Effective VM sizing in virtualized data centers

In this paper, we undertake the problem of server consolidation in virtualized data centers from the perspective of approximation algorithms. We formulate server consolidation as a stochastic bin packing problem, where the server capacity and an allowed server overflow probability p are given, and the objective is to assign VMs to as few physical servers as possible, and the probability that the aggregated load of a physical server exceeds the server capacity is at most p. We propose a new VM sizing approach called effective sizing, which simplifies the stochastic optimization problem by associating a VM's dynamic load with a fixed demand. Effective sizing decides a VM's resource demand through statistical multiplexing principles, which consider various factors impacting the aggregated resource demand of a host where the VM may be placed. Based on effective sizing, we design a suite of polynomial time VM placement algorithms for both VM migration cost-oblivious and migration cost-aware scenarios. Through analysis, we show that our algorithm is O(1)- approximation for the stochastic bin packing problem when the VM loads can be modeled as all Poisson or all normal distributions. Through evaluations driven by a real data center load trace, we show that our consolidation solution can achieve an order of reduction on physical server requirement compared to that before consolidation; the consolidation result is only 24% more than the optimal solution. With effective sizing, our server consolidation solution achieves 10% to 23% more energy savings than state-of-the-art approaches.