Research Of VM Consolidation Algorithm Based On Load Prediction In IaaS Cloud

Virtual machine consolidation(VMC)provides an effective way to improve energy efficiency and quality of service in IaaS Cloud Datacenter.Most researchers describe VMC as a packing problem.As a well-known NP-Hard problem,which usually use heuristic algorithms to solve.Failing to treat the load of virtual machine(VM)as a time series for analysis,the efficiency of current VMC is not good enough.On the one hand,the short-term load of VM often has obvious trend,time regression method can be used to analyze the increase trend of VM load and quantities the load increment.Then,this paper proposes a migration VM selection strategy named Load increment Prediction(LIP)based on load increment prediction.LIP strategy makes it more accurate to select the VM from the overloaded physical machine(PM)by combining the current load and load increment.On the other hand,if the VMs whose load series complementary each other are consolidated into the same PM,the smoothness of the PM load will be improved.Therefore,it is possible to reduce the SLA violation and VM Migration due to the resource competition of the PM.Based on this idea,this paper proposes a VM migration point selection strategy named Saturation Increase Rate(SIR)based on load sequence prediction.The VMC algorithm based on load prediction proposed in this paper is the organic combination of LIP algorithm and SIR algorithm.Finally,we evaluate our strategies and algorithm by simulating experiments in cloud computing simulator CloudSim using real load data.The experimental results show that LIP algorithm and SIR algorithm can effectively reduce the cost of VM migration and SLA violation due to the resources competition of PM.