[1]鲍春林,宋丽华,余 航.基于节点分层和延迟敏感的服务放置策略[J].计算机技术与发展,2022,32(10):14-20.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 003]
　BAO Chun-lin,SONG Li-hua,YU Hang.Service Placement Policies Based on Node Tiering and Latency Sensitivity[J].,2022,32(10):14-20.[doi:10. 3969 / j. issn. 1673-629X. 2022. 10. 003]
点击复制

基于节点分层和延迟敏感的服务放置策略()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年10期
页码:
14-20
栏目:
大数据与云计算
出版日期:
2022-10-10

文章信息/Info

Title:
Service Placement Policies Based on Node Tiering and Latency Sensitivity
文章编号:
1673-629X(2022)10-0014-07
作者:
鲍春林1 ; 宋丽华2 ; 余 航1
1. 陆军工程大学 研究生院,江苏 南京 210000;
2. 陆军工程大学 指挥控制工程学院,江苏 南京 210000
Author(s):
BAO Chun-lin1 ; SONG Li-hua2 ; YU Hang1
1. School of Graduate,Army Engineering University of PLA,Nanjing 210000,China;
2. School of Command Control and Engineering,Army Engineering University of PLA,Nanjing 210000,China
关键词:
灾难响应; 雾计算; 微服务; 服务部署; 无人机
Keywords:
disaster response; fog computing; microservice; service deployment; UAV
分类号:
TP39
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 10. 003
摘要:
在灾难应急响应时,为了提高救援效率,使用无人机作为灾区的临时通信基站提供中继服务,由无人机以及边缘设备组成雾节点网络共同提供服务。 但是由于雾节点设备的计算存储资源和传输带宽有限,无法部署大型应用程序。 为了避免单一雾节点负载过高,将原应用拆分为若干微服务并且分散部署到雾节点上。 然而服务数量的激增导致了服务请求时间过长。 针对该问题,提出了基于节点分层和延迟敏感的服务放置算法。 将雾节点划分为三层,约束微服务的部署位置。 对每个雾节点的等待时间建模,尽最大努力使每个雾节点的服务等待延迟最小。 在仿真实验中,通过与 Edge-ward策略进行对比,表明该方法能够在资源受限的情况下有效减少服务等待时间。
Abstract:
In disaster- response operations, UAV are used as temporary communication base stations in disaster areas to provide relayservices to improve the efficiency for rescue requests. Fog node network is formed by UAV and edge - device to offer services.Meanwhile,due to the limited computational storage resources and transmission bandwidth of the fog node,it is impossible to deploy huge applications. The original application is split into several microservices and deployed to the fog nodes in a decentralized manner to avoid excessive load on a single fog node. However,the proliferation of the number of services leads to more service request. To address this problem,a node-based hierarchical and latency-sensitive service placement algorithm is proposed. These fog nodes are divided into three layers to constrain the deployment location of microservices. The request time of each fog node is modeled,and the best effort is made to minimize the service waiting delay of each fog node. In simulation experiments,by comparing with the Edge-ward strategy,it is shownthat the proposed method can effectively reduce the service waiting time under the resource constraint.

相似文献/References:

[1]涂 亮,徐 雷.雾计算中基于古诺博弈的协作缓存优化算法[J].计算机技术与发展,2019,29(06):13.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 003]
　TU Liang,XU Lei.Cooperative Cache Optimization Algorithm Based on Cournot Game in Fog Computing[J].,2019,29(10):13.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 003]
[2]葛欣炜,段聪颖,陈思光.基于雾计算的能耗最小化公平计算迁移研究[J].计算机技术与发展,2022,32(03):107.[doi:10. 3969 / j. issn. 1673-629X. 2022. 03. 018]
　GE Xin-wei,DUAN Cong-ying,CHEN Si-guang.Fog Computing Based Energy Minimization and Fair Computation Offloading Mechanism[J].,2022,32(10):107.[doi:10. 3969 / j. issn. 1673-629X. 2022. 03. 018]

更新日期/Last Update: 2022-10-10