A Scheduling Scheme in a Container-Based Edge Computing Environment Using Deep Reinforcement Learning Approach
Tingting Lu, Fanping Zeng, Jingfei Shen, Guozhu Chen, Wenjuan Shu, Weikang Zhang · 2021 17th International Conference on Mobility, Sensing and Networking (MSN) · 2021
Edge computing has been proposed as an extension of cloud computing to provide computation, storage, and network services in network edge. The tasks requested from terminal devices can be processed at the edge to save network bandwidth and reduce response time as long as the edge server is configured with the corresponding virtualization services. However, the limited capacity of various resources of edge servers and the low-delay service demands of tasks limit the application of traditional virtualization technologies in the task scheduling and resource management of edge computing. Meanwhile, the tasks have become more diverse, which are often divided into independent tasks and complex tasks composed of multiple dependent tasks.In this paper, we study the task scheduling problem in the container-based edge computing environment. Based on the Proximal Policy optimization algorithm, we propose two Task Scheduling algorithms for independent (PPOTS) and dependent (PPODTS). Our objective is to minimize the utility which is a trade-off between the completion time and the energy consumption. Experimental results show that our proposed PPOTS and PPODTS algorithms can reduce the average utility by at least 15.83% (and up to 77.9%) and at least 3.5% (and up to 10.3%) compared with baselines respectively.