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Optimizing task allocation in multi-query edge analytics

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Abstract

Edge analytics receives an ever-increasing interest since processing streaming data closer to where they are produced, rather than transferring them to the cloud, ensures lower latency while also addresses data privacy issues. In this work, we deal with the placement of analytic tasks to heterogeneous geo-distributed edge devices while targeting three objectives, namely latency, quality of results, and resource utilization. In addition, we investigate this multi-objective problem in a multi-query setting, where we jointly optimize multiple analytic jobs while dynamically adjusting task placement decisions. We explore multiple solutions that we thoroughly evaluate; interestingly, in a multi-query setting, all three objectives can be improved simultaneously by our proposals in many cases. Furthermore, we develop a proof-of-concept prototype using Apache Storm. Our solutions are thoroughly evaluated and shown to yield improvements by more than 50% compared to advanced baselines targeting only latency. Moreover, our software prototype managed to achieve speedups of up to 6\(\times\) over the Resource Aware Apache Storm scheduler, with an average speedup of 2.76\(\times\), when deployed over a small-scale infrastructure.

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Data availability

The scripts to generate the datasets analysed during the current study are publicly available in the following Github repository: https://github.com/annavalentina/Multi-Query-Optimization.

Notes

  1. In the remainder, we refer to edge computing as covering both edge and fog devices although in general fog computing may be deemed as distinct from edge computing [3].

  2. It is trivial to extend our notation and solution to consider task types and the requirements to refer to task types rather than individual tasks; for simplicity and to reduce notation, we do not consider task types.

  3. Again, for simplicity, we assume that tuples in all queries are of the same length; it is straightforward to account for variable-length tuples across different task outputs.

  4. https://storm.apache.org/

  5. https://flink.apache.org/

  6. https://github.com/apache/incubator-heron.

  7. https://github.com/thombashi/tcconfig.

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Acknowledgements

The research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant (Project Number:1052, Project Name: DataflowOpt).

Funding

The research work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant (Project Number:1052, Project Name: DataflowOpt).

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  1. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Anna-Valentini Michailidou, Christos Bellas & Anastasios Gounaris

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  1. Anna-Valentini Michailidou
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Contributions

A-V.M conducted the main research, which was supervised by A.G. The system implementation was performed by A-V.M and C.B. A-V.M and A.G both contributed to the writing. All authors reviewed the manuscript.

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Correspondence to Anna-Valentini Michailidou, Christos Bellas or Anastasios Gounaris.

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Michailidou, AV., Bellas, C. & Gounaris, A. Optimizing task allocation in multi-query edge analytics. Cluster Comput 27, 8289–8306 (2024). https://doi.org/10.1007/s10586-024-04427-1

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