA, Matching-BDE, and Matching-DDE) selection optimization methods are developed and employed. The evaluation uses a synthetic database created from several real cloud provider indicator values in experiments comprising scenarios with different user needs and several cloud providers indicating that the proposed approach is appropriate for solving the cloud provider selection problem, showing promising results for a large-scale application. Particularly, comparing which approach chooses the most appropriate cloud provider the better, the hybrid one presents better results, achieving the best average hit percentage, dealing with simple and multi-cloud user requests.

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Section: Hybridsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of deterministic, stochastic, and hybrid methods for cloud provider selection

2022

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“…Achar and Thilagam [34] proposed a cloud broker architecture with multiple QoS criteria that can be used for selecting a suitable CSP among available ones in an Inter-Cloud environment.…”

Section: With Respect To Cloud Broker Designsmentioning

confidence: 99%

A Semi-Markov decision model-based brokering mechanism for mobile cloud market

Degtiareve¹

2021

Preprint

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As the multitude and complexity of cloud market increases the evaluation and selection of cloud services becomes a burdensome task for the users. With the increased rise of available services from various Cloud Service Providers (CSP), the role of cloud brokers becomes more and more important. In this thesis, the challenge of optimally allocating multiple cloud system resources to multiple mobile user’s requests with different requirements is investigated and an optimal Cloud Broker model is proposed. The cloud brokering mechanism is formulated as a Semi-Markov Decision Process (SMDP) model under the average system cost criteria, taking into consideration the cost of the occupying computing resources, the communication costs, the request traffic, and some security risk degrees and resource requirements from the multiple mobile users. Through minimizing the overall system cost, the optimal resource allocation policy is derived by using the Value Iteration Algorithm. Simulation results are provided, demonstrating the efficiency of the proposed Cloud Broker design.

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“…Works (Soni and Kalra 2014;Xu et al 2016;Li and Zhang 2016;Abazari et al 2018;Juarez et al 2018;Shojafar et al 2016) focus on makespan reduction using different methods. Papers (Birje et al 2012;Buyya et al 2008;Achar and Thilagam 2014;Goudar et al 2017;Birje et al 2014) discuss the different strategies based on game theory for developing a pricing model.…”

Section: Related Workmentioning

confidence: 99%

Utility based load balancing using firefly algorithm in cloud

Tapale

Goudar

Birje

et al. 2020

J. of Data, Inf. and Manag.

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Scheduling and load balancing are the major challenges faced in cloud scenario due to distributed computing and heterogeneous nature of the infrastructure. Scheduling of tasks to the appropriate virtual machines (VMs) can be done using different mechanisms, but balancing the load is the major problem that occurs due to fluctuation of load, and different VM specifications. This leads to imbalanced resource utilization and performance degradation of the system. To address this issue, the paper proposes a scheme that tries to maximize resource utilization while optimizing the utility (profit) using bargaining protocol, and also balances the load across cloud system by distributing jobs to reliable VMs using firefly algorithm for better performance. The proposed scheme is simulated using CloudSim tool and the results are compared with existing work. It is observed that the proposed scheme performs better than the existing work.

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A broker based approach for cloud provider selection

Cited by 16 publications

References 20 publications

Application of deterministic, stochastic, and hybrid methods for cloud provider selection

Application of deterministic, stochastic, and hybrid methods for cloud provider selection

A Semi-Markov decision model-based brokering mechanism for mobile cloud market

Utility based load balancing using firefly algorithm in cloud

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