A Novel Multi-Factor Aware Online Scheduling Method for Improving Vehicular Edge Computing Efficiency

• Published in: IEEE International Conference on Communications (2003) pp. 3357 - 3362
• Main Authors: Qian, Lang, Sun, Peng, Yang, Kun, Boukerche, Azzedine, Song, Liang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.05.2023
• Subjects: deadline-aware tasks; fine-grained computing scheduling; multi-factor metric; Numerical simulation; Quality of service; Road safety; Road side unit; Scheduling; Scheduling algorithms; System performance; vehicular edge computing
• ISSN: 1938-1883
• DOI: 10.1109/ICC45041.2023.10279345

Vehicular Edge Computing (VEC), as one of the major components of Intelligent Transportation Systems, improves road safety by providing computing services to safety-related applications on vehicles. Currently, the existing fine-grained computing scheduling algorithms are normally designed based on some simple scheduling policies. Due to the heterogeneous nature of tasks offloaded from various applications, they may not effectively satisfy various performance requirements of the real system, thereby leading to the problem that the short-term residual computing power cannot be effectively utilized when computing-costly tasks occupy the server. Therefore, improving the overall system performance and the efficiency of utilizing computing power is a critical issue. Accordingly, in this paper, we study the problem of computing scheduling inside edge servers in VEC, where multiple tasks can be offloaded to Road Side Units (RSUs). We analyze the role played by multiple evaluation metrics in the existing methods for ensuring the quality of service (QoS) and further design a novel online multi-factor aware task offloading algorithm with a hierarchical fine-grained computing scheduling scheme inside the edge server. We evaluate it by conducting intensive simulation tests and comparing the results with some state-of-the-art approaches. Numerical results show that the proposed algorithm outperforms the methods in the control group in different aspects and achieves the best overall performance.