Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks

• Published in: IEEE transactions on vehicular technology Vol. 68; no. 8; pp. 7944 - 7956
• Main Authors: Zhao, Junhui, Li, Qiuping, Gong, Yi, Zhang, Ke
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ad hoc networks; Algorithms; Automobiles; Cloud computing; Collaboration; Collaborative computation offloading; Computation offloading; computation resource allocation; Computer simulation; Delays; Design optimization; Edge computing; Mobile computing; mobile edge computing; Optimization; Resource allocation; Resource management; Servers; Task analysis; Vehicles; vehicular networks
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2019.2917890

Computation offloading services provide required computing resources for vehicles with computation-intensive tasks. Past computation offloading research mainly focused on mobile edge computing (MEC) or cloud computing, separately. This paper presents a collaborative approach based on MEC and cloud computing that offloads services to automobiles in vehicular networks. A cloud-MEC collaborative computation offloading problem is formulated through jointly optimizing computation offloading decision and computation resource allocation. Since the problem is non-convex and NP-hard, we propose a collaborative computation offloading and resource allocation optimization (CCORAO) scheme, and design a distributed computation offloading and resource allocation algorithm for CCORAO scheme that achieves the optimal solution. The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.