Managing SLAs of heterogeneous workloads using dynamic application placement

Abstract

In this paper we address the problem of managing heterogeneous workloads in a virtuafized data center. We consider two different workloads: transactional applications and long-running jobs. We present a technique that permits collocation of these workload types on the same physical hardware. Our technique dynamically modifies workload placement by leveraging control mechanisms such as suspension and migration, and strives to optimally trade off resource allocation among these workloads in spite of their differing characteristics and performance objectives. Our approach builds upon our previous work on dynamically placing transactional workloads. This paper extends our framework with the capability to manage long-running workloads. We achieve this goal by using utility functions, which permit us to compare the performance of various workloads, and which are used to drive allocation decisions. We demonstrate that our technique maximizes heterogeneous workload performance while providing service differentiation based on high-level performance goals.