Mobile edge computing (MEC) is a promising paradigm to improve the quality of computation experience of mobile devices because it allows mobile devices to offload computing tasks to MEC servers, benefiting from the powerful computing resources of MEC servers. However, the existing computation-offloading works have also some open issues: 1) security and privacy issues, 2) cooperative computation offloading, and 3) dynamic optimization. To address the security and privacy issues, we employ blockchain technology that ensures the reliability and irreversibility of data in MEC systems. Meanwhile, we jointly design and optimize the performance of blockchain and MEC. In this paper, we develop a cooperative computation offloading and resource allocation framework for blockchain-enabled MEC systems. In the framework, we design a multi-objective function to maximize the computation rate of MEC systems and the transaction throughput of blockchain systems by jointly optimizing offloading decision, power allocation, block size and block interval. Due to the dynamic characteristics of the wireless fading channel and the processing queues at MEC servers, the joint optimization is formulated as a Markov decision process (MDP). To tackle the dynamics and complexity of the blockchain-enabled MEC system, we develop an A3C-based cooperation computation offloading and resource allocation algorithm to solve the MDP problem. In the algorithm, deep neural networks are optimized by utilizing asynchronous gradient descent and eliminating the correlation of data. Simulation results show that the proposed algorithm converges fast and achieves significant performance improvements over existing schemes in terms of total reward.

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IEEE Internet of Things Journal
School of Information Technology

Feng, J. (Jie), Yu, F.R, Pei, Q. (Qingqi), Chu, X. (Xiaoli), Du, J. (Jianbo), & Zhu, L. (Li). (2019). Cooperative Computation Offloading and Resource Allocation for Blockchain-Enabled Mobile Edge Computing: A Deep Reinforcement Learning Approach. IEEE Internet of Things Journal. doi:10.1109/JIOT.2019.2961707