Cooperative mobile edge computing‐cloud computing in Internet of vehicle: Architecture and energy‐efficient workload allocation. Issue 8 (5th August 2020)

• Record Type: Journal Article
• Authors: Gu, Xiaohui; Zhang, Guoan; Cao, Yujie
• Is Part Of: Transactions on emerging telecommunications technologies. Volume 32:Issue 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-05
• Subjects: Cloud computing -- Energy‐latency tradeoff -- Internet of vehicles -- Mobile edge computing -- Workload allocation; Telecommunication -- Periodicals; 384.05
• DOI: 10.1002/ett.4095
• Languages: English
• ISSNs: 2161-5748

Abstract

Abstract: With the increasing number of vehicles, the generating vehicular data exceeds the capacity of mobile edge computing (MEC). Therefore, studying the interaction and collaboration of edge computing and cloud computing is of significance to provide vehicular users with low‐latency high‐rate services. This paper first proposes a MEC‐cloud computing collaboration architecture for Internet of vehicles, then designs the interconnection/interaction framework between MEC and cloud computing. We consider reducing computation delay and power consumption, and formulate an energy‐efficient workload allocation problem with load balancing and dynamic voltage frequency scaling technology, to obtain the optimal workload allocations of MEC and cloud computing. We then present the overall distribution optimization algorithm to solve this problem. The simulation and numerical results show that by saving communication bandwidth and reducing transmission delay, MEC significantly enhances the performance of cloud computing. Besides, the proposed workload balance scheme is better than the benchmark schemes in terms of power consumption and latency. Abstract : We propose a MEC‐cloud computing collaboration architecture for Internet of Vehicle (IoV), then design the interconnection/interaction framework between MEC and cloud computing. Considering reducing computation delay and power consumption, we formulate an energy‐efficient workload allocation problem and provide the overall distribution optimization algorithm, to obtain the optimal workload allocation scheme of MEC and cloud computing. The numerical results demonstrate the behavior of the proposed scheme, and verify its energy efficiency.