An Efficient Caching and Offloading Resource Allocation Strategy in Vehicular Social Networks

• Published in: IEEE transactions on vehicular technology Vol. 73; no. 4; pp. 5690 - 5703
• Main Authors: Zhang, Yuexia, Zhou, Ying, Zhang, Siyu, Gui, Guan, Adebisi, Bamidele, Gacanin, Haris, Sari, d Hikmet
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-04-2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Caching; Collaboration; Computation offloading; Cuckoo algorithm; Delay; Delays; Edge computing; Evolutionary computation; Genetic algorithms; Load management; Network latency; Optimization; Particle swarm optimization; Resource allocation; Resource management; Search algorithms; Servers; Social networking (online); Social networks; Task analysis; task offloading; Traffic volume; Vehicles; vehicular social networks
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2023.3332905

Limited edge server resources and uneven distribution of traffic density in vehicular networks result in problems such as unbalanced network load and high task processing latency. To address these issues, we proposed an efficient caching and offloading resource allocation (ECORA) strategy in vehicular social networks. First, to improve the utilization of vehicular idle resources, a collaborative computation and storage resource allocation mechanism was designed using mobile social similarity. Next, with the optimization objective of minimizing the average task processing delay, we studied the combined resource allocation optimization problem and decoupled it into two sub-problems. For the service caching subproblem, we designed a stable matching algorithm by mobile social connections to dynamically update the cache resource allocation scheme for improving the task unloading efficiency. For the task offloading subproblem, a discrete cuckoo search algorithm based on differential evolution was designed to adaptively select the best task offloading scheme, which minimized the average task processing delay. Simulation results revealed that the ECORA strategy outperformed the resource allocation strategy based on particle swarm optimization and genetic algorithm, and reduced the average task processing delay by at least 7.59%. Meanwhile, the ECORA strategy can achieve superior network load balancing.