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Load-balanced and locality-aware scheduling for data-intensive workloads at extreme scales.

, , , , , , and . Concurrency and Computation: Practice and Experience, 28 (1): 70-94 (2016)

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Toward high-performance key-value stores through GPU encoding and locality-aware encoding., , , , , and . J. Parallel Distrib. Comput., (2016)High-Performance Storage Support for Scientific Applications on the Cloud., , , , , and . ScienceCloud@HPDC, page 33-36. ACM, (2015)Overcoming Hadoop Scaling Limitations through Distributed Task Execution., , , , , , , , and . CLUSTER, page 236-245. IEEE Computer Society, (2015)Albatross: An efficient cloud-enabled task scheduling and execution framework using distributed message queues., , , , , and . eScience, page 11-20. IEEE Computer Society, (2016)MHT: A light-weight scalable zero-hop MPI enabled distributed key-value store., , , , , and . BigData, page 2901-2903. IEEE Computer Society, (2015)FaBRiQ: Leveraging Distributed Hash Tables towards Distributed Publish-Subscribe Message Queues., , , , , , and . BDC, page 11-20. IEEE Computer Society, (2015)Load-balanced and locality-aware scheduling for data-intensive workloads at extreme scales., , , , , , and . Concurrency and Computation: Practice and Experience, 28 (1): 70-94 (2016)A flexible QoS fortified distributed key-value storage system for the cloud., , , , , , and . BigData, page 515-522. IEEE Computer Society, (2015)Towards In-Order and Exactly-Once Delivery Using Hierarchical Distributed Message Queues., , , and . CCGRID, page 883-892. IEEE Computer Society, (2014)A convergence of key-value storage systems from clouds to supercomputers., , , , , , and . Concurrency and Computation: Practice and Experience, 28 (1): 44-69 (2016)