Performance Evaluation in Grid Computing: A Modeling and Prediction Perspective

Experimental performance studies on computer systems, including Grids, require deep understandings on their workload characteristics. The need arises from two important and closely related topics in performance evaluation, namely, workload modeling and performance prediction. Both topics rely heavily on the representative workload data and have their arsenal from statistics and machine learning. Nevertheless, their goals and the nature of research differ considerably. Workload modeling aims at building mathematical models to generate workloads that can be used in simulation-based performance evaluation studies. It should statistically resemble the original real-world data therefore marginal statistics and second-order properties such as autocorrelation and power spectrum are important matching criteria. Performance prediction, on the other hand, intends to provide realtime forecast of important performance metrics (such as application run time and queue wait time) which can support Gr...

• Type: Conference
• Year: 2007
• Where: CCGRID
• Authors: Hui Li
• Keywords: CCGRID 2007, Cluster Computing, Performance, Workload, Workload Modeling