Makespan Optimisation in Cloudlet Scheduling with Improved DQN Algorithm in Cloud Computing

被引：5

作者：

Chraibi, Amine ^{[1
]}

Ben Alla, Said ^{[1
]}

Ezzati, Abdellah ^{[1
]}

机构：

[1] Hassan First Univ Settat, Fac Sci & Technol, Math & Comp Sci Dept, LAVETE Lab, Settat 26000, Morocco

来源：

SCIENTIFIC PROGRAMMING | 2021年 / 2021卷

关键词：

Internet service providers - Quality of service - Scheduling - Distributed database systems;

D O I：

10.1155/2021/7216795

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Despite increased cloud service providers following advanced cloud infrastructure management, substantial execution time is lost due to minimal server usage. Given the importance of reducing total execution time (makespan) for cloud service providers (as a vital metric) during sustaining Quality-of-Service (QoS), this study established an enhanced scheduling algorithm for minimal cloudlet scheduling (CS) makespan with the deep Q-network (DQN) algorithm under MCS-DQN. A novel reward function was recommended to enhance the DQN model convergence. Additionally, an open-source simulator (CloudSim) was employed to assess the suggested work performance. Resultantly, the recommended MCS-DQN scheduler revealed optimal outcomes to minimise the makespan metric and other counterparts (task waiting period, resource usage of virtual machines, and the extent of incongruence against the algorithms).</p>

引用

页数：11

共 35 条

[1]

Ajmal M.S., 2020, P 2020 14 INT C OPEN, P1, DOI [10.1109/icosst51357.2020.9333057, DOI 10.1109/ICOSST51357.2020.9333057]

[2] Cloudlet Scheduling with Particle Swarm Optimization [J].

Al-Olimat, Hussein S. ;

Alam, Mansoor ;

Green, Robert ;

Lee, Jong Kwan .

2015 FIFTH INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS AND NETWORK TECHNOLOGIES (CSNT2015), 2015, :991-995

[3]

[Anonymous], 1993, REINFORCEMENT LEARNI, DOI 10.5555/168871

[4] CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms [J].

Calheiros, Rodrigo N. ;

Ranjan, Rajiv ;

Beloglazov, Anton ;

De Rose, Cesar A. F. ;

Buyya, Rajkumar .

SOFTWARE-PRACTICE & EXPERIENCE, 2011, 41 (01) :23-50

[5] A Deep Reinforcement Learning Approach to the Optimization of Data Center Task Scheduling [J].

Che, Haiying ;

Bai, Zixing ;

Zuo, Rong ;

Li, Honglei .

COMPLEXITY, 2020, 2020

[6] Deep reinforcement learning for computation offloading in mobile edge computing environment [J].

Chen, Miaojiang ;

Wang, Tian ;

Zhang, Shaobo ;

Liu, Anfeng .

COMPUTER COMMUNICATIONS, 2021, 175 (175) :1-12

[7] Q-learning based dynamic task scheduling for energy-efficient cloud computing [J].

Ding, Ding ;

Fan, Xiaocong ;

Zhao, Yihuan ;

Kang, Kaixuan ;

Yin, Qian ;

Zeng, Jing .

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2020, 108 :361-371

[8] Task scheduling based on deep reinforcement learning in a cloud manufacturing environment [J].

Dong, Tingting ;

Xue, Fei ;

Xiao, Chuangbai ;

Li, Juntao .

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2020, 32 (11)

[9]

Foster I, 2008, GCE: 2008 GRID COMPUTING ENVIRONMENTS WORKSHOP, P60

[10] Q-learning based flexible task scheduling in a global view for the Internet of Things [J].

Ge, Junxiao ;

Liu, Bin ;

Wang, Tian ;

Yang, Qiang ;

Liu, Anfeng ;

Li, Ang .

TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2021, 32 (08)

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