基于连续学习的视频物联网任务需求理解与调度方法  

Continuous Learning-Based Task Demand Understanding and Scheduling Method for Video Internet of Things

作　　者：徐冬竹 周安福 马华东[1,2] 张园 Xu Dongzhu;Zhou Anfu;Ma Huadong;and Zhang Yuan(School of Computer Science(National Pilot Software Engineering School),Beijing University of Posts and Telecommunications,Beijing 100876;State Key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications),Beijing 100876;China Telecommunications Corporation,Shanghai 200125)

机构地区：[1]北京邮电大学计算机学院(国家示范性软件学院),北京100876 [2]网络与交换技术全国重点实验室(北京邮电大学),北京100876 [3]中国电信集团有限公司,上海200125

出　　处：《计算机研究与发展》2024年第11期3793-3805,共13页Journal of Computer Research and Development

基　　金：国家自然科学基金创新研究群体项目(61921003);国家自然科学基金青年科学基金项目(6240072128);博士后创新人才支持计划项目(BX20240046)。

摘　　要：云网资源与视频任务的高效调度是保障视频物联网(video Internet of things,VIoT)应用性能的关键.然而,目前运营化VIoT所用调度算法对差异化的任务需求和高度动态的云网资源变化适应能力不足,导致VIoT应用性能不佳.针对上述问题,提出了一种基于连续学习的视频物联网任务需求理解与调度方法(continuous learning-based task demand understanding and scheduling method for VIoT,CLTUS).与传统启发式或机器学习驱动的调度算法不同,将连续学习引入云网资源与视频任务需求的匹配中.首先,基于通用的连续学习框架实现各类视频任务需求的准确理解;其次,依据视频任务之间的需求依赖关系,实现任务与服务器的适配,以精细化调度云网资源;最后,将所提方法部署于软件定义的VIoT实验平台上.与传统方法相比,CLTUS不仅将视频任务的平均处理效率提高了127.73%,还将云网资源利用均衡率提高至67.2%,有效增强了VIoT应用性能.Efficient scheduling between cloud-network resources and video tasks is crucial for the performance of video Internet of things(VIoT)applications.However,the current scheduling algorithms used in operational VIoT systems are insufficiently adaptable to differentiated task demands and highly dynamic changes in cloud-network resources,resulting in poor performance of VIoT applications.To overcome the aforementioned problem,we propose a continuous learning-based task demand understanding and scheduling method for VIoT(CLTUS)Unlike traditional heuristic or machine learning-driven scheduling algorithms,CLTUS integrates the continuous learning into the matching between cloud-network resources and video task demands.Specifically,it first employs a continuous learning framework to accurately comprehend various video task demands.Subsequently,based on the dependency relationships among video tasks,it achieves an optimal match between tasks and servers,thereby refining the scheduling of cloud-network resources.Finally,the proposed method is deployed on a software-defined VIoT experimental platform.Compared with conventional methods,CLTUS not only improves the average processing efficiency of video tasks by 127.73%but also increases the balanced utilization rate of cloud-network resources to 67.2%on average,effectively improving the performance of VIoT applications.

关 键 词：视频物联网 连续学习 任务需求理解 资源调度 运营化网络测量 

分 类 号：TP393.02[自动化与计算机技术—计算机应用技术]