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Power-aware virtual machine scheduling on clouds using active cooling control and DVFS

Authors:

Daniel Guimaraes do Lago,

Edmundo R. M. Madeira,

Luiz Fernando BittencourtAuthors Info & Claims

MGC '11: Proceedings of the 9th International Workshop on Middleware for Grids, Clouds and e-Science

Article No.: 2, Pages 1 - 6

https://doi.org/10.1145/2089002.2089004

Published: 12 December 2011 Publication History

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Abstract

This paper presents a virtual machine scheduling algorithm for Cloud Computing based on Green Computing concepts. The goal of this algorithm is the minimization of energy consumption in task executions in a cloud computing environment. This algorithm uses some features like shutdown of underutilized hosts, migration of loads of hosts that are operating below a certain threshold, and DVFS. It also applies the concept of active cooling control in order to minimize power consumption. The choice of which host will receive load is based on the concept of higher energy efficiency from the hosts, which is given by the ratio of MIPS by the energy consumed of each host. Results from simulation of this algorithm confronted with other surveyed algorithms have shown that it can improve the power consumption in clouds composed of heterogeneous datacenters while being equivalent to the best algorithms in homogeneous datacenters.

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https://doi.org/10.1109/TNSM.2022.3210204
Alkalbani AAl Ruqeishi KSalah AMohamed M(2023)Virtual machine placement in service-oriented computing environmentsService Oriented Computing and Applications10.1007/s11761-023-00362-y17:2(119-127)Online publication date: 18-Apr-2023
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Index Terms

Power-aware virtual machine scheduling on clouds using active cooling control and DVFS
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
2. Software and its engineering
  1. Software organization and properties
    1. Contextual software domains
      1. Operating systems
        Process management
        Scheduling
    2. Software system structures
      1. Distributed systems organizing principles

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Published In

MGC '11: Proceedings of the 9th International Workshop on Middleware for Grids, Clouds and e-Science

December 2011

38 pages

ISBN:9781450310680

DOI:10.1145/2089002

Editors:
Bruno Schulze
National Lab for Scientific Computing (LNCC), Brazil
,
Omer Rana
Cardiff University, UK
,
Edmundo Madeira
State University of Campinas (UNICAMP), Brazil

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 December 2011

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Fundação de Amparo à Pesquisa do Estado de São Paulo

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Middleware '11

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ACM
USENIX Assoc

Middleware '11: 12th International Middleware Conference

December 12 - 16, 2011

Lisbon, Portugal

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MGC '11 Paper Acceptance Rate 5 of 13 submissions, 38%;

Overall Acceptance Rate 14 of 36 submissions, 39%

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Lou JTang ZJia W(2023)Energy-Efficient Joint Task Assignment and Migration in Data Centers: A Deep Reinforcement Learning ApproachIEEE Transactions on Network and Service Management10.1109/TNSM.2022.321020420:2(961-973)Online publication date: Jun-2023
https://doi.org/10.1109/TNSM.2022.3210204
Alkalbani AAl Ruqeishi KSalah AMohamed M(2023)Virtual machine placement in service-oriented computing environmentsService Oriented Computing and Applications10.1007/s11761-023-00362-y17:2(119-127)Online publication date: 18-Apr-2023
https://doi.org/10.1007/s11761-023-00362-y
Amici CRotilio MBerardinis PCucchiella F(2022)Framework for Computerizing the Processes of a Job and Automating the Operational Management on Site—A Case Study of Demolition and Reconstruction Construction SiteBuildings10.3390/buildings1206080012:6(800)Online publication date: 10-Jun-2022
https://doi.org/10.3390/buildings12060800
Ayeesha Nasreen MRavindran S(2022)Long short‐term memory‐based power‐aware algorithm for prompt heterogenous activityInternational Journal of Communication Systems10.1002/dac.516335:10Online publication date: 18-Apr-2022
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Hummaida APaton NSakellariou R(2022)A hierarchical decentralized architecture to enable adaptive scalable virtual machine migrationConcurrency and Computation: Practice and Experience10.1002/cpe.748735:2Online publication date: 18-Nov-2022
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Метелкин ЯМаковий К(2021)Hardware resource use intellectualization of data centers providing cloud-computing servicesМОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ10.26102/2310-6018/2021.35.4.0369:4(35)(36-37)Online publication date: 20-Oct-2021
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Lago Dda Silva RMadeira Eda Fonseca NMedhi D(2021)SinergyCloud: A simulator for evaluation of energy consumption in data centers and hybrid cloudsSimulation Modelling Practice and Theory10.1016/j.simpat.2021.102329110(102329)Online publication date: Jul-2021
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Hagras TAtef AMahdy Y(2021)Greening Duplication-Based Dependent-Tasks Scheduling on Heterogeneous Large-Scale Computing PlatformsJournal of Grid Computing10.1007/s10723-021-09554-219:1Online publication date: 13-Mar-2021
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Liang BDong XWang YZhang X(2020)A low-power task scheduling algorithm for heterogeneous cloud computingThe Journal of Supercomputing10.1007/s11227-020-03163-8Online publication date: 18-Jan-2020
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Recommendations

Performance-to-Power Ratio Aware Virtual Machine (VM) Allocation in Energy-Efficient Clouds

Power-efficient distributed scheduling of virtual machines using workload-aware consolidation techniques

Load and Thermal-Aware VM Scheduling on the Cloud

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