ISSN 0253-2778

CN 34-1054/N

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Research of a novel cloud task scheduling algorithm

  • 1.

    College of Computer and Information, Hohai University, Nanjing 211100, China

  • 2.

    College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2014.07.008
  • Received Date: 21 March 2014
  • Accepted Date: 15 June 2014
  • Rev Recd Date: 15 June 2014
  • Publish Date: 30 July 2014
    Abstract

  • Abstract

    With its flexibility, guaranteed quality of service and on-demand features such as resource allocation model, cloud computing is often used to handle large computing tasks, so efficient task scheduling strategies of cloud computing play a vital role. Given the uncertainty in the number of tasks and the time of arrival at the server, and the fact that, users tend to have certain expectations (such as task priority, execution time, etc.) for the implementation of the tasks, reasonable allocation of computing resources for task scheduling to satisfy users QoS requirements is of great importance. A novel QoS-aware task scheduling mechanism (QTS) was proposed, this scheduling mechanism can best meet the users QoS requirements. By comparing QTS with RR, Max-Min and Min-Min scheduling policies by CloudSim simulation, it was found that QTS is a more effective task scheduling mechanism.

    Abstract

    With its flexibility, guaranteed quality of service and on-demand features such as resource allocation model, cloud computing is often used to handle large computing tasks, so efficient task scheduling strategies of cloud computing play a vital role. Given the uncertainty in the number of tasks and the time of arrival at the server, and the fact that, users tend to have certain expectations (such as task priority, execution time, etc.) for the implementation of the tasks, reasonable allocation of computing resources for task scheduling to satisfy users QoS requirements is of great importance. A novel QoS-aware task scheduling mechanism (QTS) was proposed, this scheduling mechanism can best meet the users QoS requirements. By comparing QTS with RR, Max-Min and Min-Min scheduling policies by CloudSim simulation, it was found that QTS is a more effective task scheduling mechanism.
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    • References(18)
  • [1]
    陈康, 郑纬民. 云计算：系统实例与研究现状[J]. 软件学报, 2009, 20(5): 1 337-1 348.
    [2]
    Wang L Z, Ranjan R, Chen J J et al. Cloud Computing: Methodology, Systems And Applications[M]. Boca Raton: CRC Press, 2012.
    [3]
    Liu G, Li J, Xu J C. An improved Min-Min algorithm in cloud computing [C]// Proceedings of the 2012 International Conference of Modern Computer Science and Applications. Berlin, Germany: Springer, 2013: 47-52.
    [4]
    Guo L Z, Zhao S G, Shen S G, et al. Task scheduling optimization in cloud computing based on heuristic algorithm [J]. Journal of Networks, 2012, 7(3): 547-553.
    [5]
    Li K, Xu G C, Zhao G Yu, et al. Cloud task scheduling based on load balancing ant colony optimization [C]// Sixth Annual ChinaGrid Conference. Dalian, China: IEEE Press, 2011:3-9.
    [6]
    史少峰, 刘宴兵. 基于动态规划的云计算任务调度研究 [J]. 重庆邮电大学学报. 2012, 24(6): 687-692.
    [7]
    Cui Y F, Li X M, Dong K W, et al. Cloud computing resource scheduling method research based on improved genetic algorithm [J]. Advanced Materials Research, 2011, 271: 552-557.
    [8]
    Sindhu S, Mukherjee S. Efficient task scheduling algorithms for cloud computing environment [C]// International Conference on High Performance Architecture and Grid Computing. Chandigard, India: Springer, 2011: 79-83.
    [9]
    朱宗斌, 杜中军. 基于改进GA的云计算任务调度算法[J]. 计算机工程与应用. 2013, 49(5): 77-80.
    [10]
    Wang L Z, von Laszewski G, Kunze M, et al. Schedule distributed virtual machines in a service oriented environment [C]// Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications. Perth, Australia: IEEE Press, 2010: 230-236.
    [11]
    Fang Y Q, Wang F, Ge J W. A task scheduling algorithm based on load balancing in cloud computing [C]// International Conference on Web Information Systems and Mining. Sanya China: Springer 2010, 6318: 271-277.
    [12]
    Wang J P, Zhu Y L, Feng H Y. A multi-task scheduling method based on ant colony algorithm [J]. Advances in information Sciences and Service Sciences, 2012, 4(11): 185-192.
    [13]
    Rahman M M, Thulasiram R, Graham P. Differential time-shared virtual machine multiplexing for handling QoS variation in clouds [C]//Proceedings of the 1st ACM Multimedia International Workshop on Cloud-Based Multimedia Applications and Services for E-health. Nara, Japan: ACM Press, 2012: 3-8.
    [14]
    Jung J K, Kim N U, Jung S M, et al. Improved CloudSim for simulating QoS-based cloud services [C]// Ubiquitous Information Technologies and Applications. Netherlands: Springer, 2013: 537-545.
    [15]
    孙瑞锋, 赵政文. 基于云计算的资源调度策略[J]. 航空计算技术, 2010, 40(3): 103-105.
    [16]
    Lin W W, Chen L, Wang J Z, et al. Bandwidth-aware divisible task scheduling for cloud computing [J]. Software: Practice and Experience, 2014, 44(2): 163-174.
    [17]
    Buyya R, Ranjan R, Calheiros R N. Modeling and simulation of scalable cloud computing environments and the CloudSim toolkit: Challenges and opportunities [C]// International Conference on High Performance Computing & Simulation. Leipzig, Germany: IEEE Press, 2009: 1-11.
    [18]
    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 & Experience, 2011, 41(1): 23-50.
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    [1]
    陈康, 郑纬民. 云计算：系统实例与研究现状[J]. 软件学报, 2009, 20(5): 1 337-1 348.
    [2]
    Wang L Z, Ranjan R, Chen J J et al. Cloud Computing: Methodology, Systems And Applications[M]. Boca Raton: CRC Press, 2012.
    [3]
    Liu G, Li J, Xu J C. An improved Min-Min algorithm in cloud computing [C]// Proceedings of the 2012 International Conference of Modern Computer Science and Applications. Berlin, Germany: Springer, 2013: 47-52.
    [4]
    Guo L Z, Zhao S G, Shen S G, et al. Task scheduling optimization in cloud computing based on heuristic algorithm [J]. Journal of Networks, 2012, 7(3): 547-553.
    [5]
    Li K, Xu G C, Zhao G Yu, et al. Cloud task scheduling based on load balancing ant colony optimization [C]// Sixth Annual ChinaGrid Conference. Dalian, China: IEEE Press, 2011:3-9.
    [6]
    史少峰, 刘宴兵. 基于动态规划的云计算任务调度研究 [J]. 重庆邮电大学学报. 2012, 24(6): 687-692.
    [7]
    Cui Y F, Li X M, Dong K W, et al. Cloud computing resource scheduling method research based on improved genetic algorithm [J]. Advanced Materials Research, 2011, 271: 552-557.
    [8]
    Sindhu S, Mukherjee S. Efficient task scheduling algorithms for cloud computing environment [C]// International Conference on High Performance Architecture and Grid Computing. Chandigard, India: Springer, 2011: 79-83.
    [9]
    朱宗斌, 杜中军. 基于改进GA的云计算任务调度算法[J]. 计算机工程与应用. 2013, 49(5): 77-80.
    [10]
    Wang L Z, von Laszewski G, Kunze M, et al. Schedule distributed virtual machines in a service oriented environment [C]// Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications. Perth, Australia: IEEE Press, 2010: 230-236.
    [11]
    Fang Y Q, Wang F, Ge J W. A task scheduling algorithm based on load balancing in cloud computing [C]// International Conference on Web Information Systems and Mining. Sanya China: Springer 2010, 6318: 271-277.
    [12]
    Wang J P, Zhu Y L, Feng H Y. A multi-task scheduling method based on ant colony algorithm [J]. Advances in information Sciences and Service Sciences, 2012, 4(11): 185-192.
    [13]
    Rahman M M, Thulasiram R, Graham P. Differential time-shared virtual machine multiplexing for handling QoS variation in clouds [C]//Proceedings of the 1st ACM Multimedia International Workshop on Cloud-Based Multimedia Applications and Services for E-health. Nara, Japan: ACM Press, 2012: 3-8.
    [14]
    Jung J K, Kim N U, Jung S M, et al. Improved CloudSim for simulating QoS-based cloud services [C]// Ubiquitous Information Technologies and Applications. Netherlands: Springer, 2013: 537-545.
    [15]
    孙瑞锋, 赵政文. 基于云计算的资源调度策略[J]. 航空计算技术, 2010, 40(3): 103-105.
    [16]
    Lin W W, Chen L, Wang J Z, et al. Bandwidth-aware divisible task scheduling for cloud computing [J]. Software: Practice and Experience, 2014, 44(2): 163-174.
    [17]
    Buyya R, Ranjan R, Calheiros R N. Modeling and simulation of scalable cloud computing environments and the CloudSim toolkit: Challenges and opportunities [C]// International Conference on High Performance Computing & Simulation. Leipzig, Germany: IEEE Press, 2009: 1-11.
    [18]
    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 & Experience, 2011, 41(1): 23-50.
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