SLA‐Aware and Energy‐Efficient VM Consolidation in Cloud Data Centers Using Host State 3rd‐Order Markov Chain Model

Li, Lianpeng; Dong, Jian; Zuo, Decheng; Ji, Songyan

doi:10.1049/cje.2020.10.008

Cited by 4 publications

(1 citation statement)

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“…(Dingetal.,2020)proposedaframeworkthatcontainsfivealgorithmsbasedontheperformanceto-powerratio(PPR)andthepredictionmodeltoresolvethetrade-offbetweenenergyconsumption andSLAviolations.Thepredictionalgorithmusesthemovingaverage(MA)andtheinterquartile range(IQR)techniques,anoverloaddetectionalgorithmbuiltontheresidualavailablecomputingcapacity (RACC)evaluationmodel,andanunderloaddetectionalgorithmbasedontheZ-scoretechniques.Minimum DataTransfer(MDT)isaVMselectionalgorithmbasedondynamicprogramming.ThelastoneisaVM placementalgorithm,anextendedversionofPABFDtakingintoaccountthePPRofheterogeneoushosts. (Li et al, 2020) introduced an algorithm based on the Host State 3rd-order Markov Chain (HS3MC)modelthatissplitintothreealgorithms.ThefirstalgorithmisaVMselectionalgorithm takingintoaccounttheminimumCPUutilization,theminimummemoryutilization,andtheminimum numberofmigrations.ThesecondalgorithmisanextendedversionofPABFD,whilethelastoneis forVMconsolidationbasedonanimprovedversionofHS3MC.…”

Section: Bin Packing Heuristicsmentioning

confidence: 99%

Server Consolidation Algorithms for Cloud Computing

Mikram

Kafhali

Saadi

2022

International Journal of Cloud Applications and Computing

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In recent years, companies and researchers have hosted and rented computer resources over ‎the ‎‎internet due to cloud computing, which led to an increase in the energy consumed by ‎data centers. This ‎‎consumption is considered one of the world's highest, ‎which pushed many ‎researchers to propose ‎several techniques such as server ‎consolidation (SC) to solve the‎‏ ‏trade‏-‏off‏ ‏‏‎between energy saving and ‎quality of service ‎‎(QoS). SC requires maintaining service level ‎agreements (SLA) violations and ‎minimizing ‎the number of active physical machines (PMs). ‎Furthermore, to achieve this balance and ‎‎avoid ‎increasing hardware costs, the SC challenge targets ‎placing new virtual machines ‎‎(VMs) in ‎suitable PMs. This work explored the existing SC algorithms ‎that include ‎CloudSim as a simulator ‎environment and PlanetLab as a dataset. The authors compared ‎the well-known optimization methods ‎and extracted the weaknesses of the main three deployed ‎‎approaches involved in the consolidation ‎process: bin-packing model, metaheuristics, ‎and machine ‎learning-based solutions.‎

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Section: Bin Packing Heuristicsmentioning

confidence: 99%