Multi objective dynamic task scheduling optimization algorithm based on deep reinforcement learning

In the stochastic and dynamic edge-cloud collaborative environment, the computing resources of the host are limited, and the resource requirements of computing tasks are random and changeable. Therefore, how to efficiently schedule dynamic tasks and improve system performance becomes challenging. The scheduling algorithm based on deep reinforcement learning optimizes the delay and energy consumption of the system by dynamically interacting with the environment, which can solve the problem of dynamic and changeable environment to a certain extent, but there are still problems such as poor model adaptability, low training efficiency, and unbalanced system load. In this paper, aiming at optimizing the average response time of task scheduling and the average energy consumption of the system, a multi-objective task scheduling model is designed, and a task scheduling policy optimization algorithm based on improved asynchronous advantage actor-critic (A3C) is proposed. The residual convolutional neural network (RCNN) improves the network structure of A3C, using asynchronous multi-threaded training methods to interact with the edge-cloud collaborative environment and capturing the random dynamic characteristics of resources required for computing tasks and heterogeneous edge-cloud hosts resource change characteristics to better adapt to random dynamic environments, and it can quickly updating network parameters, improving training speed, and make full use of host resources to solve system load imbalance problems. Simulation results show that the scheduling algorithm proposed in this paper can effectively reduce task response time and system energy consumption in an edge-cloud collaborative environment.