Manage On-Road Services with Chains: A Distributed Vehicular Task Offloading Approach Based on Blockchain Technology

In resource-constrained vehicular environments, offloading tasks to edge servers simultaneously from multiple client vehicles leads to severe resource competition, causing a cycle of increased latency. Designing a task offloading strategy that balances the latency of new offloaded tasks and those already being executed on edge servers is a crucial challenge. Additionally, centralized task offloading strategies based on global information require the design of complex communication mechanisms to collect task and computational workload information in real time, which is not desirable in vehicular environments due to the dynamic changes in the locations of client vehicles and the varying task offloading demands across time and space. To address these issues in multi-client vehicular task offloading environments, we propose Moscato, a blockchain-based distributed task offloading framework. In Moscato, client vehicles function as blockchain consensus nodes, utilizing existing consensus mechanisms for asynchronous, non-real-time global information sharing. To optimize task offloading decisions under diverse task profiles and dynamic traffic conditions, we integrate Federated Learning (FL) with Deep Q-Network (DQN), enabling intelligent, decentralized decision-making. Real-world datasets on edge server workloads and task latencies were collected to conduct simulation-based evaluations. Through comparison with state-of-the-art methods, we demonstrate that Moscato can design a better balance between the execution of newly offloaded tasks and ongoing ones, effectively alleviating resource competition under multi-client scenarios.