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Storage"],"published-print":{"date-parts":[[2009,11]]},"abstract":"<jats:p>Load balancing for clusters has been investigated extensively, mainly focusing on the effective usage of global CPU and memory resources. However, previous CPU- or memory-centric load balancing schemes suffer significant performance drop under I\/O-intensive workloads due to the imbalance of I\/O load. To solve this problem, we propose two simple yet effective I\/O-aware load-balancing schemes for two types of clusters: (1) homogeneous clusters where nodes are identical and (2) heterogeneous clusters, which are comprised of a variety of nodes with different performance characteristics in computing power, memory capacity, and disk speed. In addition to assigning I\/O-intensive sequential and parallel jobs to nodes with light I\/O loads, the proposed schemes judiciously take into account both CPU and memory load sharing in the system. Therefore, our schemes are able to maintain high performance for a wide spectrum of workloads. We develop analytic models to study mean slowdowns, task arrival, and transfer processes in system levels. Using a set of real I\/O-intensive parallel applications and synthetic parallel jobs with various I\/O characteristics, we show that our proposed schemes consistently improve the performance over existing non-I\/O-aware load-balancing schemes, including CPU- and Memory-aware schemes and a PBS-like batch scheduler for parallel and sequential jobs, for a diverse set of workload conditions. Importantly, this performance improvement becomes much more pronounced when the applications are I\/O-intensive. For example, the proposed approaches deliver 23.6--88.0 % performance improvements for I\/O-intensive applications such as LU decomposition, Sparse Cholesky, Titan, Parallel text searching, and Data Mining. 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