Cloud Job Scheduling Model Based on Improved Plant Growth Algorithm

doi:10.16182/j.issn1004731x.joss.201812020

Abstract

Abstract: The performance of cloud job scheduling algorithm has a great importance to the whole cloud system. The key factors that affect cloud operation scheduling are found out, and a resource constraint model is established. The existing simulation plant growth algorithm is improved based on the Logistic model of plant growth law, so that the plant growth way was made to change according to the energy power. The comparison of four different plant models was carried out and their different features were analyzed. Compared with 6 typical cloud job scheduling algorithms, it is concluded that the improved simulation plant growth algorithm based on Logistic model has better job scheduling efficiency.

Key words: Hadoop, cloud computing, scheduling, plant growth simulation algorithm, MapReduce

CLC Number:

TP393
TP181

Li Qiang, Liu Xiaofeng. Cloud Job Scheduling Model Based on Improved Plant Growth Algorithm[J]. Journal of System Simulation, 2018, 30(12): 4649-4658.

References

[1] 黄山, 王波涛, 王国仁, 等. MapReduce优化技术综述[J]. 计算机科学与探索, 2013, 7(10): 885-905.
Huang Shan, Wang Botao, Wang Guoren, et al.A Survey on MapReduce Optimization Technologies[J]. Journal of Frontiers of Computer Science & Technology, 2013, 7(10): 885-905.
[2] Shivnath B.Towards automatic optimization of MapReduce programs[C]// Proceedings of the 1st ACM Symposium on Cloud Computing (SoCC '10), Indianapolis, USA, Jun 10- 11, 2010. New York: NY, USA: ACM, 2010: 137-142.
[3] Herodotou H, Lim H, Luo Gang, et al.Starfish: a self-tuning system for big data analytics[C]// Proceedings of the 5th Biennial Conference on Innovative Data Systems Research (CIDR 11), Asilomar, USA: Jan 9-12, 2011: 261-272.
[4] Herodotos H, Shivnath B.Profiling, what- if analysis, and cost-based optimization of MapReduce programs[C]// Proceedings of the 36th International Conference on Very Large Data Bases (VLDB 10), Singapore, 2010: 1111-1122.
[5] Tian Chao, Zhou Haojie, He Yongqiang, et al.A dynamic MapReduce scheduler for heterogeneous workloads[C]// Proceedings of the 8th International Conference on Grid and Cooperative Computing (GCC 09), Lanzhou: 2009: 218-224.
[6] Jahani E, Cafarella M J, Re C.Automatic optimization for MapReduce programs[C]// Proceedings of the 37th International Conference on Very Large Data Bases (VLDB 11), Seattle, USA: 2011: 385-396.
[7] Ahmad F, Chakradhar S T, Raghunathan A, et al.Tarazu:optimizing MapReduce on heterogeneous clusters[C]// Proceedingsof the 7th International Conference on Architectur-al Support for Programming Languages and Operating Systems(ASPLOS 12), London: UK, 2012: 61-74.
[8] Polo J, Carrera D, Becerra Y, et al.Performance management of accelerated MapReduce workloads in heterogeneous clusters[C]// Proceedings of the 2010 39th International Conference on Parallel Processing (ICPP 10), San Diego, USA, 2010. Washington, DC, USA: IEEE Computer Society, 2010: 653-662.
[9] Z Fadika, E Dede, J Hartog, et al.MARLA: MapReduce for heterogeneous clusters[C]// Proceedings of the 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CC-Grid’12), Ottawa, Canada, 2012: 49-56.
[10] 宋杰, 徐澍, 郭朝鹏, 等. 一种优化MapReduce系统能耗的任务分发算法[J]. 计算机学报, 2016, 39(2): 323-338.
Song Jie, Xu Shu, Guo Chaopen, et al.A Task Distribution Algorithm for Energy Consumption Optimization of MapReduce System[J]. Chinese Journal of Computers, 2016, 39(2): 323-338.
[11] 李强, 刘晓峰. 基于Hopfield神经网络的云存储负载均衡策略[J]. 计算机应用, 2017, 37(8): 2214-2217.
Li Qiang, Liu Xiaofeng.Load balancing strategy of cloud storage based on Hopfield neural network[J]. Journal of Computer Applications, 2017, 37(8): 2214-2217.
[12] 刘洋, 王伦文. 基于Hopfield网络的短波协同侦收设备调度技术[J]. 火力与指挥控制, 2017, 42(12): 135-138, 143.
Liu Yang, Wang Lunwen.Research on Technology of Resource Scheduling for HF Cooperative Reception Based on Hopfield Neural Network[J]. Fire Control & Command Control, 2017, 42(12): 135-138, 143.
[13] 王莉, 秦勇, 徐杰, 等. 植物多向生长模拟算法[J]. 系统工程理论与实践, 2014, 34(4): 1018-1027.
Wang Li, Qin Yong, Xu Jie, et al.Plant multi-direction growth simulation algorithm[J]. Systems Engineering- Theory & Practice, 2014, 34(4): 1018-1027.
[14] 曹庆奎, 刘新雨, 任向阳. 基于模拟植物生长算法的车辆调度问题[J]. 系统工程理论与实践, 2015, 35(6): 1449-1456.
Cao Qingkui, Liu Xinyu, Ren Xiangyang.Vehicle scheduling problem based on plant growth simulation algorithm[J]. Systems Engineering-Theory & Practice, 2015, 35(6): 1449-1456.
[15] 樊贵香. 混合模拟植物生长算法在包装件配送中的应用[J]. 包装工程, 2016, 37(13): 43-49.
Fan Guixiang.Application of Hybrid Plant Growth Simulation Algorithm in Packaged Products Distribution[J]. Packaging Engineering, 2016, 37(13): 43-49.
[16] 杨磊, 龚学余. 基于GPU的并行植物模拟生长算法[J].计算机工程与设计, 2016, 37(12): 3271-3279.
Yang Lei, Gong Xueyu.Parallel plant growth simulation algorithm based on GPU[J]. Computer Engineering and Design, 2016, 37(12): 3271-3279.
[17] 唐卫东, 李萍萍, 李金忠. 基于生长动力学的芦苇属植株虚拟生长模型[J]. 计算机应用, 2015, 35(4): 1110-1115.
Tang Weidong, Li Pingping, Li Jinzhong.Virtual development model of plant-reed based on growth kinetics[J]. Journal of Computer Applications, 2015, 35(4): 1110-1115.
[18] 张彩琴, 杨持. 内蒙古典型草原生长季内不同植物生长动态的模拟[J]. 生态学报, 2007, 27(9): 3618-3629.
Zhang Caiqin, Yang Chi.Simulation of growth dynamics of four plants in the typical steppe of Inner Mongolia in growing season[J]. Acta Ecologica Sinica, 2007, 27(9): 3618-3629.