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Recently, blockchain has gained momentum in the academic community thanks toits decentralization, immutability, transparency and security. As an emergingparadigm, Multi-access Edge Computing (MEC) has been widely used to providecomputation and storage resources to mobile user equipments (UE) at the edge ofthe network for improving the performance of mobile applications. In thispaper, we propose a novel blockchain-based MEC architecture where UEs canoffload their computation tasks to the MEC servers. In particular, a blockchainnetwork is deployed and hosted on the MEC platform as Blockchain as a Service(BaaS) that supports smart contract-based resource trading and transactionmining services for mobile task offloading. To enhance the performance of theblockchain-empowered MEC system, we propose a joint scheme of computationoffloading and blockchain mining. Accordingly, an optimization problem isformulated to maximize edge service revenue and blockchain mining reward whileminimizing the service computation latency with respect to constraints of userservice demands and hash power resource. We then propose a novel DeepReinforcement Learning (DRL) approach using a double deep Q-network (DQN)algorithm to solve the proposed problem. Numerical results demonstrate that theproposed scheme outperforms the other baseline methods in terms of bettersystem utility with computational efficiency. Experiment results also verifythat the trading contract design is efficient with low operation cost, showingthe feasibility of the proposed scheme.

Blockchain as a Service for Multi-Access Edge Computing: A Deep Reinforcement Learning Approach