Leveraging User-Diversity in Energy-Efficient Edge-Facilitated Wireless Collaborative-Computing

by   Antoine Paris, et al.

In this work, a heterogeneous set of wireless devices sharing a common access point (AP) or base station (BS) collaborates to complete a set of computing tasks within a given deadline in the most energy-efficient way. This pool of devices somehow acts like a distributed mobile edge computing (MEC) server to augment the computing capabilities of individual devices while reducing their total energy consumption. Using the Map-Reduce distributed computing framework – which involves both local computing at devices and communications between them – the tasks are optimally distributed amongst the nodes, taking into account their diversity in term of computing and communications capabilities. In addition to optimizing the computing load distribution, local parameters of the nodes such as CPU frequency and RF transmit power are also optimized for energy-efficiency. The corresponding optimization problem can be shown to be convex and optimality conditions offering insights into the structure of the solutions can be obtained through Lagrange duality theory. A waterfilling-like interpretation for the size of the computing task assigned to each node is given. Numerical experiments demonstrate the benefits of the proposed optimal collaborative-computing scheme over various other schemes in several respects. Most notably, the proposed scheme exhibits increased probability of successfully dealing with larger computing loads and/or smaller latency and energy-efficiency gains of up to two orders of magnitude. Both improvements come from the scheme ability to optimally leverage devices diversity.


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