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Poor I/O performance can prevent an application from scaling to a large number of nodes even if the computation is parallelized appropriately. Therefore, improving I/O performance of large-scale parallel applications is very important. Caching recently and frequently accessed I/O blocks in memory is a widely used technique for improving I/O performance of these applications on high-end machines. However, simultaneous storage cache accesses of multiple applications may lead to unacceptable degradations in application performance due to interferences at the storage cache layer. As a result, efficient management of storage cache space across multiple I/O servers among competing applications is critical in order to ensure performance quality of service (QoS) to individual applications. In this paper, we propose a novel two-step approach to the management of the storage caches to provide predictable performance in multi-server storage architectures: (1)An adaptive QoS decomposition and optimization step uses max-flow algorithm to determine the best decomposition of application-level QoS to sub-QoSs such that the application performance is optimized, and (2) A storage cache allocation step uses feedback control theory to allocates hared storage cache space such that the specified QoSs are satisfied throughout the execution. Our experimental evaluation indicates that, on an average, our approach improves the I/O throughput of applications by 48.6%, 29.2%, and 20.7%, respectively, over the uncontrolled partitioning, fair share and uniform decomposition schemes. We also observed 31.4%, 20.2%, and 44.7% improvements by our approach, in our global metric, called the fair speedup metric, against the fair share, uncontrolled partitioning and uniform decomposition schemes, respectively.