vmBBProfiler: a black-box profiling approach to quantify sensitivity of virtual machines to shared cloud resources

Abstract

Virtualized Data Centers are packed with numerous web and cloud services nowadays. In such large infrastructures, providing reliable service platforms depends heavily on efficient sharing of physical machines (PMs) by virtual machines (VMs). To achieve efficient consolidation, performance degradation of co-located VMs must be correctly understood, modeled, and predicted. This work is a major step toward understanding such baffling phenomena by not only identifying, but also quantifying sensitivity of general purpose VMs to their demanded resources. vmBBProfiler, our proposed system in this work, is able to systematically profile behavior of any general purpose VM and calculate its sensitivity to system provided resources such as CPU, Memory, and Disk. vmBBProfiler is evaluated using 12 well-known benchmarks, varying from pure CPU/Mem/Disk VMs to mixtures of them, on three different PMs in our VMware-vSphere based private cloud. Extensive empirical results conducted over 1200 h of profiling prove the efficiency of our proposed models and solutions; it also opens doors for further research in this area.