Batch job profiling and adaptive profile enforcement for virtualized environments

Abstract

Data center management is driven by high-level performance goals, and it is the responsibility of a management middleware to ensure that those goals are met using dynamic resource allocation. The performance delivered by the heterogeneous set of applications running in a virtualized enterprise data center must be predicted to make resource allocation decisions. For some of these applications, it is required to produce accurate profiles based on previous executions: that is the case of batch jobs. In this paper we propose a methodology to produce resource consumption profiles for batch applications running inside of virtual machines and a technique to enforce and adapt the profiles to actual execution conditions and application performance. For this purpose we have developed a testing prototype. The enforcement technique observes the fact that management middleware usually run in control cycles in which the system can be reconfigured, what imposes a tradeoff between the accuracy of the profiles and their applicability in real deployments. The novel contribution of this work is the study of the tradeoff between accuracy and applicability of workload profiles, what is a necessary step to enable existing management middleware with the performance prediction mechanisms required to perform effective dynamic resource allocation.