面向延迟敏感型物联网应用的计算迁移策略

doi:10.11772/j.issn.1001-9081.2019050891

计算机应用 ›› 2019, Vol. 39 ›› Issue (12): 3590-3596.DOI: 10.11772/j.issn.1001-9081.2019050891

面向延迟敏感型物联网应用的计算迁移策略

郭棉, 李绮琦

广东石油化工学院电子信息工程学院, 广东茂名 525000

收稿日期:2019-05-27 修回日期:2019-06-27 出版日期:2019-12-10 发布日期:2019-07-19
作者简介:郭棉(1979-),女,广东茂名人,讲师,博士,CCF会员,主要研究方向:边缘计算、云计算、网络服务质量、深度强化学习;李绮琦(1998-),女,广东广州人,主要研究方向:边缘计算、深度学习。
基金资助:
广东省自然科学基金资助项目（2015A030310287）；广东石油化工学院大学生创新创业培育计划项目（733149）。

Computation offloading policy for delay-sensitive Internet of things applications

GUO Mian, LI Qiqi

School of Electronic and Information Engineering, Guangdong University of Petrochemical Technology, Maoming Guangdong 525000, China

Received:2019-05-27 Revised:2019-06-27 Online:2019-12-10 Published:2019-07-19
Contact: 郭棉
Supported by:
This work is partially supported by the Natural Science Foundation of Guangdong Province (2015A030310287), the College Students' Innovation and Entrepreneurship Training Program of Guangdong University of Petrochemical Technology (733149).

摘要/Abstract

摘要： 针对云计算网络延迟较长、能耗过高和边缘服务器计算资源有限的问题，提出了一种提高延迟敏感型物联网（IoT）应用服务质量（QoS）的边缘-云合作的漂移加惩罚计算迁移策略（DPCO）。首先，建立物联网-边缘-云系统模型，对业务模式、计算任务所经历的传输延迟和计算延迟、系统产生的计算能耗和传输能耗等进行数学建模；然后，以系统能耗和任务平均延迟为优化目标，以边缘服务器的队列稳定性为限制条件构建边缘-云合作的计算迁移优化模型；接着，以优化目标为惩罚函数，基于李雅普诺夫稳定性理论推导出计算迁移优化模型的漂移加惩罚函数特性。最后，基于推导结果提出了DPCO计算迁移算法，通过每时隙选择使当前漂移加惩罚函数最小化的计算迁移策略来降低长期的单位时间能耗和缩短系统平均延迟。与轻流雾处理（LFP）、基准边缘计算（EC）、基准云计算（CC）策略相比，DPCO的系统能耗最低，约是CC策略的2/3；任务平均延迟也最小，可减少为CC的1/5。实验结果表明，DPCO能够有效降低边缘-云计算系统的能量消耗，减少计算任务的端到端延迟，满足延迟敏感型IoT应用的QoS要求。

关键词: 云计算, 边缘计算, 计算迁移, 能量消耗, 服务质量

Abstract: The large network transmission delay and high energy consumption in cloud computing as well as the limited computing resource in edge servers are the bottlenecks for the development of delay-sensitive Internet of Things (IoT) applications. In order to improve the Quality of Service (QoS) of IoT applications while achieving green computing for computing systems, an edge-cloud cooperation Drift-plus-Penalty-based Computation Offloading (DPCO) policy was proposed. Firstly, mathematical modeling was performed on the business model, the transmission delay as well as the computation delay of the computation job, the computation energy as well as the transmission energy generated by the system were modeled by constructing the IoT-Edge-Cloud model. Then, the system consumption and the job average delay were optimized, with the queueing stability of the edge servers as constraint condition, the edge-cloud cooperation computation offloading optimization model was built. After that, with the optimization targets as the penalty function, the drift-plus-penalty function properties of computation offloading optimization model were analyzed based on Liapunov stability theory. Finally, DPCO was proposed based on the above results, the long-term energy consumption per unit time and the average system delay were reduced by selecting the computation offloading policy of minimizing the present drift-plus-penalty function in every time slot. In comparison with Light Fog Processing (LFP), the benchmarked Edge Computing (EC) and Cloud Computing (CC) policies, DPCO consumes the lowest system energy, which is 2/3 of that of the CC policy; DPCO also provides the shortest average job delay, which is 1/5 of that of the CC policy. The experimental results show that DPCO can efficiently reduce the energy consumption of edge-cloud computing system, shorten the end-to-end delay of the computation job, and satisfy the QoS requirements of delay-sensitive IoT applications.

Key words: cloud computing, edge computing, computation offloading, energy consumption, Quality of Service (QoS)

中图分类号:

TP393.027

郭棉, 李绮琦. 面向延迟敏感型物联网应用的计算迁移策略[J]. 计算机应用, 2019, 39(12): 3590-3596.

GUO Mian, LI Qiqi. Computation offloading policy for delay-sensitive Internet of things applications[J]. Journal of Computer Applications, 2019, 39(12): 3590-3596.

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面向延迟敏感型物联网应用的计算迁移策略

Computation offloading policy for delay-sensitive Internet of things applications

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