Multitasks Scheduling in Delay-Bounded Mobile Edge Computing

The Mobile Edge Computing (MEC) is a very novel technology in the social network. In order to satisfy the users’ delay and enhance data security and reduce energy consumption of the system. In this paper, we design an optimized strategy of edge servers chosen by reinforcement learning (AI algorithm) for resource consumption of multi-edge server. We transform the objective function into a convex optimization problem for the single edge server, and give proofs. In this scenario, it is made up of multi-users with multi-tasks and the same type of edge servers deployed on the edge of the base stations, the edge servers allocate computing resources to users. Meanwhile, the users send different tasks to the edge servers to complete computing tasks. In order to complete users’ requirements under their delay-bounded, we design an optimal path algorithm named Betweenness Centrality Algorithm (BCA) to reduce the transmission delay and we use Fuzzy Logical algorithm to classify computing tasks. We propose a resource allocation mechanism under satisfying delay-bounded. Finally, comparing to the random selected strategy and other schemes, we prove the effectiveness of the introduced algorithm, which reduces the energy consumption by 20% approximately for the single edge server, and the simulation proves that the Double Deep Q-learning (Double DQN) algorithm shows better performance than Random Selected Strategy for the multi-edge servers.