Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling

TLDR

Dynamic voltage scaling enhances flexibility in mobile edge computing by improving computation offloading. The study aims to jointly optimize mobile device speed, transmit power, and offloading ratio to minimize energy consumption and latency. The authors formulate energy and latency minimization as nonconvex optimization problems, solve the energy problem via convex reformulation and the latency problem with a locally optimal univariate search, and extend the analysis to multiple cloud servers with closed‑form optimal offloading distributions. Simulations confirm that the proposed algorithms markedly lower energy use and execution latency versus prior offloading methods.

Abstract

The incorporation of dynamic voltage scaling technology into computation offloading offers more flexibilities for mobile edge computing. In this paper, we investigate partial computation offloading by jointly optimizing the computational speed of smart mobile device (SMD), transmit power of SMD, and offloading ratio with two system design objectives: energy consumption of SMD minimization (ECM) and latency of application execution minimization (LM). Considering the case that the SMD is served by a single cloud server, we formulate both the ECM problem and the LM problem as nonconvex problems. To tackle the ECM problem, we recast it as a convex one with the variable substitution technique and obtain its optimal solution. To address the nonconvex and nonsmooth LM problem, we propose a locally optimal algorithm with the univariate search technique. Furthermore, we extend the scenario to a multiple cloud servers system, where the SMD could offload its computation to a set of cloud servers. In this scenario, we obtain the optimal computation distribution among cloud servers in closed form for the ECM and LM problems. Finally, extensive simulations demonstrate that our proposed algorithms can significantly reduce the energy consumption and shorten the latency with respect to the existing offloading schemes.