Shares and utilities based power consolidation in virtualized server environments

TLDR

Virtualization platforms such as VMware and Xen expose min‑max and shares mechanisms for resource allocation, yet prior VM placement and power‑consolidation studies rarely exploit these features. The paper proposes a suite of techniques that leverage min‑max and shares features for VM placement and power consolidation in data centers. The authors design a smooth power‑performance trade‑off mechanism that dynamically adjusts VM resources according to available capacity, power costs, and application utilities, and evaluate it on large synthetic and real VMware ESX testbeds. Experiments demonstrate that exploiting min‑max and shares features can raise data‑center utility by at least 47 % and that the PowerExpandMinMax algorithm consistently delivers the highest overall utility across diverse workloads, capacities, and power costs.

Abstract

Virtualization technologies like VMware and Xen provide features to specify the minimum and maximum amount of resources that can be allocated to a virtual machine (VM) and a shares based mechanism for the hypervisor to distribute spare resources among contending VMs. However much of the existing work on VM placement and power consolidation in data centers fails to take advantage of these features. One of our experiments on a real testbed shows that leveraging such features can improve the overall utility of the data center by 47% or even higher. Motivated by these, we present a novel suite of techniques for placement and power consolidation of VMs in data centers taking advantage of the min-max and shares features inherent in virtualization technologies. Our techniques provide a smooth mechanism for power-performance tradeoffs in modern data centers running heterogeneous applications, wherein the amount of resources allocated to a VM can be adjusted based on available resources, power costs, and application utilities. We evaluate our techniques on a range of large synthetic data center setups and a small real data center testbed comprising of VMware ESX servers. Our experiments confirm the end-to-end validity of our approach and demonstrate that our final candidate algorithm, PowerExpandMinMax, consistently yields the best overall utility across a broad spectrum of inputs - varying VM sizes and utilities, varying server capacities and varying power costs - thus providing a practical solution for administrators.