Joint Task Partition and Resource Allocation for Multiuser Cooperative Mobile Edge Computing

• Published in: Wireless communications and mobile computing Vol. 2022; no. 1
• Main Authors: Xie, Gang, Wang, Zhenzhen, Liu, Yuanan
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 2022; John Wiley & Sons, Inc
• Subjects: Communication; Computation offloading; Cooperation; Edge computing; Energy consumption; Frequency division multiple access; Mobile computing; Network latency; Optimization; Orthogonal Frequency Division Multiplexing; Random variables; Remote computing; Resource allocation; Servers
• ISSN: 1530-8669; 1530-8677; 1530-8677
• DOI: 10.1155/2022/5143640

Exploiting the idle computation resources distributed at wireless devices (WDs) can enhance the mobile edge computing (MEC) computation performance. This paper studies a multiuser cooperative computing system consisting of one local user and multiple helpers, in which the user solicits multiple nearby WDs acting as helpers for cooperative computing. We design an efficient orthogonal frequency-division multiple access- (OFDMA-) aided three-phase transmission protocol, under which the user’s computation-intensive tasks can be executed in parallel by local computing and offloading. Under this setup, we study the energy consumption minimization problem by optimizing the user’s task partition, jointly with the communication and computation resources allocation for task offloading and results downloading, subject to the user’s computation latency constraint. For the nonconvex problem, we first transform the original problem into a convex one and then use the Lagrange duality method to obtain the globally optimal solution. Compared with other benchmark schemes, numerical results validate the effectiveness of the proposed joint task partition and resource allocation (JTPRA) scheme.