Blockchain-Enabled Elastic Task Offloading and Migration Scheme in Edge Computing System

Mobile edge computing (MEC), which improves the quality of experience of mobile devices by offloading tasks to edge servers, has become a promising paradigm. This method can effectively reduce computational latency and lower energy consumption. However, traditional task offloading and migration schemes only consider a single static scenario, making it difficult to provide users with elastic computing services in changing environments. Moreover, the quality and cost of the same task vary depending on the service level. To tackle this problem, we propose a reinforcement learning approach called LSTM-SAC, which integrates long short-term memory (LSTM) with soft actor-critic (SAC). LSTM-SAC provides elastic computing services to users in MEC systems by adaptively selecting models based on the current environment, thereby enhancing the flexibility of MEC systems. In addition, we deploy the blockchain on the MEC system to enhance its security. All users must authenticate their identities by running a smart contract deployed on the blockchain before offloading tasks. The experimental results show that our proposed method outperforms benchmark methods in terms of convergence and security.