Boosting Performance for I/O-Intensive Workload by
Preemptive Job Migrations in a Cluster System

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 

Load balancing in a cluster system has been investigated extensively, mainly focusing on the effective usage of global CPU and memory resources. However, if a significant portion of applications running in the system is I/O-intensive, traditional load balancing policies that focus on CPU and memory usage may cause the system performance to decrease substantially. To solve this problem, a new I/O-aware load-balancing scheme with preemptive job migration is presented to sustain the high performance of a cluster with a diverse set of workload conditions. The proposed scheme dynamically detects I/O load imbalance on nodes of a cluster, and determines whether to preempt some running jobs on overloaded nodes and migrate them to other less- or under-loaded nodes. Besides balancing I/O load, the scheme takes into account both CPU and memory load sharing in clusters, thereby maintaining the same level of performance as existing schemes when I/O load is low or well balanced. Results from a trace-driven simulation show that, compared to the existing approaches that only consider I/O with non-preemptive job migrations, the proposed schemes achieve the improvement in mean slowdown by up to a factor of 10.

In the Proceedings of the 15th Symposium on Computer Architecture and High Performance Computing, Nov.10-12, 2003, IEEE Press.

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