An improved thermodynamic simulated annealing-based approach for resource-skewness-aware and power-efficient virtual machine consolidation in cloud datacenters
Cloud computing attracted great attention in both industry and research communities for the sake of its ubiquitous, elasticity and economic services. The first class concern of cloud providers is power management for both reducing their total cost of ownership and green computing objectives. To reac...
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| Vydané v: | Soft computing (Berlin, Germany) Ročník 25; číslo 7; s. 5233 - 5260 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.04.2021
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| ISSN: | 1432-7643, 1433-7479 |
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| Abstract | Cloud computing attracted great attention in both industry and research communities for the sake of its ubiquitous, elasticity and economic services. The first class concern of cloud providers is power management for both reducing their total cost of ownership and green computing objectives. To reach the goal, a system framework is presented which has different modules. The main concentration of the paper is on virtual machine (VM) consolidation module which launches users requested VMs on the minimum number of active servers to reduce datacenter total power consumption (
TPC
). In this paper, the VMs consolidation is abstracted to two-dimensional bin-packing problem and also is formulated to an integer linear programming. Since the papers in the literature scarcely are aware of skewness in resources of requested VMs and for discrete nature of search space, this paper presents the resource skewness-aware VMs consolidation algorithm based on improved thermodynamic simulated annealing approach because resource skewness potentially compels the algorithm to activate additional servers. The proposed SA-based algorithm is validated in extensive scenarios with different resource skewness in comparison with two heuristics and two meta-heuristics. The average results reported from different scenarios proves superiority of proposed algorithm in comparison with other approaches in terms of the number of used servers,
TPC
, and total resource wastage of datacenter. |
|---|---|
| AbstractList | Cloud computing attracted great attention in both industry and research communities for the sake of its ubiquitous, elasticity and economic services. The first class concern of cloud providers is power management for both reducing their total cost of ownership and green computing objectives. To reach the goal, a system framework is presented which has different modules. The main concentration of the paper is on virtual machine (VM) consolidation module which launches users requested VMs on the minimum number of active servers to reduce datacenter total power consumption (
TPC
). In this paper, the VMs consolidation is abstracted to two-dimensional bin-packing problem and also is formulated to an integer linear programming. Since the papers in the literature scarcely are aware of skewness in resources of requested VMs and for discrete nature of search space, this paper presents the resource skewness-aware VMs consolidation algorithm based on improved thermodynamic simulated annealing approach because resource skewness potentially compels the algorithm to activate additional servers. The proposed SA-based algorithm is validated in extensive scenarios with different resource skewness in comparison with two heuristics and two meta-heuristics. The average results reported from different scenarios proves superiority of proposed algorithm in comparison with other approaches in terms of the number of used servers,
TPC
, and total resource wastage of datacenter. |
| Author | Hosseini Shirvani, Mirsaeid Saeedi, Pedram |
| Author_xml | – sequence: 1 givenname: Pedram surname: Saeedi fullname: Saeedi, Pedram organization: Department of Computer Engineering, Sari Branch, Islamic Azad University – sequence: 2 givenname: Mirsaeid surname: Hosseini Shirvani fullname: Hosseini Shirvani, Mirsaeid email: mirsaeid_hosseini@iausari.ac.ir organization: Department of Computer Engineering, Sari Branch, Islamic Azad University |
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| Cites_doi | 10.1016/j.advengsoft.2016.01.008 10.1016/j.engappai.2020.103501 10.1007/s11277-019-06440-9 10.1016/j.cie.2017.12.001 10.1016/j.future.2018.02.026 10.1080/08839514.2019.1689714 10.1007/s10489-015-0710-x 10.1109/icnn.1995.488968 10.1016/0196-6774(85)90018-5 10.1007/s11063-014-9339-8 10.1109/CUBE.2013.12 10.1002/spe.2528 10.1109/MITP.2018.053891338 10.1002/cpe.1867 10.1016/j.eswa.2014.03.039 10.1126/science.220.4598.671 10.1007/s10489-020-01633-3 10.1007/s10586-011-0177-4 10.1016/j.advengsoft.2013.12.007 10.1007/978-1-4939-2092-1 10.1016/j.suscom.2020.100374 10.1002/j.1538-7305.1948.tb01338.x 10.1016/j.physleta.2003.08.070 10.1535/itj.1201.06 10.1109/TCC.2016.2617374 10.1016/j.jksuci.2018.07.001 10.1016/j.comcom.2014.02.008 10.1016/j.jcss.2013.02.004 10.1016/j.jss.2014.11.014 10.1080/0952813X.2020.1725652 10.1109/INISTA.2018.8466267 |
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| Keywords | Server consolidation Simulated annealing algorithm Cloud computing Resource skewness |
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| References | GaoYGuanHQiZHouYLiuLA multi-objective ant colony system algorithm for virtual machine placement in cloud computingJ Comput Syst Sci201379812301242307921710.1016/j.jcss.2013.02.0041410.68038 ReddyVDSetzBRaoGSVRKGangadharanGAielloMBest practices for sustainable datacenterIT Prof2018205576710.1109/MITP.2018.053891338 AddyaSKTurukAKSahooBSarkarMBiswashSKSimulated annealing based VM placement strategy to maximize the profit for cloud service providersEng Sci Technol Int J20172012491259 Hosseini ShirvaniMTo move or not to move: an iterative four-phase cloud adoption decision model for IT outsourcing based on TCOJ Soft Comput Inf Technol202091717 Jian-pingLLiXMin-rongCHybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centersExpert Syst Appl201441135804581610.1016/j.eswa.2014.03.039 HabeeraTPMadhu KumarSDSalamSMKrishnanKMOptimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithmEng Sci Technol Int J2017202017616628 LiHLiWWangHWangJAn optimization of virtual machine selection and placement by using memory content similarity for server consolidation in cloudFut Gener Comput Syst201810.1016/j.future.2018.02.026 BakerBSA new proof for the first-fit decreasing bin-packing algorithmJ Algorithms198561497078085010.1016/0196-6774(85)90018-50563.68042 Hosseini ShirvaniMBabazadeh GorjiAOptimisation of automatic web services composition using genetic algorithmInt J Cloud Comput202092115 BlaglazovABuyyaROptimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centersConcurr Comput Pract Exp201124131397142010.1002/cpe.1867 Hosseini ShirvaniMA hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systemsEng Appl Artif Intell20209012010.1016/j.engappai.2020.103501 LeTDWright, Scheduling workloads in a network of datacenters to reduce electricity cost and carbon footprintSustain Comput Inf Syst201553140 FarzaiSHosseini ShirvaniMRabbaniMMulti-objective communication-aware optimization for virtual machine placement in cloud datacentersSustain Comput Inf Syst20202810037410.1016/j.suscom.2020.100374 FilaniDHeJGaoSRajappaMKumarAShahPNagappanRDynamic data center power management: trends, issues, and solutionsIntel Technol J20081219310.1535/itj.1201.06 Hosseini ShirvaniMRahmaniAMSahafiAA survey study on virtual machine migration and server consolidation techniques in DVFS-enabled cloud datacenter: taxonomy and challengesJ King Saud Univ Comput Inf Sci202032326728610.1016/j.jksuci.2018.07.001 MokaripoorPHosseini ShirvaniMA state of the art survey on DVFS techniques in cloud computing environmentJ Multidiscip Eng Sci Technol201635545559 Adamuthe A, Pandharpatte RM, Thampai GT (2013) Multi-objective virtual machine placement in cloud environment. In: 2013 international conference on cloud and ubiquitous computing and emerging technologies Hosseini Shirvani M (2018a) A new shuffled genetic-based task scheduling algorithm in heterogeneous distributed systems. J Advan Comput Res 9(4):19–36 Mills M (2013) The cloud begins with coal-an overview of the electricity used by the global digital ecosystem. Technical Report, Digital Power Group, Washington, DC HosseinzadehSHosseini ShirvaniMOptimizing energy consumption in clouds by using genetic algorithmJ Multidiscip Eng Sci Technol20152614311434 ReddyMARavindranathKVirtual machine placement using JAYA optimization algorithmAppl Artif Intell201910.1080/08839514.2019.1689714 Babazadeh GorjiRHosseini ShirvaniMRamezaniFA new image encryption method using chaotic mapJ Multidiscip Eng Sci Technol20152216 ShannonCEBell Syst Tech J19482737910.1002/j.1538-7305.1948.tb01338.x BrownRReport to congress on server and data center energy efficiency: public law 109–4312008BerkeleyLawrence Berkeley National Laboratory Hosseini Shirvani M (2018b) Web service composition in multi-cloud environment: a bi-objective genetic optimization algorithm. In 2018 innovations in intelligent systems and applications (INISTA). IEEE, New York, pp 1–6. https://doi.org/10.1109/INISTA.2018.8466267 SaitSMBalaAEl-MalehAHCuckoo search based resource optimization of datacentersAppl Intell20164448950610.1007/s10489-015-0710-x TavanaMShahdi-PashakiSTeymourianESantos-ArteagaFJKomakiMA discrete cuckoo optimization algorithm for consolidation in cloud computingComput Ind Eng201710.1016/j.cie.2017.12.001 MoschakisIoannis AKaratzaHelen DMulti-criteria scheduling of Bag-of-Tasks applications on heterogeneous interlinked clouds with simulated annealingJ Syst Softw201410.1016/j.jss.2014.11.014 Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol IV, pp 1942–1948. https://doi.org/10.1109/icnn.1995.488968 MirjaliliSeyedaliLewisAndrewThe whale optimization algorithmAdv Eng Softw201695516710.1016/j.advengsoft.2016.01.008 QinYWangHYiSVirtual machine placement based on multi-objective reinforcement learningAppl Intell202010.1007/s10489-020-01633-3 Hosseini ShirvaniMGhojoghiAServer consolidation schemes in cloud computing environment: a reviewEur J Eng Res Sci2018131824 MirjaliliSMirjaliliSMLewisAGrey wolf optimizerAdv Eng Softw201469466110.1016/j.advengsoft.2013.12.007 Van HeddeghemWLambertSLannooBColleDPickavetMDemeesterPTrends in worldwide ICT electricity consumption from 2007 to 2012Comput Commun201450647610.1016/j.comcom.2014.02.008 Hosseini ShirvaniMBi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithmJ Exp Theor Artif Intell202010.1080/0952813X.2020.1725652 TangMPanSA hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centersNeural Process Lett20154121122110.1007/s11063-014-9339-8 KirkpatrickSGelattCDVecchiMPOptimization by simulated annealingScience198322067168070248510.1126/science.220.4598.671 de VicenteJLancharesJHermidaRPlacement by thermodynamic simulated annealingPhys Lett A20033175641542310.1016/j.physleta.2003.08.070 KhanSUZomayaAYHandbook on datacenters2015New YorkSpringer10.1007/978-1-4939-2092-1 Hosseini ShirvaniMRahmaniAMSahafiAAn iterative mathematical decision model for cloud migration: a cost and security risk approachSoftw Pract Exp201848344948510.1002/spe.2528 KliazovichDBouvryPKhanSUDENS: data center energy-efficient network-aware schedulingClust Comput201316657510.1007/s10586-011-0177-4 Amazon (2020). http://www.amazon.com/. 6 May 2020 Amazon EC2 (2020). http://aws.amazon.com/EC2. 6 May 2020 OsamyWEl-sawyAAKhedrAMSATC: a simulated annealing based tree construction and scheduling algorithm for minimizing aggregation time in wireless sensor networksWirel Pers Commun201910892193810.1007/s11277-019-06440-9 FarahnakianFPahikkalaTLiljebergPPlosilaJTrung HieuNTenhunenHEnergy-aware VM consolidation in cloud data centers using utilization prediction modelIEEE Trans Cloud Comput20207252453610.1109/TCC.2016.2617374 MA Reddy (5523_CR38) 2019 S Mirjalili (5523_CR33) 2014; 69 A Blaglazov (5523_CR7) 2011; 24 S Farzai (5523_CR11) 2020; 28 D Filani (5523_CR12) 2008; 12 5523_CR31 W Osamy (5523_CR36) 2019; 108 D Kliazovich (5523_CR28) 2013; 16 P Mokaripoor (5523_CR34) 2016; 3 J de Vicente (5523_CR9) 2003; 317 TD Le (5523_CR29) 2015; 5 Y Qin (5523_CR37) 2020 5523_CR1 F Farahnakian (5523_CR10) 2020; 7 S Kirkpatrick (5523_CR27) 1983; 220 5523_CR4 M Hosseini Shirvani (5523_CR18) 2020 5523_CR3 5523_CR15 M Hosseini Shirvani (5523_CR20) 2018; 1 H Li (5523_CR30) 2018 Ioannis A Moschakis (5523_CR35) 2014 W Van Heddeghem (5523_CR44) 2014; 50 L Jian-ping (5523_CR24) 2014; 41 M Hosseini Shirvani (5523_CR21) 2018; 48 M Tang (5523_CR42) 2015; 41 R Babazadeh Gorji (5523_CR5) 2015; 2 5523_CR500 M Tavana (5523_CR43) 2017 M Hosseini Shirvani (5523_CR16) 2020; 90 VD Reddy (5523_CR39) 2018; 20 SK Addya (5523_CR2) 2017; 20 R Brown (5523_CR8) 2008 Seyedali Mirjalili (5523_CR32) 2016; 95 CE Shannon (5523_CR41) 1948; 27 S Hosseinzadeh (5523_CR23) 2015; 2 SU Khan (5523_CR26) 2015 SM Sait (5523_CR40) 2016; 44 5523_CR25 M Hosseini Shirvani (5523_CR22) 2020; 32 M Hosseini Shirvani (5523_CR17) 2020; 9 M Hosseini Shirvani (5523_CR19) 2020; 9 BS Baker (5523_CR6) 1985; 6 Y Gao (5523_CR13) 2013; 79 TP Habeera (5523_CR14) 2017; 20 |
| References_xml | – reference: Hosseini ShirvaniMBabazadeh GorjiAOptimisation of automatic web services composition using genetic algorithmInt J Cloud Comput202092115 – reference: Hosseini ShirvaniMRahmaniAMSahafiAA survey study on virtual machine migration and server consolidation techniques in DVFS-enabled cloud datacenter: taxonomy and challengesJ King Saud Univ Comput Inf Sci202032326728610.1016/j.jksuci.2018.07.001 – reference: BrownRReport to congress on server and data center energy efficiency: public law 109–4312008BerkeleyLawrence Berkeley National Laboratory – reference: Hosseini ShirvaniMGhojoghiAServer consolidation schemes in cloud computing environment: a reviewEur J Eng Res Sci2018131824 – reference: ReddyVDSetzBRaoGSVRKGangadharanGAielloMBest practices for sustainable datacenterIT Prof2018205576710.1109/MITP.2018.053891338 – reference: Mills M (2013) The cloud begins with coal-an overview of the electricity used by the global digital ecosystem. Technical Report, Digital Power Group, Washington, DC – reference: FarahnakianFPahikkalaTLiljebergPPlosilaJTrung HieuNTenhunenHEnergy-aware VM consolidation in cloud data centers using utilization prediction modelIEEE Trans Cloud Comput20207252453610.1109/TCC.2016.2617374 – reference: de VicenteJLancharesJHermidaRPlacement by thermodynamic simulated annealingPhys Lett A20033175641542310.1016/j.physleta.2003.08.070 – reference: HosseinzadehSHosseini ShirvaniMOptimizing energy consumption in clouds by using genetic algorithmJ Multidiscip Eng Sci Technol20152614311434 – reference: Hosseini ShirvaniMTo move or not to move: an iterative four-phase cloud adoption decision model for IT outsourcing based on TCOJ Soft Comput Inf Technol202091717 – reference: MirjaliliSeyedaliLewisAndrewThe whale optimization algorithmAdv Eng Softw201695516710.1016/j.advengsoft.2016.01.008 – reference: Hosseini ShirvaniMBi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithmJ Exp Theor Artif Intell202010.1080/0952813X.2020.1725652 – reference: BlaglazovABuyyaROptimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centersConcurr Comput Pract Exp201124131397142010.1002/cpe.1867 – reference: Adamuthe A, Pandharpatte RM, Thampai GT (2013) Multi-objective virtual machine placement in cloud environment. 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| Title | An improved thermodynamic simulated annealing-based approach for resource-skewness-aware and power-efficient virtual machine consolidation in cloud datacenters |
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