广东第二师范学院计算机学院,广东 广州 510303
刘林东(1978年生),男;研究方向:分布式计算、高性能计算;E-mail:hongox@163.com
邬依林(1970年生),男;研究方向:大数据分析与处理;E-mail:lyw@gdei.edu.cn
纸质出版日期:2021-09-25,
网络出版日期:2020-11-05,
收稿日期:2020-04-21,
录用日期:2020-05-29
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刘林东,邬依林.一种面向雾计算的任务调度算法研究[J].中山大学学报(自然科学版),2021,60(05):166-174.
LIU Lindong,WU Yilin.Research of a task scheduling algorithm in fog computing[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2021,60(05):166-174.
刘林东,邬依林.一种面向雾计算的任务调度算法研究[J].中山大学学报(自然科学版),2021,60(05):166-174. DOI: 10.13471/j.cnki.acta.snus.2020.04.21.2020A014.
LIU Lindong,WU Yilin.Research of a task scheduling algorithm in fog computing[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2021,60(05):166-174. DOI: 10.13471/j.cnki.acta.snus.2020.04.21.2020A014.
在雾计算环境中,为提高雾计算效率和给用户请求的任务分配合适的资源,需要对任务调度问题进行研究。面向流水线独立任务进行调度研究,首先基于经典Apriori算法提出一种雾计算环境中任务的分类I-Apriori算法;将得到的分类规则以及加权最早完成时间作为任务优先级选择的依据;对于同时达到的任务,优先选择出现在调度关系中的任务进行调度,其他雾结点则依据最早完成时间和加权链接数的高低进行调度。通过仿真实验对ITPS(improved task priority scheduling)算法的性能进行了评估,结果表明,ITPS算法在makespan及AWT(average waiting time)方面具有较好的性能。
In the heterogeneous and distributed computing environment of fog computing, in order to improve the efficiency of fog computing and allocate appropriate resources to corresponding tasks, task scheduling problem needs to be studied. The scheduling of pipeline independent tasks is studied. Firstly, based on the traditional Apriori algorithm, a task classification algorithm I-Apriori algorithm in fog computing environment is proposed. Association rules generated by I-Apriori algorithm are combined with the weighted earliest completion time of tasks in the task set. Tasks appear in the association rules are selected to schedule first, other fog nodes are scheduled according to the earliest completion time and the number of weighted links. The performance of ITPS algorithm is evaluated by simulation experiments. The results show that ITPS algorithm has a good performance in makespan and AWT.
雾计算任务调度关联规则雾计算结点
fog computingtask schedulingassociation rulefog computing node
刘林东.分布式异构环境中任务调度算法研究[D]. 广州: 华南理工大学, 2019.
LIU L D. Researches on task scheduling algorithm in distributed heterogeneous environment [D]. Guangzhou: South China University of Technology, 2019.
ZHU Q L, SI B J, YANG F F, et al. Task offloading decision in fog computing system [J]. China Communications, 2017, 14(11):59-68.
MUKHERJEE M, SHU L, WANG D. Survey of fog computing: fundamental, network applications, and research challenges [J].IEEE Communications Surveys & Tutorials, 2018,20(3): 1826-1857.
PULIAFITO C, MINGOZZI E, ANASTASI G. Fog computing for the internet of mobile things: issues and challenges [C]// IEEE International Conference on Smart Computing, 2017.
刘林东, 邬依林. 多DAG任务调度算法[J]. 中山大学学报(自然科学版), 2019, 58(4): 99-107.
LIU L D, WU Y L. Multi-DAG task scheduling algorithms [J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2019, 58(4): 99-107.
PHAM X Q, HUH E N. Towards task scheduling in a cloud-fog computing system [C]//IEEE Network Operations & Management Symposium, 2016.
YI S, HAO Z, QIN Z, et al. Fog computing: platform and applications [C]//The third IEEE Workshop on Hot Topics in Web Systems & Technologies, 2015.
LYU X, REN C, NI W, et al. Distributed optimization of collaborative regions in large-scale inhomogeneous fog computing [J]. IEEE Journal on Selected Areas in Communications, 2018: 574-586.
ZHANG Y H, ZHENG D, DENG R H. Security and privacy in smart health: efficient policy-hiding attribute-based access control [J]. IEEE Internet of Things Journal, 2018, 5(3): 2130-2145.
HUANG M, LIU Y, ZHANG N, et al. A services routing based caching scheme for cloud assisted CRNs [J]. IEEE Access, 2018,6(1): 15787-15805.
LIU X, DONG M, LIU Y, et al. Construction low complexity and low delay CDS for big data codes dissemination [J]. Complexity, 2018:1-19.
NI L, ZHANG J, JIANG C, et al. Resource allocation strategy in fog computing based on priced timed Petri nets [J]. IEEE Internet of Things Journal, 2017,4(5): 1216-1228.
ZENG D Z, GU L, GUO S, et al. Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system [J]. IEEE Transactions on Computers, 2016, 65(12): 3702-3712.
OUEIS J, STRINATI E C, SARDELLITTI S, et al. Small cell clustering for efficient distributed fog computing: a multi-user case [C]//Vehicular Technology Conference, IEEE, 2016.
INTHARAWIJITR K, IIDA K, KOGA H. Analysis of fog model considering computing and communication latency in 5G cellular networks [C]// IEEE International Conference on Pervasive Computing & Communication Workshops, 2016.
DENG R, LU R, LAI C, et al. Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption [J]. IEEE Internet of Things Journal, 2016,3(6):1171-1181.
PHAM X Q, HUH E N. Towards task scheduling in a cloud-fog computing system [C]// IEEE Network Operations & Management Symposium, 2016.
TANG C, WEI X, XIAO S, et al. A mobile cloud based scheduling strategy for industrial internet of things [J]. IEEE Access, 2018,6(3): 7262-7275.
DANG T, HOANG D. FBRC: optimization of task scheduling in fog-based region and cloud [C]//IEEE Trustcom/BigDataSE/ICESS, 2017:1109-1114.
YANG J, HUANG H, JIN X. Mining web access sequence with improved apriori algorithm [C]// IEEE International Conference on Computational Science & Engineering, 2017.
ZHANG S, DU Z, WANG J T L. New techniques for mining frequent patterns in unordered trees [J]. IEEE Transactions on Cybernetics, 2015,45(6):1113-1125.
BRENNAND C A R L, DUARTE J M, SILVA A P. SimGrid: A simulator of network monitoring topologies for peer-to-peer based computational grids [C]// IEEE 8th Latin-American Conference on Communications (LATINCOM), 2016.
DEGOMME A, LEGRAND A, MARKOMANOLIS G S, et al. Simulating MPI applications: the SMPI approach [J]. IEEE Transactions on Parallel and Distributed Systems, 2017, 28(8): 2387-2400.
MOHAMMED A, ELELIEMY A, CIORBA F M. Towards the reproduction of selected dynamic loop scheduling experiments using SimGrid-SimDag [C]//IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems, 2017.
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