Performance impacts of autocorrelated flows in multi-tiered systems
Performance Evaluation
Long range dependent job arrival process and its implications in grid environments
Proceedings of the first international conference on Networks for grid applications
Model-based simulation and performance evaluation of grid scheduling strategies
Future Generation Computer Systems
Analysis of scheduling policies under correlated job sizes
Performance Evaluation
Future Generation Computer Systems
An up-to-date survey in web load balancing
World Wide Web
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Size-based policies have been shown to successfully balance load and improve performance in homogeneous cluster environments where a dispatcher assigns a job to a server strictly based on the job size. While the success of size-based policies is based on separating jobs to different servers according to their sizes by avoiding the unfavorable performance effects of having short jobs been stuck behind long jobs, we show that their effectiveness quickly deteriorates in the presence of job arrivals that are characterized by correlation in their dependence structure. We propose a new policy that still strives to separate jobs according to their sizes, but this separation is biased by the effort to reduce the performance loss due to autocorrelation. As a result, not all servers are equally utilized (i.e., the load in the system becomes unbalanced) but the performance benefits of this load unbalancing are significant. The proposed policy can be used on-line, i.e., it does not assume any knowledge neither of the correlation structure of the arrival stream, nor of the job size distribution in the system. Via detailed trace-driven simulation we quantify the performance benefits of the proposed policy and we show that it can effectively self adjust its configuration parameters to improve performance under continuously changing workload conditions.