Dynamic Network Resource Autonomy Management and Task Scheduling Method

• Published in: Mathematics (Basel) Vol. 11; no. 5; p. 1232
• Main Authors: Li, Xiuhong, Yang, Jiale, Fan, Huilong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Algorithms; Alliances; dynamic networks; Efficiency; Food science; Linear programming; Methods; Quality of service architectures; Resource allocation; Resource management; Resource scheduling; Resource utilization; resources management; Revenue; Satellite communications; Satellite networks; Satellites; Scheduling; Scheduling (Management); Software; spatial networks; Task scheduling; China
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11051232

Satellite network resource management and scheduling technology are significant to constructing integrated information networks in heaven and earth. The difficulty in realizing this technology lies in improving resource utilization efficiency while ensuring the service quality of satellites and efficiently coordinating complex satellite network systems and services. This paper proposes a model, A Dynamic task scheduling method based on a UNified resource Management architecture(DUNM), based on the designed resource management architecture supported by dynamic scheduling algorithms to address the problems of low resource utilization, resource allocation, and task completion rate. First, with sufficient resources, the task execution time to complete a task is calculated based on the number of resources, task transmission time, task waiting time, etc. Secondly, based on the tasks assigned to satellites, the execution time of all functions with different transmission rates of communication links between satellites is calculated, and the total sum of all time consumption is analyzed. Finally, after simulation experiments and comparison with various baseline algorithms, about a 40% reduction in time to complete scheduled tasks and an almost 25% reduction in the average cost to finish a scheduling task, our method has higher scheduling efficiency and lower task completion revenue. It also guarantees a higher task completion rate while completing the tasks. Our approach attained a nearly 100% completion rate for scheduling tasks, which means that our algorithm can achieve the scheduling tasks faster and at high task revenue, thus improving the efficiency and economic efficiency of the whole system. Therefore, it validates the advantages of our method, such as high efficiency and high revenue.