KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Permanent link to this recordhttp://hdl.handle.net/10754/626112
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AbstractMany data center applications nowadays rely on distributed computation models like MapReduce and Bulk Synchronous Parallel (BSP) for data-intensive computation at scale . These models scale by leveraging the partition/aggregate pattern where data and computations are distributed across many worker servers, each performing part of the computation. A communication phase is needed each time workers need to synchronize the computation and, at last, to produce the final output. In these applications, the network communication costs can be one of the dominant scalability bottlenecks especially in case of multi-stage or iterative computations .
CitationSapio A, Abdelaziz I, Canini M, Kalnis P (2017) DAIET. Proceedings of the 2017 Symposium on Cloud Computing - SoCC ’17. Available: http://dx.doi.org/10.1145/3127479.3132018.
Conference/Event name2017 Symposium on Cloud Computing, SoCC 2017