Performance-Guided Load (Un)balancing under Autocorrelated Flows

Abstract

Size-based policies have been shown in the literature to effectively balance the load and improve performance in cluster environments. Size-based policies assign jobs to servers based on the job size and their performance improvements are an outcome of separating ;short; from ;long; jobs, by avoiding having short jobs waiting behind long jobs for service. In this paper, we present evidence that performance improvements due to this separation quickly vanish if the arrival process to the cluster is autocorrelated. Based on our observations, we devise a new size-based policy called D_EQAL that still strives to separate jobs to servers according to job size but this separation is now biased by an effort to reduce performance loss due to autocorrelation in the arrival flows to each server. As a result of this bias, all servers may not be equally utilized (i.e., the load in the system may be ;unbalanced;), but performance benefits become significant. D_EQAL can be used on-line as it does not assume any a priori knowledge of the incoming workload. Extensive simulations show the effectiveness of D_EQAL under autocorrelated and uncorrelated arrival streams and illustrate that the policy successfully self- adjusts the degree of load unbalancing based on monitored performance measures.