Type
PresentationKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Extreme Computing Research Center
Date
2017-09-27Online Publication Date
2017-09-27Print Publication Date
2017Permanent link to this record
http://hdl.handle.net/10754/626112
Metadata
Show full item recordAbstract
Many data center applications nowadays rely on distributed computation models like MapReduce and Bulk Synchronous Parallel (BSP) for data-intensive computation at scale [4]. 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 [1].Citation
Sapio 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 name
2017 Symposium on Cloud Computing, SoCC 2017Additional Links
https://dl.acm.org/citation.cfm?doid=3127479.3132018ae974a485f413a2113503eed53cd6c53
10.1145/3127479.3132018