The cloud data centres evolved with an issue of energy management due to the constant increase in size,complexity and enormous consumption of energy.Energy management is a challenging issue that is critical in cloud d...The cloud data centres evolved with an issue of energy management due to the constant increase in size,complexity and enormous consumption of energy.Energy management is a challenging issue that is critical in cloud data centres and an important concern of research for many researchers.In this paper,we proposed a cuckoo search(CS)-based optimisation technique for the virtual machine(VM)selection and a novel placement algorithm considering the different constraints.The energy consumption model and the simulation model have been implemented for the efficient selection of VM.The proposed model CSOA-VM not only lessens the violations at the service level agreement(SLA)level but also minimises the VM migrations.The proposed model also saves energy and the performance analysis shows that energy consumption obtained is 1.35 kWh,SLA violation is 9.2 and VM migration is about 268.Thus,there is an improvement in energy consumption of about 1.8%and a 2.1%improvement(reduction)in violations of SLA in comparison to existing techniques.展开更多
Virtual machine(VM)consolidation aims to run VMs on the least number of physical machines(PMs).The optimal consolidation significantly reduces energy consumption(EC),quality of service(QoS)in applications,and resource...Virtual machine(VM)consolidation aims to run VMs on the least number of physical machines(PMs).The optimal consolidation significantly reduces energy consumption(EC),quality of service(QoS)in applications,and resource utilization.This paper proposes a prediction-basedmulti-objective VMconsolidation approach to search for the best mapping between VMs and PMs with good timeliness and practical value.We use a hybrid model based on Auto-Regressive Integrated Moving Average(ARIMA)and Support Vector Regression(SVR)(HPAS)as a prediction model and consolidate VMs to PMs based on prediction results by HPAS,aiming at minimizing the total EC,performance degradation(PD),migration cost(MC)and resource wastage(RW)simultaneously.Experimental results usingMicrosoft Azure trace show the proposed approach has better prediction accuracy and overcomes the multi-objective consolidation approach without prediction(i.e.,Non-dominated sorting genetic algorithm 2,Nsga2)and the renowned Overload Host Detection(OHD)approaches without prediction,such as Linear Regression(LR),Median Absolute Deviation(MAD)and Inter-Quartile Range(IQR).展开更多
文摘The cloud data centres evolved with an issue of energy management due to the constant increase in size,complexity and enormous consumption of energy.Energy management is a challenging issue that is critical in cloud data centres and an important concern of research for many researchers.In this paper,we proposed a cuckoo search(CS)-based optimisation technique for the virtual machine(VM)selection and a novel placement algorithm considering the different constraints.The energy consumption model and the simulation model have been implemented for the efficient selection of VM.The proposed model CSOA-VM not only lessens the violations at the service level agreement(SLA)level but also minimises the VM migrations.The proposed model also saves energy and the performance analysis shows that energy consumption obtained is 1.35 kWh,SLA violation is 9.2 and VM migration is about 268.Thus,there is an improvement in energy consumption of about 1.8%and a 2.1%improvement(reduction)in violations of SLA in comparison to existing techniques.
基金funded by Science and Technology Department of Shaanxi Province,Grant Numbers:2019GY-020 and 2024JC-YBQN-0730.
文摘Virtual machine(VM)consolidation aims to run VMs on the least number of physical machines(PMs).The optimal consolidation significantly reduces energy consumption(EC),quality of service(QoS)in applications,and resource utilization.This paper proposes a prediction-basedmulti-objective VMconsolidation approach to search for the best mapping between VMs and PMs with good timeliness and practical value.We use a hybrid model based on Auto-Regressive Integrated Moving Average(ARIMA)and Support Vector Regression(SVR)(HPAS)as a prediction model and consolidate VMs to PMs based on prediction results by HPAS,aiming at minimizing the total EC,performance degradation(PD),migration cost(MC)and resource wastage(RW)simultaneously.Experimental results usingMicrosoft Azure trace show the proposed approach has better prediction accuracy and overcomes the multi-objective consolidation approach without prediction(i.e.,Non-dominated sorting genetic algorithm 2,Nsga2)and the renowned Overload Host Detection(OHD)approaches without prediction,such as Linear Regression(LR),Median Absolute Deviation(MAD)and Inter-Quartile Range(IQR).
