Resource Demand Misalignment: An Important Factor to Consider for Reducing Resource Over-Provisioning in Cloud Datacenters

Abstract

Previous resource provisioning strategies in cloud datacenters allocate physical resources to virtual machines (VMs) based on the predicted resource utilization pattern of VMs. The pattern for VMs of a job is usually derived from historical utilizations of multiple VMs of the job. We observed that these utilization curves are usually misaligned in time, which would lead to resource over-prediction and hence over-provisioning. Since this resource utilization misalignment problem has not been revealed and studied before, in this paper, we study the VM resource utilization from public datacenter traces and Hadoop benchmark jobs to verify the commonness of the utilization misalignments. Then, to reduce resource over-provisioning, we propose three VM resource utilization pattern refinement algorithms to improve the original generated pattern by lowering the cap of the pattern, reducing cap provision duration and varying the minimum value of the pattern. We then extend these algorithms to further improve the resource efficiency by considering periodical resource demand patterns that have multiple pulses in a pattern. These algorithms can be used in any resource provisioning strategy that considers predicted resource utilizations of VMs of a job. We then adopt these refinement algorithms in an initial VM allocation mechanism and test them in trace-driven experiments and real-world testbed experiments. The experimental results show that each improved mechanism can increase resource utilization, and reduce the number of PMs needed to satisfy tenant requests. Also, our extended refinement algorithms are effective in improving resource efficiency of the refinement algorithms.