Computational task offloading algorithm based on deep reinforcement learning and multi-task dependency

Abstract

Edge computing is an emerging promising computing paradigm, which can significantly reduce the service latency by moving computing and storage demands to the edge of the network. Resource-constrained edge servers may fail to process multiple tasks simultaneously when several time-delay-sensitive and computationally demanding tasks are offloaded to only one edge server, and results in some issues such as high task processing costs. In this paper, we introduce a novel idea by dividing one task into several sub-tasks via the dependencies within the task and then offloading the sub-tasks to other edge servers in light of high concurrency for synchronization to minimize the total cost of task processing. To address the challenge of task dependencies and adaptation to dynamic scenes, we propose a Multi-Task Dependency Offloading Algorithm (MTDOA) based on deep reinforcement learning. The task offloading decision is modeled as a Markov decision process, and then a graph attention network is applied to extract the dependency information of different tasks, while LSTM and DQN are combined to deal with sequential problems. The simulation results show that the proposed MTDOA has better convergence ability compared with the baseline algorithms.