面向边云协同计算的能耗感知资源调度方法

doi:10.3969/j.issn.0255-8297.2023.03.001

Abstract

Abstract: An energy-aware resource scheduling method for edge-cloud collaborative computing is proposed to address the issues of degraded real-time execution performance and high energy consumption when processing computationally complex tasks in edge computing. First, tasks are assigned to cloud computing and edge computing according to the real-time guaranteed rate. Then, an energy-aware resource scheduling strategy is proposed based on elastic resource characteristics to generate virtual resource configuration schemes for real-time tasks. Finally, simulation results verify the effectiveness of the proposed algorithm, which reduces energy consumption while ensuring real-time performance.

Key words: energy-aware, resource scheduling, edge-cloud collaborative computing, elastic resource

CLC Number:

P311

YANG Jun, ZHU Yingwen. Energy-Aware Resource Scheduling Method for Edge-Cloud Collaborative Computing[J]. Journal of Applied Sciences, 2023, 41(3): 369-377.

References

[1] Satyanarayanan M. The emergence of edge computing [J]. Computer, 2017, 50(1): 30-39.
[2] De Assuncao M D, Veith A D S, Buyya R. Distributed data stream processing and edge computing: a survey on resource elasticity and future directions [J]. Journal of Network & Computer Applications, 2018, 103(2): 1-17.
[3] Elsayed H, et al. Edge of things: the big picture on the integration of edge, IoT and the cloud in a distributed computing environment [J]. IEEE Access, 2018, 6(4): 1706-1717.
[4] Zhu Q H, Tang H, Huang J J, et al. Task scheduling for multi-cloud computing subject to security and reliability constraints [J]. IEEE/CAA Journal of Automatica Sinica, 2021, 8(4): 848-865.
[5] Huang W, Shi Z, Xiao Z, et al. A large-scale task scheduling algorithm based on clustering and duplication [J]. Journal of Smart Environments and Green Computing, 2021, 1(4): 202-217.
[6] Zhou E, Zhang J, Dai K. Research on task and resource matching mechanism in the edge computing network [J]. International Core Journal of Engineering, 2020, 6(4): 94-104.
[7] Tung N D, Bao L L, Vijay B. Price-based resource allocation for edge computing: a market equilibrium approach [J]. IEEE Transactions on Cloud Computing, 2021, 9(1): 302-317.
[8] 许小龙, 方子介, 齐连永, 等. 车联网边缘计算环境下基于深度强化学习的分布式服务卸载方法[J]. 计算机学报, 2021, 44(12): 2382-2405. Xu X L, Fang Z J, Qi L Y, et al. A deep reinforcement learning-based distributed service offloading method for edge computing empowered Internet of vehicles [J]. Chinese Journal of Computers, 2021, 44(12): 2382-2405. (in Chinese)
[9] Nasri A. Energy-efficient cloud servers: an overview of solutions and architectures [J]. The International Journal of Robotics Research, 2021, 13(1): 33-44.
[10] Abts D, Mary M R, Wells P M. Energy proportional datacenter networks [J]. International Symposium on Computer Architecture, 2010, 38(3): 338-347.
[11] Zhu X, Yang L T, Chen H, et al. Real-time tasks oriented energy-aware scheduling in virtualized clouds [J]. IEEE Transactions on Cloud Computing, 2014, 2(2): 168-180.
[12] Zotkiewicz M, Guzek M, Kliazovich D, et al. Minimum dependencies energy-efficient scheduling in data centers [J]. IEEE Transactions on Parallel and Distributed Systems, 2016, 27(2): 3561-3574.
[13] Wang Y, Min S, Wang X, et al. Mobile-edge computing: partial computation offloading using dynamic voltage scaling [J]. IEEE Transactions on Communications, 2016, 64(10): 4268-4282.
[14] Wang Y, Sheng M, Wang X, et al. Cooperative dynamic voltage scaling and radio resource allocation for energy-efficient multiuser mobile edge computing [C]//2018 IEEE International Conference on Communications (ICC), 2018: 1-6.
[15] Calheiros R N, Ranjan R, Beloglazov A, et al. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms [J]. Software Practice and Experience, 2011, 41(1): 23-50.
[16] Gutierrez-Garcia J O, Sim K M. A family of heuristics for agent-based elastic cloud bag-oftasks concurrent scheduling [J]. Future Generation Computer Systems, 2013, 29(7): 1682-1699.

Energy-Aware Resource Scheduling Method for Edge-Cloud Collaborative Computing

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	ZUO Yuxuan, QIANG Zhenping, DAI Fei, SU Shiqi, LIANG Zhihong. Improved Hashed Timelock Contract Based on Miners [J]. Journal of Applied Sciences, 2023, 41(3): 431-447.
[2]	WANG Feng, LIU Linlin, LIU Yang, BAI Hao, ZHANG Qiang. Book Resource Sharing System of Inter-university Alliance Based on Blockchain [J]. Journal of Applied Sciences, 2023, 41(3): 515-526.
[3]	ZHAO Haihong, YAO Zhongyuan, ZHU Weihua, ZHU Ziqiang, PAN Changfeng, SI Xueming. An Electronic Contract Sharing Scheme Based on Blockchain [J]. Journal of Applied Sciences, 2023, 41(2): 359-368.
[4]	XIE Shenglong, WANG Lu, LIU Ruijia, PU Ying, LIU Xiao. Proactive Self-Adaptive Approach Driven by LSTM Prediction for Software System [J]. Journal of Applied Sciences, 2023, 41(1): 121-140.
[5]	WANG Huajian, LI Renwei, ZHOU Huan, YANG Guogui. Design and Implementation of Decentralized Trusted Crowdsourcing Platform Based on Commitment Scheme [J]. Journal of Applied Sciences, 2023, 41(1): 141-152.
[6]	YE Xianghe, LIU Xueye, WANG Binhui, XING Shusong. Distributed Notary Cross-Chain Model for Consortium Chain [J]. Journal of Applied Sciences, 2022, 40(4): 567-582.
[7]	ZHANG Shuihai, SUN Haoyi, SUN Yiwei, PEI Bei, Lü Chunli. A Decentralized Storage Space Trading System under Blockchain Network [J]. Journal of Applied Sciences, 2022, 40(4): 583-599.
[8]	LIU Wei, WANG Dong, SHE Wei, PAN Heng, SONG Xuan, TIAN Zhao. An Efficient Query Method for Blockchain Traceability [J]. Journal of Applied Sciences, 2022, 40(4): 623-638.
[9]	ZHANG Lihua, LIU Ji, CAO Yu, CHEN Shihong, ZHEN Chen, ZHANG Ganzhe. Dual Consensus Hybrid Blockchain Cross Heterogeneous Domain Identity Authentication Scheme [J]. Journal of Applied Sciences, 2022, 40(4): 666-680.
[10]	YANG Yanbo, WAN Wunan, ZHANG Shibin, ZHANG Jinquan, QIN Zhi. Blockchain Consensus Mechanism for Risk Assessment Model of Heterogeneous Identity Alliance [J]. Journal of Applied Sciences, 2022, 40(4): 681-694.
[11]	CHEN Xiaohan, ZHAO Xiangfu, ZHANG Dengji, FEI Jiajia. SlightDetection: A Static Analysis Tool for Smart Contracts Security Vulnerabilities on Ethereum [J]. Journal of Applied Sciences, 2022, 40(4): 695-712.
[12]	PAN Lihu, YANG Fenyu, LU Feiping, QIN Shipeng. Modeling of Multi-agent City Safety and Livability Based on Street Perspective [J]. Journal of Applied Sciences, 2022, 40(1): 47-60.
[13]	ZHAO Yingqi, ZHU Xueyang, LI Guangyuan, GAO Ya, BAO Yulong. Verification of Smart Contracts with Time Constraints [J]. Journal of Applied Sciences, 2021, 39(1): 1-16.
[14]	WANG Siyuan, ZOU Shihong. Blockchain and Capability Based Access Control Mechanism in Multi-domain IoT [J]. Journal of Applied Sciences, 2021, 39(1): 55-69.
[15]	ZHOU Qi, SHEN Tao, ZHU Yan, LIU Yingli. Authentication Method of Integrated Energy Management System Based on Blockchain [J]. Journal of Applied Sciences, 2021, 39(1): 70-78.