Energy and carbon-aware initial VM placement in geographically distributed cloud data centers

Khodayarseresht, Ehsan; Shameli‐Sendi, Alireza; Fournier, Quentin; Dagenais, Michel R.

doi:10.1016/j.suscom.2023.100888

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2024

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Cited by 2 publications

References 21 publications

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Carbon-Aware and Fault-Tolerant Migration of Deep Learning Workloads in the Geo-Distributed Cloud

Park,

Kim,

Kim

et al. 2024

2024 IEEE 17th International Conference on Cloud Computing (CLOUD)

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Carbon-Aware and Fault-Tolerant Migration of Deep Learning Workloads in the Geo-Distributed Cloud

Park,

Kim,

Kim

et al. 2024

2024 IEEE 17th International Conference on Cloud Computing (CLOUD)

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SSEPC cloud: Carbon footprint aware power efficient virtual machine placement in cloud milieu

Parida,

Rath,

Pati

et al. 2024

COMSIS J

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The consumption of energy and carbon emission in cloud datacenters are the alarming issues in recent times, while optimizing the average response time and service level agreement (SLA) violations. Handful of researches have been conducted in these domains during virtual machine placement (VMP) in cloud milieu. Moreover it is hard to find researches on VMP considering the cloud regions and the availability zones along with the datacenters, although both of them play significant roles in VMP. Hence, we have worked on a novel approach to propose a hybrid metaheuristic technique combining the salp swarm optimization and emperor penguins colony algorithm, i.e. SSEPC to place the virtual machines in the most suitable regions, availability zones, datacenters, and servers in a cloud environment, while optimizing the mentioned quality of service parameters. Our suggested technique is compared with some of the contemporary hybrid algorithms in this direction like Sine Cosine Algorithm and Salp Swarm Algorithm (SCA-SSA), Genetic Algorithm and Tabu-search Algorithm (GATA), and Order Exchange & Migration algorithm and Ant Colony System algorithm (OEMACS) to test its efficacy. It is found that the proposed SSEPC is consuming 4.4%, 8.2%, and 16.6% less energy and emitting 28.8%, 32.83%, and 37.45% less carbon, whereas reducing the average response time by 11.43%, 18.57%, and 26% as compared to its counterparts GATA, OEMACS, and SCA-SSA respectively. In case of SLA violations, SSEPC has shown its effectiveness by lessening the value of this parameter by 0.4%, 1.2%, and 2.8% as compared to SCA-SSA, GATA, and OEMACS respectively.

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Energy and carbon-aware initial VM placement in geographically distributed cloud data centers

Cited by 2 publications

References 21 publications

Carbon-Aware and Fault-Tolerant Migration of Deep Learning Workloads in the Geo-Distributed Cloud

Carbon-Aware and Fault-Tolerant Migration of Deep Learning Workloads in the Geo-Distributed Cloud

SSEPC cloud: Carbon footprint aware power efficient virtual machine placement in cloud milieu

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