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Towards Load Balancing Support for I/O-Intensive Parallel Jobs
in a Cluster of Workstations

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 

While previous CPU- or memory-centric load balancing schemes are capable of achieving the effective usage of global CPU and memory resources in a cluster system, the cluster exhibits significant performance drop under I/O-intensive workload conditions due to the imbalance of I/O load. To tackle this problem, we have developed two simple yet effective I/O-aware load-balancing schemes, which make it possible to balance I/O load by assigning I/O intensive sequential and parallel jobs to nodes with light I/O loads. Moreover, the proposed schemes judiciously take into account both CPU and memory load sharing in the cluster, thereby maintaining a high performance for a wide spectrum of workload. Using a set of real I/O-intensive parallel applications in addition to synthetic parallel jobs, we show that the proposed schemes consistently outperform the existing non-I/O-aware load-balancing schemes for a diverse set of workload conditions. Importantly, the performance improvement becomes much more pronounced when the applications are I/O-intensive.

in the Proceedings of the 5th IEEE International Conference on Cluster Computing (Cluster 2003), pp.100-107,
Hong Kong, Dec. 1-4, 2003.