Joint task allocation and resource optimization for blockchain enabled collaborative edge computing

Abstract

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless networks. However, edge computing servers (ECSs) from different operators may not trust each other, and thus the incentives for collaboration cannot be guaranteed. In this paper, we propose a consortium blockchain enabled collaborative edge computing framework, where users can offload computing tasks to ECSs from different operators. To minimize the total delay of users, we formulate a joint task offloading and resource optimization problem, under the constraint of the computing capability of each ECS. We apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal solution. Finally, we propose a reputation based node selection approach to facilitate the consensus process, and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the blockchain. Simulation results validate the effectiveness of the proposed algorithm, and the total delay can be reduced by up to 40% compared with the non-cooperative case.