Dynamic Tuning for Parameter-Based Virtual Machine Placement

Abstract

Virtual machine (VM) placement is the process that allocates virtual machines onto physical machines (PMs) in cloud data centers. Reservation-based VM placement allocates VMs to PMs according to a (statically) reserved VM size regardless of the actual workload. If, at some point in time, a VM is making use of only a fraction of its reservation this leads to PM underutilization, which wastes energy and, at a grand scale, it may result in financial and environmental costs. In contrast, demand-based VM placement consolidates VMs based on the actual workload's demand. This may lead to better utilization, but it may incur a higher number of Service Level Agreement Violations (SLAVs) resulting from overloaded PMs and/or VM migrations from one PM to another as a result of workload fluctuations. To control the tradeoff between utilization and the number of SLAVs, parameter-based VM placement can allow a provider, through a single parameter, to explore the whole space of VM placement options that range from demand-based to reservation-based. The idea investigated by this paper is to adjust this parameter continuously at run-time in a way that a provider can maintain the number of SLAVs below a certain (predetermined) threshold while using the smallest possible number of PMs for VM placement. Two dynamic algorithms to select a value of this parameter on-the-fly are proposed. Experiments conducted using CloudSim evaluate the performance of the two algorithms using one synthetic and one real workload.