Energy-Saving Offloading by Jointly Allocating Radio and Computational Resources for Mobile Edge Computing

• Published in: IEEE access Vol. 5; pp. 11255 - 11268
• Main Authors: Zhao, Pengtao, Tian, Hui, Qin, Cheng, Nie, Gaofeng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: branch-and-bound method; Cloud computing; Complexity; Computation offloading; Computational modeling; Edge computing; Electronic devices; Energy conservation; Energy consumption; energy minimization; Gini coefficient; Minimization; Mobile communication; Mobile computing; Mobile edge computing; Network latency; Nonlinear programming; Optimization; Polynomials; Radio; Radio spectrum management; reformulation-linearization-technique; Resource allocation; Resource management; Servers
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2710056

Mobile edge computing (MEC) providing information technology and cloud-computing capabilities within the radio access network is an emerging technique in fifth-generation networks. MEC can extend the computational capacity of smart mobile devices (SMDs) and economize SMDs' energy consumption by migrating the computation-intensive task to the MEC server. In this paper, we consider a multi-mobile-users MEC system, where multiple SMDs ask for computation offloading to a MEC server. In order to minimize the energy consumption on SMDs, we jointly optimize the offloading selection, radio resource allocation, and computational resource allocation coordinately. We formulate the energy consumption minimization problem as a mixed interger nonlinear programming (MINLP) problem, which is subject to specific application latency constraints. In order to solve the problem, we propose a reformulation-linearization-technique-based Branch-and-Bound (RLTBB) method, which can obtain the optimal result or a suboptimal result by setting the solving accuracy. Considering the complexity of RTLBB cannot be guaranteed, we further design a Gini coefficient-based greedy heuristic (GCGH) to solve the MINLP problem in polynomial complexity by degrading the MINLP problem into the convex problem. Many simulation results demonstrate the energy saving enhancements of RLTBB and GCGH.