Parked Vehicles Task Offloading in Edge Computing

Published in IEEE Access (IF: 3.367), 2022

Abstract: The analytical research has recently indicated that the computational resources of Connected Autonomous Vehicles (CAVs) have been wasted since almost all vehicles spend over 95% of their time in parking lots. This paper presents a collaborative computing framework to efficiently offload online computational tasks to parked vehicles (PVs) during peak business hours. To maintain the service continuity, we advocate for integrating Kubernetes-based container orchestration to leverage its advanced features (e.g., auto-healing, load balancing, and security). We analytically formulate the task-offloading problem and then propose an intelligent meta-heuristic algorithm to dynamically deal with online heterogeneous demands. Additionally, we take a cumulative incentives model into account, where the PV owners are able to earn profit by sharing their computation resources. We also compare our algorithm with several existent heuristics on different sizes of the parking lot. Extensive simulation results show that our proposed computing framework significantly increases the possibility of accepting the online tasks and improves average task offloading cost by at least 40%. Besides, we quantify the PV availability by task acceptance ratios, which can be a critical criterion for network planners to achieve desired network service goals.

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Recommended citation: K. Nguyen, S. Drew, C. Huang and J. Zhou, “Parked Vehicles Task Offloading in Edge Computing,” in IEEE Acess, doi: 10.1109/ACCESS.2022.3167641.