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Dynamic Load Balancing for I/O-Intensive Tasks on Heterogeneous Clusters

Xiao Qin, Hong Jiang, Yifeng Zhu, and David R. Swanson

Department of Computer Science and Engineering
University of Nebraska-Lincoln
Lincoln, NE 68588-0115, {xqin, jiang, yzhu, dswanson}@cse.unl.edu 

Since I/O-intensive tasks running on a heterogeneous cluster need a highly effective usage of global I/O resources,
previous CPU- or memory-centric load balancing schemes suffer significant performance drop under I/O-intensive workload due to the imbalance of I/O load. To solve this problem, we develop two I/O-aware load-balancing schemes, which consider system heterogeneity and migrate more I/O-intensive tasks from a node with high I/O utilization to those with low I/O utilization. If the workload is memory-intensive in nature, the new method applies a memory-based load balancing policy to assign the tasks. Likewise, when the workload becomes CPU-intensive, our scheme leverages a CPU-based policy as an efficient means to balance the system load. In doing so, the proposed approach maintains the same level of performance as the existing schemes when I/O load is low or well balanced. Results from a trace-driven simulation study show that, when a workload is I/O-intensive, the proposed schemes improve the performance with respect to mean slowdown over the existing schemes by up to a factor of 8. In addition, the slowdowns of almost all the policies increase consistently with the system heterogeneity.

in the Proceedings of the 10th International Conference on High Performance Computing (HiPC 2003), pp.300-309, Hyderabad, India, December 17-20, 2003.