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• W3213046182 abstract "Distributed computing enables <i>parallel</i> execution of smaller tasks that make up a large computing job. Its purpose is to reduce the job completion time. However, random fluctuations in task service times lead to straggling tasks with long execution times. Redundancy provides <i>diversity</i> that allows job completion when only a subset of redundant tasks is executed, thus removing the dependency on the straggling tasks. Under constrained resources (here, a fixed number of parallel servers), increasing redundancy reduces the available resources for parallelism. In this paper, we characterize the <i>diversity vs. parallelism</i> trade-off and identify the optimal strategy among replication, coding, and splitting, which minimizes the expected job completion time. We consider three common service time distributions and establish three models that describe the scaling of these distributions with the task size. We find that different distributions with different scaling models operate optimally at different redundancy levels, thus requiring very different code rates." @default.
• W3213046182 created "2021-11-22" @default.
• W3213046182 creator A5034055788 @default.
• W3213046182 creator A5034577486 @default.
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• W3213046182 date "2022-02-01" @default.
• W3213046182 modified "2023-09-24" @default.
• W3213046182 title "Diversity/Parallelism Trade-Off in Distributed Systems With Redundancy" @default.
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• W3213046182 doi "https://doi.org/10.1109/tit.2021.3127920" @default.
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