Load balancing techniques play a critically important role in developing high-performance cluster computing platforms. Existing load balancing approaches are concerned with the effective usage of CPU and memory resources. Due to imbalance in disk I/O resources under I/O-intensive workloads, the previous CPU- or memory-aware load balancing schemes suffer significant performance drop. To remedy this deficiency, in this paper we propose a novel loadbalancing algorithm (hereinafter referred to as IOLB) for clusters, which aims at maintaining high resource utilization under a wide range of workload conditions. Specifically, IOLB is conducive to reducing the average slowdown of all parallel jobs submitted to a cluster by balancing load in disk resources. This can, in turn, not only achieve the effective usage of global disk resources but also reduce response times of I/O-intensive parallel jobs. To theoretically study the optimization of the IOLB algorithm, we qualitatively comparing IOLB w...