Yitao Lei and Xingxiang Zhai and Burra Venkata Durga Kumar
Distributed System Computing Resource Scheduling Algorithm Based on Deep Reinforcement Learning
180 - 185
2023
17
3
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/10012985
https://publications.waset.org/vol/195
World Academy of Science, Engineering and Technology
As the quantity and complexity of computing in largescale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes highperformance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NPhard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decisionmaking ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling. The research proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling. The paper concludes the challenges and improvement directions for Deep Reinforcement Learningbased resource scheduling algorithms.
Open Science Index 195, 2023