卫星网络中基于任务分割与调整的多维资源分配算法

Qu Hua, Wang Hongqiang, Z. Jihong, Yu Yongyue
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引用次数: 0

摘要

目前,大多数卫星网络资源分配算法只关注单一的资源分配场景。但是在实际的卫星网络中,资源是多种多样的,而且这些资源是相互制约的。此外,大多数卫星网络系统将用户请求的服务视为一项独立的、不可分割的任务。然而,任务的持续时间不同,卫星有执行任务的时间窗口,以及多种资源约束。随着任务的增加,卫星网络系统所能完成的任务数量和资源利用率将受到限制。针对上述问题,本文提出了一种基于任务拆分和调整的多维资源分配算法,包括任务拆分算法(TSA)和动态任务调整算法(DTAA)。首先,建立以单个区域窗口内的任务完成率、资源利用率和分区窗口利用率为优化目标的模型,通过拆分该区域窗口内最后一个不能执行的任务来解决资源利用率较低的问题。然后,建立多区域窗口间的动态任务调整模型,通过原窗口任务调整或新窗口任务调整策略解决任务执行的冲突问题。最后,将该算法与现有的解耦资源算法(DRA)和改进的贪婪算法(IGA)进行比较,通过仿真实验验证其在任务完成数、资源利用率和分区窗口利用率方面的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multi-dimensional Resource Allocation Algorithm Based on Task Splitting and Adjustment in Satellite Networks
Currently, most resource allocation algorithms in satellite networks only focus on single resource allocation scenarios. But there are many kinds of resources in the actual satellite network, and these resources restrict each other. In addition, most satellite network systems treat the services requested by users as an independent and indivisible task. However, the duration of the task is different and satellites have time windows to execute tasks, as well as multiple resource constraints. With the increase of tasks, the number of tasks that the satellite network system can complete and the resources utilization will be limited. For the above problems, this paper proposes a multi-dimensional resource allocation algorithm based on task splitting and adjustment, including task splitting algorithm (TSA) and dynamic task adjustment algorithm (DTAA). First, build a model that takes task completion rate, resource utilization and distriction window utilization as the optimization targets in a single distriction window, and solve the problem of lower resource utilization by splitting the last task that cannot be executed in the distriction window. Then, build a dynamic task adjustment model among multiple distriction windows, and solve the conflict problem of task execution through the original window task adjustment or the new window task adjustment strategy. Finally, compare the algorithm with the existing decoupled resource algorithm (DRA) and improved greedy algorithm (IGA), and verify its gains in the number of tasks completed, resource utilization and distriction window utilization through simulation experiments.
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