期刊
2020 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA (ICCC)
卷 -, 期 -, 页码 835-840出版社
IEEE
DOI: 10.1109/iccc49849.2020.9238937
关键词
Edge computing; Task scheduling; Deep reinforcement learning; Latency Optimization
资金
- National Natural Science Foundation of China [61872044, 61502040]
- Beijing Municipal Program for Top Talent
- Beijing Municipal Program for Top Talent Cultivation [CITTCD201804055]
- Qinxin Talent Program of Beijing Information Science and Technology University
With the rapid development of 5G mobile networks services, massive data explodes in the network edge. Cloud computing services suffer from long latency and huge bandwidth requirement. Edge computing has become the key technology of reducing service delay and traffic load in 5G mobile networks. However, how to intelligently schedule tasks in the edge computing environment is still a critical challenge. In this paper, we define the optimization problem of minimizing the delay for task scheduling in the cloud-edge network architecture. The problem is proved NP-hard and modeled following Markov decision process. We design a cloud-edge collaboration scheduling algorithm based on asynchronous advantage actor-critic (CECS-A3C). Simulation results show that the proposed algorithm has good convergence speed and can reduce the task processing time by an average of 28.3% and 46.1% compared with the existing DQN and RL-G algorithms, while keeping the performance scalable.
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