Joint Task Assignment and Resource Allocation for D2D-Enabled Mobile-Edge Computing

• Published in: IEEE transactions on communications Vol. 67; no. 6; pp. 4193 - 4207
• Main Authors: Xing, Hong, Liu, Liang, Xu, Jie, Nallanathan, Arumugam
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Benchmarks; Cloud computing; computation offloading; Edge computing; Energy conservation; fog computing; Mobile computing; Mobile-edge computing (MEC); OFDM; Optimization; Protocols; Resource allocation; Resource management; Servers; Task analysis; task assignment; Time Division Multiple Access; Wireless communication; Wireless networks
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2019.2903088

With the proliferation of computation-extensive and latency-critical applications in the 5G and beyond networks, mobile-edge computing (MEC) or fog computing, which provides cloud-like computation and/or storage capabilities at the network edge, is envisioned to reduce computation latency as well as to conserve energy for wireless devices (WDs). This paper studies a novel device-to-device (D2D)-enabled multi-helper MEC system, in which a local user solicits its nearby WDs serving as helpers for cooperative computation. We assume a time division multiple access (TDMA) transmission protocol, under which the local user offloads the tasks to multiple helpers and downloads the results from them over orthogonal pre-scheduled time slots. Under this setup, we minimize the computation latency by optimizing the local user's task assignment jointly with the time and rate for task offloading and results downloading, as well as the computation frequency for task execution, subject to individual energy and computation capacity constraints at the local user and the helpers. However, the formulated problem is a mixed-integer non-linear program (MINLP) that is difficult to solve. To tackle this challenge, we propose an efficient algorithm by first relaxing the original problem into a convex one, and then constructing a suboptimal task assignment solution based on the obtained optimal one. Furthermore, we consider a benchmark scheme that endows the WDs with their maximum computation capacities. To further reduce the implementation complexity, we also develop a heuristic scheme based on the greedy task assignment. Finally, the numerical results validate the effectiveness of our proposed algorithm, as compared against the heuristic scheme and other benchmark ones without either joint optimization of radio and computation resources or task assignment design.