Resource Allocation for Autonomic Data Centers using Analytic Performance Models

TLDR

Large data centers host multiple application environments with highly variable workloads, requiring dynamic server redeployment to optimize global utility, yet existing solutions struggle with scalability and multi‑class workload handling. The paper proposes a solution to overcome these scalability and multi‑class workload challenges. The approach uses analytic queuing network models coupled with combinatorial search, applied to both online and batch workloads. Simulation experiments confirm the effectiveness of the proposed method.

Abstract

Large data centers host several application environments (AEs) that are subject to workloads whose intensity varies widely and unpredictably. Therefore, the servers of the data center may need to be dynamically redeployed among the various AEs in order to optimize some global utility function. Previous approaches to solving this problem suffer from scalability limitations and cannot easily address the fact that there may be multiple classes of workloads executing on the same AE. This paper presents a solution that addresses these limitations. This solution is based on the use of analytic queuing network models combined with combinatorial search techniques. The paper demonstrates the effectiveness of the approach through simulation experiments. Both online and batch workloads are considered