Multi-objective optimization model of virtual resources scheduling under cloud computing and it's solution

Zhao, Jianfeng; Zeng, Wenhua; Liu, Min; Li, Guangming

doi:10.1109/csc.2011.6138518

Cited by 22 publications

(12 citation statements)

References 8 publications

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“…Além disso, os testes deveriam ser adequados para serem executados em aplicações reais. Zhao e colaboradores [19] propõem um modelo de escalonamento de recursos virtuais baseado na seleção por meio de algoritmos genéticos multi-objetivo (NSGA II -Nondominated Sorting Genetic Algorithm). Esse modelo foi avaliado pelo equilíbrio da carga distribuída, recursos físicos e virtuais com a análise de escalonamento dos recursos virtuais.…”

Section: Trabalhos Relacionadosunclassified

An Analysis of Optimization Algorithms designed to fully comply with SLA in Cloud Computing

Nakamura

Azevedo

Batista

et al. 2017

IEEE Latin Am. Trans.

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Abstract-Nowadays the access to a cloud computing environment is provided on-demand offering transparent services to customers. Although the cloud allows an abstraction of the behavior of the service providers in the infrastructure (involving logical and physical resources), it remains a challenge to fully comply with the Service Level Agreements (SLAs), because, depending on the service demand and system configuration, the providers may not be able to meet the requirements of the customers. There is a need for mechanisms that take account of load balancing algorithms to provide an efficient load distribution with the available resources. However, the studies in the literature do not effectively address the problem of the availability of resources to meet customers' requirements with analysis restricted to a limited set of objectives. This paper proposes algorithms to address the need for optimization when handling computational resources during the execution time. The methods optimize the efficient use of the resources available in the infrastructure aiming to comply with the service level agreements defined between client and provider.

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Section: Trabalhos Relacionadosunclassified

An Analysis of Optimization Algorithms designed to fully comply with SLA in Cloud Computing

Nakamura

Azevedo

Batista

et al. 2017

IEEE Latin Am. Trans.

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“…Providers' Revenue [7]- [9] Power / Energy Consumption [8], [10]- [14] Load Balancing [5], [15] Response Time [2], [11] Minimizing number of servers used [12], [13], [16] Improve Utilization of Cloud resources [17], [18] Throughput [11] Process maximum requests of customers [19] with attributes CPU, memory and bandwidth can be described formally as v(c,m,b), where c is attribute CPU, m is attribute memory and b is bandwidth. Similarly the physical resource can be described using p(c,m,b).…”

Section: Objectives Referencesmentioning

confidence: 99%

“…Load Balancing: In [5], [15] author presents virtual resource allocation on physical resources in datacenter of Cloud providers' with balancing load on each physical machine.…”

Section: Prior Workmentioning

confidence: 99%

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Multiobjective Virtual Machine Placement in Cloud Environment

Adamuthe¹,

Pandharpatte²,

Thampi

2013

2013 International Conference on Cloud &Amp; Ubiquitous Computing &Amp; Emerging Technologies

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In this paper, the virtual machine placement problem is formulated as a multi-objective optimization problem. The objectives are maximizing profit, maximizing load balancing and minimizing recourse wastage. Results of Genetic algorithms, Non-dominated Sorting Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II are compared with common solution representation, penalty and benefit values. All the three algorithms reported good solutions whereas GA and NSGA are subjected to premature convergence and duplicate solutions. NSGA-II gives good and diversified range of solutions.

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“…Among others, Kessaci et al [84] and Zhao et al [85] treated the scheduling problem as a multiobjective optimization problem. A intuitive way to solve the scheduling problem is to employ the well-known multiobjective GA (NSGA II) the results of which take into account the cost of CPU, memory, and bandwidth at the same time [85].…”

Section: B Metaheuristic Scheduling Algorithmsmentioning

confidence: 99%

Metaheuristic Scheduling for Cloud: A Survey

Tsai

Rodrigues

2014

IEEE Systems Journal

207

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Cloud computing has become an increasingly important research topic given the strong evolution and migration of many network services to such computational environment. The problem that arises is related with efficiency management and utilization of the large amounts of computing resources. This paper begins with a brief retrospect of traditional scheduling, followed by a detailed review of metaheuristic algorithms for solving the scheduling problems by placing them in a unified framework. Armed with these two technologies, this paper surveys the most recent literature about metaheuristic scheduling solutions for cloud. In addition to applications using metaheuristics, some important issues and open questions are presented for the reference of future researches on scheduling for cloud.

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Multi-objective optimization model of virtual resources scheduling under cloud computing and it's solution

Cited by 22 publications

References 8 publications

An Analysis of Optimization Algorithms designed to fully comply with SLA in Cloud Computing

An Analysis of Optimization Algorithms designed to fully comply with SLA in Cloud Computing

Multiobjective Virtual Machine Placement in Cloud Environment

Metaheuristic Scheduling for Cloud: A Survey

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