Driven by advanced 5G cellular systems, mobile edge computing (MEC) has emerged as a promising technology that can meet the energy efficiency and latency requirements of IoT applications. Edge service migration in the MEC environment plays an important role in ensuring user service quality and enhancing terminal computing capabilities. Application services on the edge side should be migrated from different edge servers to edge nodes closer to users, so that services follow users and ensure high-quality services. In addition, during the migration process, edge services face security challenges in an edge network environment without centralized management. To tackle this challenge, this paper innovatively proposes a blockchain-based security edge service migration framework, Falcon, which uses mobile agents different from VM and container as edge service carriers, making migration more flexible. Furthermore, we considered the dependencies between agents and designed a service migration algorithm to maximize the migration benefits and obtain better service quality. In order to ensure the migration of edge services in a safe and reliable environment, Falcon maintains an immutable alliance chain among multiple edge clouds. Finally, the experimental results show that “Falcon” has lower energy consumption and higher service quality.
With the rise of latency-sensitive and computationally intensive applications in mobile edge computing (MEC) environments, the computation offloading strategy has been widely studied to meet the low-latency demands of these applications. However, the uncertainty of various tasks and the time-varying conditions of wireless networks make it difficult for mobile devices to make efficient decisions. The existing methods also face the problems of long-delay decisions and user data privacy disclosures. In this paper, we present the FDRT, a federated learning and deep reinforcement learning-based method with two types of agents for computation offload, to minimize the system latency. FDRT uses a multi-agent collaborative computation offloading strategy, namely, DRT. DRT divides the offloading decision into whether to compute tasks locally and whether to offload tasks to MEC servers. The designed DDQN agent considers the task information, its own resources, and the network status conditions of mobile devices, and the designed D3QN agent considers these conditions of all MEC servers in the collaborative cloud-side end MEC system; both jointly learn the optimal decision. FDRT also applies federated learning to reduce communication overhead and optimize the model training of DRT by designing a new parameter aggregation method, while protecting user data privacy. The simulation results showed that DRT effectively reduced the average task execution delay by up to 50% compared with several baselines and state-of-the-art offloading strategies. FRDT also accelerates the convergence rate of multi-agent training and reduces the training time of DRT by 61.7%.
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