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• W2078565624 abstract "High performance clusters are being configured specially to give data centers that require extreme performance, the processing power they need. In Cluster Computing Environment the data latency time has significant impact on the performance, when the data is accessed across clusters. Instead of processing power, memory and I/O have become the new bottleneck in achieving efficient load balance at higher performance for cluster computer systems. Initial job placement and load balancing are the key aspects affecting the performance. In this research, data access patterns, memory and CPU utilization and locality of memory are combined to consider as load metric in the load balancing aspect across cluster. A scheduling algorithm based on this metric has been proposed to dynamically balance the load in the cluster. Initial job placement for a job in the cluster considers data access patterns and for load balance aspect metric constitutes CPU, memory utilization including locality of memory. Experimental results show performance improvement to considerable levels with the implementation of the concept, specifically when the cost of data access from other clusters is higher and is proportionate to the amount of data." @default.
• W2078565624 created "2016-06-24" @default.
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• W2078565624 date "2008-01-01" @default.
• W2078565624 modified "2023-09-25" @default.
• W2078565624 title "Effective Load Metric and Efficient Initial Job Placement for Dynamic Load Balancing in Cluster" @default.
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• W2078565624 doi "https://doi.org/10.3844/jcssp.2008.72.79" @default.
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