Profiling Applications for Virtual Machine Placement in Clouds

Abstract

Application profiling is an important technique for efficient resource management. The decision making of scheduling and resource allocation typically takes great advantage of such a technique primarily for improving resource utilization. With the advent of cloud computing as a multitenant virtualized platform, diverse applications are increasingly deployed onto the cloud and they more than often share physical resources. The background load (other applications running on the same physical machine) is therefore an important factor for profiling an application in this cloud computing scenario. In this paper, we present a novel application profiling technique using the canonical correlation analysis (CCA) method, which identifies the relationship between application performance and resource usage. We further devise a performance prediction model based on application profiles generated using CCA. Clearly, our profiling technique with this prediction model has a lot of potentials particularly in virtual machine (VM) placement with performance awareness. Our experimental results demonstrate the capability of our profiling technique and the accuracy of our prediction model.