PerfGreen: Performance and Energy Aware Resource Provisioning for Heterogeneous Clouds

Tesfatsion, Selome Kostentinos; Wadbro, Eddie; Tordsson, Johan

doi:10.1109/icac.2018.00018

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…Bhoi and Ramanuj [8] present an upgraded max-min task scheduling algorithm in cloud computing in which they consider normal or more noteworthy tasks as opposed to consistently choosing and allotting enormous assignments first as in the conventional max-min scheduling algorithm, and they see a generous decrease in makespan and successful load adjustment when the outcomes are actualized (Mao et al). [9] with Li et al [10] sets max-min calculations for task scheduling to accommodate the exact loads of a flexible cloud. The stipulated algorithm secures a work status surface to approximate the outstanding burden of VMs and the uncertain implementation time of enterprises.…”

Section: Max-min (Maximum-minimum) Algorithmmentioning

confidence: 99%

“…[9] with Li et al. [10] sets max‐min calculations for task scheduling to accommodate the exact loads of a flexible cloud. The stipulated algorithm secures a work status surface to approximate the outstanding burden of VMs and the uncertain implementation time of enterprises.…”

Section: Literature Surveymentioning

confidence: 99%

“…They then proposed a GA to allocate resources predicated on the crowd-funding findings. In [10], the authors proposed a PerfGreen as a powerful cloud resource scheduling algorithm with the primary objective of saving cloud energy. Therefore, PerfGreen is an energy-aware application-placement technique founded on a heuristic approach.…”

Section: Karthik and Sekharmentioning

confidence: 99%

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Resource scheduling approach in cloud Testing as a Service using deep reinforcement learning algorithms

Sekhar

2021

CAAI Trans on Intel Tech

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Many organizations around the world use cloud computing Testing as Service (Taas) for their services. Cloud computing is principally based on the idea of on-demand delivery of computation, storage, applications, and additional resources. It depends on delivering user services through Internet connectivity. In addition, it uses a pay-as-you-go business design to deliver user services. It offers some essential characteristics including ondemand service, resource pooling, rapid elasticity, virtualization, and measured services. There are various types of virtualization, such as full virtualization, para-virtualization, emulation, OS virtualization, and application virtualization. Resource scheduling in Taas is among the most challenging jobs in resource allocation to mandatory tasks/jobs based on the required quality of applications and projects. Because of the cloud environment, uncertainty, and perhaps heterogeneity, resource allocation cannot be addressed with prevailing policies. This situation remains a significant concern for the majority of cloud providers, as they face challenges in selecting the correct resource scheduling algorithm for a particular workload. The authors use the emergent artificial intelligence algorithms deep RM2, deep reinforcement learning, and deep reinforcement learning for Taas cloud scheduling to resolve the issue of resource scheduling in cloud Taas.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Max-min (Maximum-minimum) Algorithmmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%