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- W2941010147 abstract "Abstract Recently, there have been several proposals in the area of geo-distributed big data processing. In this work, we aim to address a limitation of the existing solutions, namely to optimize task allocation across geographically distributed data centers, in a way that both the total traffic and the running time of the whole processing in complex multi-stage flows are targeted. Apart from proposing concrete efficient solutions for this combinatorial problem, we advocate to take a critical stand on the broadly spread claim that transferring distributed data to a single or fewer places is too costly. In our proposal, we judiciously reduce the participation of some data centers in the flow execution, and we show that, in a wide range of settings, this yields significant benefits. We show that a stochastic solution is superior to a fast greedy one, at the expense of optimization time of up to a few minutes. Compared to a state-of-the-art solution, we manage to decrease total traffic by 44% and running time by 37% on average. In several cases, the improvements can reach 1-2 orders of magnitude. Moreover, we provide evidence that simple heuristics are inferior. Our experimental evaluation comprises both extensive simulations and real runs in Spark." @default.
- W2941010147 created "2019-05-03" @default.
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- W2941010147 date "2019-07-01" @default.
- W2941010147 modified "2023-09-24" @default.
- W2941010147 title "Bi-objective Traffic Optimization in Geo-distributed Data Flows" @default.
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- W2941010147 doi "https://doi.org/10.1016/j.bdr.2019.04.002" @default.
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