Overhead-Aware-Best-Fit (OABF) Resource Allocation Algorithm for Minimizing VM Launching Overhead
Hao Wu, Gabriele Garzoglio, Shangping Ren, Steven C. Timm, Seo Young Noh · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2014
FermiCloud is a private cloud developed in Fermi National Accelerator Laboratory to provide elastic and on-demand resources for different scientific research experiments. The design goal of the FermiCloud is to automatically allocate resources for different scientific applications so that the QoS required by these applications is met and the operational cost of the FermiCloud is minimized. Our earlier research shows that VM launching overhead has large variations. If such variations are not taken into consideration when mak-ing resource allocation decisions, it may lead to poor perfor-mance and resource waste. In this paper, we show how we may use an VM launching overhead reference model to mini-mize VM launching overhead. In particular, we first present a training algorithm that automatically tunes a given refer-ence model to accurately reflect FermiCloud environment. Based on the tuned reference model for virtual machine launching overhead, we develop an overhead-aware-best-fit resource allocation algorithm that decides where and when ∗Hao Wu works as an intern in Fermi National Accelerator