边缘计算中具有QoS保证的在线能耗感知任务分派

doi:10.11959/j.issn.2096-3750.2021.00230

物联网学报 ›› 2021, Vol. 5 ›› Issue (2): 71-77.doi: 10.11959/j.issn.2096-3750.2021.00230

所属专题：边缘计算

• 专题：物联网边缘智能与雾计算技术 • 上一篇下一篇

边缘计算中具有QoS保证的在线能耗感知任务分派

袁昊, 郭得科, 唐国明, 罗来龙

国防科技大学系统工程学院，湖南长沙 410073

修回日期:2021-03-01 出版日期:2021-06-30 发布日期:2021-06-01
作者简介:袁昊（1998- ），男，国防科技大学系统工程学院硕士生，主要研究方向为边缘计算、绿色计算等
郭得科（1980- ），男，博士，国防科技大学教授，主要研究方向为网络计算与系统、分布式计算与系统、网络空间安全、大数据分析处理、移动计算等
唐国明（1986- ），男，博士，国防科技大学副教授，主要研究方向为边缘计算、绿色计算等
罗来龙（1991- ），男，博士，国防科技大学讲师，主要研究方向为计算机网络、数据结构等
基金资助:
国家自然科学基金资助项目(U19B2024)

Online energy-aware task dispatching with QoS guarantee in edge computing

Hao YUAN, Deke GUO, Guoming TANG, Lailong LUO

College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Revised:2021-03-01 Online:2021-06-30 Published:2021-06-01
Supported by:
The National Natural Science Foundation of China(U19B2024)

摘要/Abstract

摘要：

通过在网络边缘布置大量的边缘服务器，边缘计算能够为用户提供低时延、高带宽的服务。然而，大量布置边缘服务器也带来了高能耗等问题。当用户将任务从终端设备分派到不同的边缘服务器时，边缘服务器的异构性，会产生不同的能耗和时延。因此，如何在众多边缘服务器中选择一个最优的服务器进行任务分派，使得能耗和时延都比较低是具有挑战性的。提出了一种基于在线学习的具有服务质量（QoS, quality of service）保证的能耗感知任务分派方法，它可以通过与环境进行交互来获取实时的信息，从而在分派任务时，在保证QoS可接受的基础上，总体能耗最低。实验结果表明，与其他方法相比，提出的方法可以高效地将任务分派到最优的边缘服务器上，显著降低边缘计算网络的整体能耗。

关键词: 边缘计算, 任务分派, QoS, 能耗感知, 在线学习

Abstract:

Edge computing can provide users with low-latency and high-bandwidth services by deploying many edge servers at the network edge.However, a large number of deployments also bring problems of high energy consumption.When dispatching tasks from end devices to different edge servers, different energy consumption and delays will occur due to the edge servers’ heterogeneity.Therefore, it is a challenge to select an optimal server among many edge servers for task dispatching so that energy consumption and delay are relatively low.An energy-aware task dispatching method with quality of service (QoS) guarantee based on online learning was proposed.It can obtain real-time information by interacting with the environment to ensure energy consumption was minimal while the QoS was acceptable when dispatching tasks.Experiments show that the proposed method can dispatch tasks efficiently to the optimal server compared with other methods, thereby reducing the edge computing network’s overall energy consumption significantly.

Key words: edge computing, task dispatching, QoS, energy-aware, online learning

中图分类号:

袁昊, 郭得科, 唐国明, 罗来龙. 边缘计算中具有QoS保证的在线能耗感知任务分派[J]. 物联网学报, 2021, 5(2): 71-77.

Hao YUAN, Deke GUO, Guoming TANG, Lailong LUO. Online energy-aware task dispatching with QoS guarantee in edge computing[J]. Chinese Journal on Internet of Things, 2021, 5(2): 71-77.

图/表 3

参考文献 19

[1]	ASHTON K . That “Internet of Things” thing[J]. RFID Journal, 2009,22(7): 97-114.
[2]	HAYES B . Cloud computing[J]. Communications of the ACM, 2008,51(7): 9-11.
[3]	CAMPBELL A , COULSON G , HUTCHISON D . A quality of service architecture[J]. ACM SIGCOMM Computer Communication Review, 1994,24(2): 6-27.
[4]	SHI W S , CAO J , ZHANG Q ,et al. Edge computing:vision and challenges[J]. IEEE Internet of Things Journal, 2016,3(5): 637-646.
[5]	MENG J Y , TAN H S , XU C ,et al. Dedas:online task dispatching and scheduling with bandwidth constraint in edge computing[C]// 2019 IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2019: 2287-2295.
[6]	ZHANG K , MAO Y M , LENG S P ,et al. Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks[J]. IEEE Access, 2016(4): 5896-5907.
[7]	TERRY A , FATHI E , et al . The theory and practice of online learning[M]. New Brunswick: Athabasca University Press, 2008.
[8]	HAN Z H , TAN H S , LI X Y ,et al. OnDisc:online latency-sensitive job dispatching and scheduling in heterogeneous edge-clouds[J]. IEEE/ACM Transactions on Networking, 2019,27(6): 2472-2485.
[9]	JIA M K , CAO J N , LIANG W F . Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks[J]. IEEE Transactions on Cloud Computing, 2017,5(4): 725-737.
[10]	MENG J Y , TAN H S , XU C ,et al. Dedas:online task dispatching and scheduling with bandwidth constraint in edge computing[C]// 2019 IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2019: 2287-2295.
[11]	HUANG D , WANG P , NIYATO D . A dynamic offloading algorithm for mobile computing[J]. IEEE Transactions on Wireless Communications, 2012,11(6): 1991-1995.
[12]	CHEN Y , ZHANG N , ZHANG Y C ,et al. Energy efficient dynamic offloading in mobile edge computing for Internet of things[J]. IEEE Transactions on Cloud Computing, 2019(99): 1.
[13]	LIN X , WANG Y Z , XIE Q ,et al. Task scheduling with dynamic voltage and frequency scaling for energy minimization in the mobile cloud computing environment[J]. IEEE Transactions on Services Computing, 2015,8(2): 175-186.
[14]	GUO S T , XIAO B , YANG Y Y ,et al. Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing[C]// Proceeding of the 35th Annual IEEE International Conference on Computer Communications. Piscataway:IEEE Press, 2016: 1-9.
[15]	GITTINS J , GLAZEBROOK K , WEBER R . Multi-armed bandit allocation indices[M]. Chichester: John Wiley ＆ Sons, 2011.
[16]	GARIVIER A , MOULINES E . On upper-confidence bound policies for switching bandit problems[C]// Proceeding of Algorithmic Learning Theory.[S.l.:s.n.], 2011: 174-188.
[17]	KAUFMANN E , CAPPé O , GARIVIER A . On Bayesian upper confidence bounds for bandit problems[C]// Proceeding of Artificial Intelligence and Statistics.[S.l.:s.n.], 2012: 592-600
[18]	JIA M K , CAO J N , LIANG W F . Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks[J]. IEEE Transactions on Cloud Computing, 2017,5(4): 725-737.
[19]	URGAONKAR R , WANG S Q , HE T ,et al. Dynamic service migration and workload scheduling in edge-clouds[J]. Performance Evaluation, 2015,91: 205-228.

边缘计算中具有QoS保证的在线能耗感知任务分派

Online energy-aware task dispatching with QoS guarantee in edge computing

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