• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Li Xuejun, Xu Jia, Zhu Erzhou, Zhang Yiwen. A Novel Computation Method for Adaptive Inertia Weight of Task Scheduling Algorithm[J]. Journal of Computer Research and Development, 2016, 53(9): 1990-1999. DOI: 10.7544/issn1000-1239.2016.20151175
Citation: Li Xuejun, Xu Jia, Zhu Erzhou, Zhang Yiwen. A Novel Computation Method for Adaptive Inertia Weight of Task Scheduling Algorithm[J]. Journal of Computer Research and Development, 2016, 53(9): 1990-1999. DOI: 10.7544/issn1000-1239.2016.20151175

A Novel Computation Method for Adaptive Inertia Weight of Task Scheduling Algorithm

More Information
  • Published Date: August 31, 2016
  • Particle swarm optimization (PSO) is a primary intelligence algorithm to solve task scheduling problem for workflow systems in cloud computing. The inertia weight is one of the most important parameters to achieve a balance between the global and local search in PSO algorithm. However, traditional adaptive inertia weight-based PSO task scheduling algorithms usually get local optimal and cause longer execution time and higher cost for scheduling plan. The traditional adaptive inertia weight does not comprehensively represent the information of particle position, and then cannot make a suitable balance between global and local search. Hence, a novel computation method for adaptive inertia weight is proposed to improve the computation method of success value of each particle. This method shows the position state of each particle more accurately and then improves the adaptability of inertia weight by comparing the fitness of each particle with the global best particle. Then a new inertia weight-based PSO algorithm is presented to solve task scheduling problem for cloud workflow systems. The novel weight can adjust the particle velocity more correctly so that the algorithm avoids premature convergence by a proper balance between local and global search. Comparing our new adaptive inertia weight with other five traditional inertia weights (viz. constant, index decreasing, linear decreasing, random, and adaptive inertia weight), the results show that our new adaptive inertia weight-based scheduling algorithm can always achieve stable convergence speed, the optimal fitness and execution cost of the scheduling plan (i.e. roughly 18% reduction of execution cost).
  • Related Articles

    [1]Lu Yuxuan, Kong Lanju, Zhang Baochen, Min Xinping. MC-RHotStuff: Multi-Chain Oriented HotStuff Consensus Mechanism Based on Reputation[J]. Journal of Computer Research and Development, 2024, 61(6): 1559-1572. DOI: 10.7544/issn1000-1239.202330195
    [2]Yu Xiao, Liu Hui, Lin Yuxiu, Zhang Caiming. Consensus Guided Auto-Weighted Multi-View Clustering[J]. Journal of Computer Research and Development, 2022, 59(7): 1496-1508. DOI: 10.7544/issn1000-1239.20210126
    [3]Yang Hongzhang, Yang Yahui, Tu Yaofeng, Sun Guangyu, Wu Zhonghai. Proactive Fault Tolerance Based on “Collection—Prediction—Migration—Feedback” Mechanism[J]. Journal of Computer Research and Development, 2020, 57(2): 306-317. DOI: 10.7544/issn1000-1239.2020.20190549
    [4]Wang Zuan, Tian Youliang, Yue Chaoyue, Zhang Duo. Consensus Mechanism Based on Threshold Cryptography Scheme[J]. Journal of Computer Research and Development, 2019, 56(12): 2671-2683. DOI: 10.7544/issn1000-1239.2019.20190053
    [5]Wei Songjie, Li Shuai, Mo Bing, Wang Jiahe. Regional Cooperative Authentication Protocol for LEO Satellite Networks Based on Consensus Mechanism[J]. Journal of Computer Research and Development, 2018, 55(10): 2244-2255. DOI: 10.7544/issn1000-1239.2018.20180431
    [6]Liu Yiran, Ke Junming, Jiang Han, Song Xiangfu. Improvement of the PoS Consensus Mechanism in Blockchain Based on Shapley Value[J]. Journal of Computer Research and Development, 2018, 55(10): 2208-2218. DOI: 10.7544/issn1000-1239.2018.20180439
    [7]Ye Songtao, Lin Yaping, Hu Yupeng, Zhou Siwang, You Zhiqiang. A Faulty Sensor Node Tolerance Algorithm Based on Cut Point Set[J]. Journal of Computer Research and Development, 2009, 46(12): 2117-2125.
    [8]Han Jianjun, Gan Lu, Ruan Youlin, Li Qinghua, Abbas A.Essa. Real-Time Dynamic Scheduling Algorithms for the Savings of Power Consumption and Fault Tolerance in Multi-Processor Computing Environment[J]. Journal of Computer Research and Development, 2008, 45(4): 706-715.
    [9]Cheng Xin, Liu Hongwei, Dong Jian, Yang Xiaozong. A Fault Tolerance Deadlock Detection/Resolution Algorithm for the AND-OR Model[J]. Journal of Computer Research and Development, 2007, 44(5): 798-805.
    [10]Luo Wei, Yang Fumin, Pang Liping, and Li Jun. A Real-Time Fault-Tolerant Scheduling Algorithm for Distributed Systems Based on Deferred Active Backup-Copy[J]. Journal of Computer Research and Development, 2007, 44(3).
  • Cited by

    Periodical cited type(7)

    1. 李翔硕,畅广辉,苏盛,阮冲,吴坡,李斌. 变电监控系统网络安全威胁指标研究综述与展望. 电力科学与技术学报. 2024(04): 1-10 .
    2. 高莉莉,高雪,林钰浩,吴钰博,范金鹏. 楼宇建筑空调系统设备错误连接关系自动检测算法. 制冷与空调(四川). 2022(02): 311-316+323 .
    3. 马标,胡梦娜,张重豪,周正寅,贾俊铖,杨荣举. 基于融合马尔科夫模型的工控网络流量异常检测方法. 信息安全学报. 2022(03): 17-32 .
    4. 燕敏,阮秀琴,赵阳,郑宏涛. 基于小样本学习的物联网异常状态修正算法. 计算机仿真. 2022(08): 389-393 .
    5. 张书钦,白光耀,李红,张敏智. 多源数据融合的物联网安全知识推理方法. 计算机研究与发展. 2022(12): 2735-2749 . 本站查看
    6. 陈国瑞,袁旭华. 基于HDFS开源架构的异常数据实时检测算法. 计算机仿真. 2021(08): 445-449 .
    7. 谢胜平. 石灰粉一体化加工设备状态检测与故障维修系统. 机械设计与制造工程. 2021(09): 44-48 .

    Other cited types(5)

Catalog

    Article views (1779) PDF downloads (890) Cited by(12)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return