• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 软件工程 • 上一篇    下一篇

一种面向多平台航电资源建模及其调度算法

史文杰,詹雨奇,李奎   

  1. (南京航空航天大学计算机科学与技术学院,江苏 南京 211106)
  • 收稿日期:2019-04-28 修回日期:2019-07-12 出版日期:2019-11-25 发布日期:2019-11-25

A modeling and scheduling algorithm
for multi-platform avionic resources

SHI Wen-jie,ZHANG Yu-qi,LI Kui   

  1. (College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
     
  • Received:2019-04-28 Revised:2019-07-12 Online:2019-11-25 Published:2019-11-25

摘要:

随着航空电子系统朝着体系化发展,依靠网络构建包含不同飞行器航电资源在内的多平台航电资源就显得尤为重要。通过综合利用不同飞行平台的资源,发挥不同平台资源的优势,利用协同提升执行任务的能力。多平台航电系统的资源管理和任务调度是其核心功能。为了仿真和验证多平台航电系统资源管理功能,进一步研究资源调度方法,对多平台航电资源建模方法和调度算法进行研究,解决了对多平台航电系统上硬件资源的合理调度问题,增加了任务接受率。首先,利用多层分级拓扑结构对多平台航电资源进行建模,并对多平台航电任务需求进行分析;其次在SST自适应调度算法的基础上增加传感器、优先级等因素,以达到更高的接受率目标,改进后的算法完成一系列任务请求对航电资源的需求分配过程;最后,利用CloudSim仿真实验环境实现改进后的算法,从不同场景对实验结果进行全面分析。实验结果表明,本文设计的算法相比于原始算法任务请求的接受率有较大的改善。
 

关键词: 多平台航电资源, 资源建模, 资源调度, 云计算CloudSim平台

Abstract:

With the development of avionics systems, it is particularly important to rely on the network to build multi-platform avionics resources including different aircraft avionic resources. By comprehensively utilizing the resources of different flight platforms, the advantages of different platform resources can be utilized and cooperation is used to enhance the task handling ability. Resource management and task scheduling of multi-platform avionics systems are their core functions. In order to simulate and verify the resource management function of multi-platform avionics systems and further study the resource scheduling method, the paper studies the modeling method and the scheduling algorithm of multi-platform avionic resources. The paper reasonably schedules hardware resources on multi-platform avionics systems to increase the task acceptance rate. Firstly, multi-platform avionics resources are modeled by multi-level hierarchical topology, and the requirements of multi-platform avionics tasks are analyzed. Secondly, based on the SST (Sliding-Scheduled Tenant) adaptive scheduling algorithm, factors such as sensors and priorities are added to achieve higher acceptance rate. The algorithm completes a series of task requests in the demand allocation process of avionic resources. Finally, the algorithm is implemented in CloudSim simulation  environment. The experimental results are analyzed comprehensively from different scenarios, which show that the proposed algorithm has a higher request acceptance rate than the original algorithm.

Key words: multi-platform avionics system, resource modeling, resource scheduling, CloudSim cloud computing platform