An approach toward design and development of an energy-aware VM selection policy with improved SLA violation in the domain of green cloud computing

Mandal, Riman; Mondal, Manash Kumar; Banerjee, Sourav; Biswas, Utpal

doi:10.1007/s11227-020-03165-6

Cited by 43 publications

(20 citation statements)

References 36 publications

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“…This solution reduces energy consumption but has a high service level agreement violations. Paper 45 presented an energy-aware virtual machine selection policy. The proposed solution uses the two criteria of current resource utilization and the amount of resource allocated to each virtual machine to select the appropriate virtual machine.…”

Section: Refmentioning

confidence: 99%

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An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

2021

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In recent years, cloud computing has become more popular because of advancements in virtualization technology. By increasing the number of servers in cloud computing environment, cloud data centers have expanded and consumed much energy. Virtual machine consolidation is a solution for energy management in cloud environment. On the other hand, by increasing resource utilization in virtual machine consolidation, service level agreement assurance is difficult to obtain. Two main challenges in virtual machine consolidation are timely detection of overloaded servers and proper immigrant virtual machine selection from detected servers. In this paper, a new model is proposed based on MAPE-k loop for autonomous virtual machine selection. The presented model uses a proposed ensemble prediction algorithm in the analysis phase. Also, in the planning phase, a new multi-heuristics algorithm with flexible weights using learning automata is proposed. The effectiveness of the proposed model is evaluated by CloudSim simulator under real workload as compared with well-known algorithms in this domain. The experimental results indicate that, the proposed approach has averagely improved the balance between service level agreement violations, energy and migration counts by 47.39% compared to other methods.

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Section: Refmentioning

confidence: 99%

“…The other two methods are the combined methods of MPCM 46 and MuMa. 45 The MPCM method selects virtual machines that have a minimum product of RAM and CPU utilization. The MuMa method also selects virtual machines with the highest amount of resource utilization and the lowest amount of allocated resource.…”

Section: The Plan Phase Evaluationmentioning

confidence: 99%

An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

2021

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“…The Partitioned Balanced Time Scheduling algorithm [7] calculates the suitable number of resources requested per billing slot so that the cost is reduced and a deadline is satisfied. Other dynamic approaches incorporate those presented by [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. The main limitation of these techniques is their task-level optimal scenario, which is a trade-off for their adjustability to unpredicted delays.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Resource Provisioning and Scheduling Algorithm for Scientific Workflows on IaaS Cloud

Rajasekar

Yogesh

2021

SN COMPUT. SCI.

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Scientific workflow applications are deployed to run extensive volumes of data and to manage comprehensive observations and simulations. They are resource-intensive and time-utilizing applications that profit from processing in distributed environments. Especially, workflow applications can highly support the simple access, scalability, and affordability provided by cloud computing. To attain this, disruptive and well-planned operation of managing the workflow tasks and running the compute pool in a cost-effective mode essential to be evolved. We present an adaptive resource provisioning and scheduling algorithm for scientific workflows on Infrastructure as a Service (IaaS) clouds. Our approach was planned to deal challenges especially to clouds, such as unlimited on-demand access, heterogeneity, performance variation and pay-per-use type (i.e., per minute billing). To correspondingly efficient to these uncertainties of cloud, therefore, our approach was developed with consider these necessary problems to run, and is achievable in making effective solutions that satisfy a user-described deadline and reduce the spending cost of the utilized environment using the service of resource provisioning and scheduling. Finally, experimental results show that it executes a workflow effectively with regard to achieving deadline and minimizing cost than other advanced algorithms.

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“…CO 2 ) [4]. Therefore, devising energy-efficient schemes is an essential goal in cloud datacenters [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Design and evaluation of a joint profit and interference-aware VMs consolidation in IaaS cloud datacenter

RahimiZadeh

Dehghani

2021

Cluster Comput

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Cloud service providers employ virtualization technologies in order to deliver energy efficiency and optimize resource utilization in cloud datacenters. In this way, Virtual Machine (VM) consolidation is an efficient technique for reducing energy related costs and environmental concerns. This technique consists of placing as much as possible VMs on as less as possible Physical Machines (PM)s by means of live VM migration. However, VM consolidation approaches bring advantages, they cannot efficiently guarantee performance isolation of co-located VMs resulting in operational interference issues. Therefore, it is necessary to be aware of workload performance degradation issues during VMs consolidation in a datacenter. In this paper, we propose a novel joint profit and interference-aware scheduling scheme (PIAS) to efficiently consolidate VMs that host multi-tier application workloads in Infrastructure-as-a-Service (IaaS) cloud datacenter. The PIAS scheme considers profit and cost of power consumption, operational interference of VMs, resource utilization, and service level agreements (SLA)s as well as amount of transferred memory pages during live VM migration for consolidating VMs. In this regard, a stochastic dynamic programming is introduced to model the operational behavior of VM. Also, an optimization problem is introduced to maximize provider's profit and minimize the overall execution cost of application workloads. We simulate a series of experiments based on real workloads traces for different IaaS cloud datacenter configurations. The PIAS scheme on average provides better results than other competitors and the results demonstrate how our approach at least improves profit, energy efficiency, and service down time by 40%, 29%, and 35%, respectively.

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An approach toward design and development of an energy-aware VM selection policy with improved SLA violation in the domain of green cloud computing

Cited by 43 publications

References 36 publications

An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

Adaptive Resource Provisioning and Scheduling Algorithm for Scientific Workflows on IaaS Cloud

Design and evaluation of a joint profit and interference-aware VMs consolidation in IaaS cloud datacenter

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