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Energy-Efficient Mobile-Edge Computation Offloading for Applications with Shared Data by He, Xiangyu; Xing, Hong; Chen, Yue; Nallanathan, Arumugam is a scholarly article available to read on EtoBox.

What is Energy-Efficient Mobile-Edge Computation Offloading for Applications with Shared Data about?

Mobile-edge computation offloading (MECO) has been recognized as a promising solution to alleviate the burden of resource-limited Internet of Thing (IoT) devices by offloading computation tasks to the edge of cellular networks (also known as {\em cloudlet}). Specifically, latency-critical applications such as virtual reality (VR) and augmented reality (AR) have inherent collaborative properties since part of the input/output data are shared by different users in proximity. In this paper, we consider a multi-user fog computing system, in which multiple single-antenna mobile users running applications featuring shared data can choose between (partially) offloading their individual tasks to a nearby single-antenna cloudlet for remote execution and performing pure local computation. The mobile users' energy minimization is formulated as a convex problem, subject to the total computing latency constraint, the total energy constraints for individual data downloading, and the computing frequency constraints for local computing, for which classical Lagrangian duality can be applied to find the optimal solution. Based upon the semi-closed form solution, the shared data proves to be transmit

Author

He, Xiangyu; Xing, Hong; Chen, Yue; Nallanathan, Arumugam

Published

2018

Language

EN