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EdgePV: Collaborative Edge Computing Framework for Task Offloading

Recent analytical research has pointed out that almost all vehicles spend over 95% of their time in parking lots where their powerful computing resources are wasted. In this paper, we propose a novel collaborative computing paradigm that efficiently offloads online heterogeneous computation tasks to parked vehicles (PVs) during peak hours. A container orchestration based on Kubernetes is advocated to integrate into the existing infrastructure due to its cutting-edge features such as auto-healing, load-balancing, and security. We formulate the offloading problem analytically and present an intelligent metaheuristic algorithm to address dynamic online demands. Extensive evaluation demonstrates that our proposed paradigm improves task arrival ratio and average offloading cost for more than 40% compared with a set of heuristic algorithms. Additionally, owners of PVs can be beneficial by sharing their idle vehicle resources through received incentives.