Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach

Liu, Xiao-Fang; Zhan, Zhi‐Hui; Du, Ke-Lin; Chen, Wei-Neng

doi:10.1145/2576768.2598265

Cited by 77 publications

(39 citation statements)

References 15 publications

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“…Within a single application, this can be achieved by careful resource provisioning to reach a given performance goal [17,49,71] or reducing the required headroom [41,44]. At the infrastructure level, mapping algorithms are used to co-locate applications with antagonist resource requirements [13,43,66]. Since most IaaS tenants tend to overprovision their VM demands, resource overbooking (over-commit) [23,63,64] was used to accept more tenants if the resource usage is predicted to allow so without SLA violations.…”

Section: Related Workmentioning

confidence: 99%

Incentivizing self-capping to increase cloud utilization

Shahrad

Klein

Zheng

et al. 2017

Proceedings of the 2017 Symposium on Cloud Computing

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Cloud Infrastructure as a Service (IaaS) providers continually seek higher resource utilization to better amortize capital costs. Higher utilization not only can enable higher proit for IaaS providers but also provides a mechanism to raise energy eiciency; therefore creating greener cloud services. Unfortunately, achieving high utilization is diicult mainly due to infrastructure providers needing to maintain spare capacity to service demand luctuations.Graceful degradation is a self-adaptation technique originally designed for constructing robust services that survive resource shortages. Previous work has shown that graceful degradation can also be used to improve resource utilization in the cloud by absorbing demand luctuations and reducing spare capacity. In this work, we build a system and pricing model that enables infrastructure providers to incentivize their tenants to use graceful degradation. By using graceful degradation with an appropriate pricing model, the infrastructure provider can realize higher resource utilization while simultaneously, its tenants can increase their proit. Our proposed solution is based on a hybrid model which guarantees both reserved and peak on-demand capacities over lexible periods. It also includes a global dynamic price pair for capacity which remains uniform during each tenant's Service Level Agreement (SLA) term.We evaluate our scheme using simulations based on real-world traces and also implement a prototype using RUBiS on the Xen hypervisor as an end-to-end demonstration. Our analysis shows that the proposed scheme never hurts a tenant's net proit, but can improve it by as much as 93%. Simultaneously, it can also improve the efective utilization of contracts from 42% to as high as 99%.

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Section: Related Workmentioning

confidence: 99%

Incentivizing self-capping to increase cloud utilization

Shahrad

Klein

Zheng

et al. 2017

Proceedings of the 2017 Symposium on Cloud Computing

View full text Add to dashboard Cite

show abstract

“…Moreover, to compare the performance of the presented approach against genetic algorithm which was aforementioned, we have utilized six different dimensions of job scheduling problem, namely, (3, 13), (5, 100), (8,60), (10,50), (60, 500), and (100, 1000) as used by Liu et al in [47]. For each testing case, the notation ( , ) is employed to indicate the number of VMs on the cloud ( ) and the number of jobs ( ) to be scheduled.…”

Section: Data Sets and Experimental Settingsmentioning

confidence: 99%

“…2 ~ 6 illustrate the performance of the two algorithms in terms of the makespan for the other five ( , ) pairs, i.e. (5,100), (8,60), (10,50), (60, 500), and (100, 1000). For the small and middle-sized scheduling problems, the results depicted in Figs.…”

Section: Fig 1 Performance Comparison For Test Case (3 13)mentioning

confidence: 99%

“…The virtualization technology further makes cloud computing different from the traditional paradigms, and also makes cloud computing more convenient in the commercialization [8]. Virtualization provides a promising approach to separate the hardware and software resources and allows running several independent virtual servers on a single physical device [9].…”

Section: Introductionmentioning

confidence: 99%

“…Each VM is an abstract unit of computing, memory and storage capacities in the cloud. The size of CPU, memory, and other resources in VMs can be configured according to the timevarying demand of consumers [8] and, accordingly the cloud resources are more effectively and efficiently utilized.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Simplified Particle Swarm Optimization for Job Scheduling in Cloud Computing

Attiya¹,

Zhang²

2017

IJCA

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Recent advances in various areas such as networking, information and communication technologies have greatly boosted the potential capabilities of cloud computing and made it become more prevalent in recent years. Cloud computing is a promising computing paradigm that facilitates the delivery of IT infrastructure, platforms, and applications of any kind to consumers as services over the internet. Although cloud computing systems nowadays provide better ways to accomplish the job requests in terms of responsiveness and scalability under various workloads, scheduling of jobs or tasks in cloud environment is still NPcomplete and complex in nature due to the dynamicity of resources and on-demand user application requirements. In this paper, a simplified version of particle swarm optimization (PSO) algorithm is proposed to solve the job scheduling problem in cloud computing environment. To evaluate the performance of the proposed approach, this study compares the proposed PSO strategy with genetic algorithm (GA), by having both of them implemented on CloudSim toolkit. The results obtained demonstrate that the presented PSO algorithm can significantly reduce the makespan of job scheduling problem compared with the other metaheuristic algorithm evaluated in this paper.

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Comparison of workload consolidation algorithms for cloud data centers

Ponto

Kecskeméti

Mann

2021

Concurrency and Computation

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Workload consolidation is an important method for the efficient operation of cloud data centers, impacting important quality attributes such as resource utilization and power consumption. Many different approaches have been proposed for workload consolidation, but few comparative studies were executed to date. Therefore, it is unclear which of the proposed approaches work best in which situation. In this article, we present a comprehensive simulation‐based comparison of five workload consolidation techniques. We introduce a general framework for workload consolidation techniques to the DISSECT‐CF simulator to foster the development and comparison of efficient data center consolidation algorithms. We use this framework to evaluate the effectiveness of a first fit best fit decreasing heuristic, a custom heuristic, and three population‐based metaheuristics (genetic algorithm, artificial bee colony, and particle swarm optimization). The evaluation is based on a wide variety of real‐world workload traces. The five algorithms are compared in terms of total energy consumption, the duration of the simulation, and the number of migrations. Based on the results, there is no generally best consolidation technique. The results deliver insight into the pros and cons of the algorithms as well as the impact of different parameters. In particular, the results show that population‐based metaheuristics do not offer a significant gain in terms of solution quality to compensate for the increased simulation time.

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Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach

Cited by 77 publications

References 15 publications

Incentivizing self-capping to increase cloud utilization

Incentivizing self-capping to increase cloud utilization

A Simplified Particle Swarm Optimization for Job Scheduling in Cloud Computing

Comparison of workload consolidation algorithms for cloud data centers

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