Energy-Efficient Edge-Facilitated Wireless Collaborative Computing using Map-Reduce

Paris, Antoine;Mirghasemi, Seyed Hamed;Stupia, Ivan;Vandendorpe, Luc
(2019) 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) — Location: Cannes, France (2.July.2019)

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Abstract—In this work, a heterogeneous set of wireless devices sharing a common access point collaborates to perform a set of tasks. Using the Map-Reduce distributed computing framework, the tasks are optimally distributed amongst the nodes with the objective of minimizing the total energy consumption of the nodes while satisfying a latency constraint. The derived optimal collaborative-computing scheme takes into account both the computing capabilities of the nodes and the strength of their communication links. Numerical simulations illustrate the benefits of the proposed optimal collaborative-computing scheme over a blind collaborative-computing scheme and the non-collaborative scenario, both in term of energy savings and achievable latency. The proposed optimal scheme also exhibits the interesting feature of allowing to trade energy for latency, and vice versa.
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Paris, A., Mirghasemi, S. H., Stupia, I., & Vandendorpe, L. (2019). Energy-Efficient Edge-Facilitated Wireless Collaborative Computing using Map-Reduce. 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). Published. 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Cannes, France. https://doi.org/10.1109/spawc.2019.8815499