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Author Chapman, Clovis ♦ Musolesi, Mirco ♦ Emmerich, Wolfgang ♦ Mascolo, Cecilia
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Computational Grid ♦ Predictive Resource Scheduling ♦ Usage Characteristic ♦ Future Cpu Resource Utilisation ♦ Compute Resource ♦ Considerable Progress ♦ Current Usage Pattern ♦ Workflow Tool ♦ Kalman Filter Theory ♦ Large Cpu Cluster ♦ Computa-tional Grid ♦ Predictive Grid ♦ Little Attention ♦ Account Current Load Indicator ♦ Many Com-putational Scientist ♦ Detailed Analysis ♦ Replicated Experiment ♦ Building Middleware
Abstract The integration of clusters of computers into computa-tional grids has recently gained the attention of many com-putational scientists. While considerable progress has been made in building middleware and workflow tools that facil-itate the sharing of compute resources, little attention has been paid to grid scheduling and load balancing techniques to reduce job waiting time. Based on a detailed analysis of usage characteristics of an existing grid that involves a large CPU cluster, we observe that grid scheduling de-cisions can be significantly improved if the characteristics of current usage patterns are understood and extrapolated into the future. The paper describes an architecture and an implementation for a predictive grid scheduling framework which relies on Kalman filter theory to predict future CPU resource utilisation. By way of replicated experiments we demonstrate that the prediction achieves a precision within 15-20 % of the utilisation later observed and can signifi-cantly improve scheduling quality, compared to approaches that only take into account current load indicators. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study