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Planner: Cost-efficient Execution Plans Placement for Uniform Stream Analytics on Edge and Cloud

Abstract : Stream processing applications handle unbounded and continuous flows of data items which are generated from multiple geographically distributed sources. Two approaches are commonly used for processing: Cloud-based analytics and Edge analytics. The first one routes the whole data set to the Cloud, incurring significant costs and late results from the high latency networks that are traversed. The latter can give timely results but forces users to manually define which part of the computation should be executed on Edge and to interconnect it with the remaining part executed in the Cloud, leading to sub-optimal placements. In this paper, we introduce Planner, a middleware for uniform and transparent stream processing across Edge and Cloud. Planner automatically selects which parts of the execution graph will be executed at the Edge in order to minimize the network cost. Real-world micro-benchmarks show that Planner reduces the network usage by 40% and the makespan (end-to-end processing time) by 15% compared to state-of-the-art.
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Contributor : Gabriel Antoniu <>
Submitted on : Wednesday, October 10, 2018 - 6:44:05 PM
Last modification on : Monday, May 4, 2020 - 11:39:28 AM


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  • HAL Id : hal-01892718, version 1


Laurent Prosperi, Alexandru Costan, Pedro Silva, Gabriel Antoniu. Planner: Cost-efficient Execution Plans Placement for Uniform Stream Analytics on Edge and Cloud. WORKS 2018: 13th Workflows in Support of Large-Scale Science Workshop, held in conjunction with the IEEE/ACM SC18 conference, Nov 2018, Dallas, United States. pp.1-10. ⟨hal-01892718⟩



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