聂轩,
吴大鹏,,
李红霞
1.重庆邮电大学通信与信息工程学院 重庆 400065
2.重庆高校市级光通信与网络重点实验室 重庆 400065
3.泛在感知与互联重庆市重点实验室 重庆 400065
基金项目:国家自然科学基金(61771082, 61871062)，重庆市高校创新团队建设计划(CXTDX201601020)
详细信息
作者简介:王汝言：男，1969年生，教授，博士，研究方向为泛在网络、多媒体信息处理等
聂轩：男，1995年生，硕士生，研究方向为移动边缘计算
吴大鹏：男，1979年生，教授，博士，研究方向为泛在无线网络、无线网络服务质量控制等
李红霞：女，1969年生，高级工程师，研究方向为光无线融合网络
通讯作者:吴大鹏 wudp@cqupt.edu.cn
中图分类号:TP393计量
文章访问数:1747
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PDF下载量:99
被引次数:0
出版历程
收稿日期:2019-04-27
修回日期:2019-10-30
网络出版日期:2019-11-13
刊出日期:2020-01-21
Social Attribute Aware Task Scheduling Strategy in Edge Computing
Ruyan WANG,Xuan NIE,
Dapeng WU,,
Hongxia LI
1. School of Telecommunication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. Chongqing Key Laboratory of Optical Communication and Network, Chongqing 400065, China
3. Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing 400065, China
Funds:The National Natural Science Foundation of China (61771082, 61871062), Chongqing Funded Project of Chongqing University Innovation Team Construction (CXTDX201601020)
摘要
摘要:边缘计算服务器的负载不均衡将严重影响服务能力，该文提出一种适用于边缘计算场景的任务调度策略(RQ-AIP)。首先，根据服务器的负载分布情况衡量整个网络的负载均衡度，结合强化学习方法为任务匹配合适的边缘服务器，以满足传感器节点任务的资源差异化需求；进而，构造任务时延和终端发射功率的映射关系来满足物理域的约束，结合终端用户社会属性，为任务不断地选择合适的中继终端，通过终端辅助调度的方式实现网络的负载均衡。仿真结果表明，所提出的策略与其他负载均衡策略相比能有效地缓解边缘服务器之间的负载和核心网的流量，降低任务处理时延。
关键词:计算机网络/
边缘计算/
社会属性/
负载均衡
Abstract:Unbalanced load on the edge computing server will seriously affect service capabilities, a task scheduling strategy Reinforced Q-learning-Automatic Intent Picking (RQ-AIP) for edge computing scenarios is proposed. Firstly, the load balance of the entire network is measured based on the load distribution of the server. By combining the reinforcement learning method, the appropriate edge server is matched for the task to meet the resource differentiation needs of sensor node tasks. Then, a mapping relationship between task delay and terminal transmit power is constructed to satisfy the constraints of the physical domain. Combining the social attributes of terminal, the appropriate relay terminal is continuously selected for the task to achieve the load balancing of network by terminal-assisted scheduling. Simulation results show that compared with other load balancing strategies, the proposed strategy can effectively alleviate the load between the edge servers and the traffic of the core network, reduce task processing latency.
Key words:Computer network/
Edge computing/
Social attribute/
Load balancing
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https://jeit.ac.cn/article/exportPdf?id=695748c0-1272-4351-9683-fbeaf09f5868
