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http://dx.doi.org/10.3837/tiis.2018.01.014

A parallel tasks Scheduling heuristic in the Cloud with multiple attributes  

Wang, Qin (School of computer and software, Nanjing University of Information Science & Technology)
Hou, Rongtao (School of computer and software, Nanjing University of Information Science & Technology)
Hao, Yongsheng (School of computer and software, Nanjing University of Information Science & Technology)
Wang, Yin (School of public administration, Nanjing University of Information Science & Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.12, no.1, 2018 , pp. 287-307 More about this Journal
Abstract
There are two targets to schedule parallel jobs in the Cloud: (1) scheduling the jobs as many as possible, and (2) reducing the average execution time of the jobs. Most of previous work mainly focuses on the computing speed of resources without considering other attributes, such as bandwidth, memory and so on. Especially, past work does not consider the supply-demand condition from those attributes. Resources have different attributes, considering those attributes together makes the scheduling problem more difficult. This is the problem that we try to solve in this paper. First of all, we propose a new parallel job scheduling method based on a classification method of resources from different attributes, and then a scheduling method-CPLMT (Cloud parallel scheduling based on the lists of multiple attributes) is proposed for the parallel tasks. The classification method categories resources into different kinds according to the number of resources that satisfy the job from different attributes of the resource, such as the speed of the resource, memory and so on. Different kinds have different priorities in the scheduling. For the job that belongs to the same kinds, we propose CPLMT to schedule those jobs. Comparisons between our method, FIFO (First in first out), ASJS (Adaptive Scoring Job Scheduling), Fair and CMMS (Cloud-Minmin) are executed under different environments. The simulation results show that our proposed CPLMT not only reduces the number of unfinished jobs, but also reduces the average execution time.
Keywords
parallel tasks; Cloud resources; multiple attributes; job requirements;
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1 Y. Hao, G. Liu, R. Hou, Y. Zhu, J. Lu, "Performance Analysis of Gang Scheduling in a Grid," Journal of the Network and Systems Management, Vol. 23, No. 3, pp. 650-672, July 2015.   DOI
2 J. Paudel, J. N. Amaral, "Hybrid parallel task placement in irregular applications," The Journal of Parallel & Distributed Computing, Vol. 76, 94-105, 2014.
3 W. Yi-Rong, H. Kuo-Chan, W. Feng-Jian, "Scheduling online mixed-parallel workflows of rigid tasks in heterogeneous multi-cluster environments," Future Generation Computer Systems, Volume 60, pp. 35-47, 2016.
4 W. Jingjin, X. Xuanxing, L. Zhiling, "Hierarchical task mapping for parallel applications on supercomputers," The Journal of supercomputing, Vol. 71, pp. 1776-1802, 2015.   DOI
5 L. Liu, G. Xie, L. Yang and R. Li, "Schedule Dynamic Multiple Parallel Jobs with Precedence-Constrained Tasks on Heterogeneous Distributed Computing Systems," in Proc. Parallel and Distributed Computing (ISPDC), 2015 14th International Symposium on Limassol, pp. 130-137, 2015.
6 H. Kuo-Chan, T.Ying-Lin, L.Hsiao-Ching, "Task ranking and allocation in list-based workflow scheduling on parallel computing platform," The Journal of Supercomputing, Vol. 71, No.1, pp. 217-240, 2015.   DOI
7 K. Oh-Heum, C. Kyung-Yong, "Scheduling parallel tasks with individual deadlines," Theoretical Computer Science, Vol. 215, No.1-2, pp. 209-223, 1999.   DOI
8 T. He, S. Chen, H. Kim, L. Tong, KW. Lee, "Scheduling Parallel Tasks onto Opportunistically Available Cloud Resources," in Proc. 15st IEEE International Conference on Cloud Computing, 2012.
9 K. Kurowski, , A. Oleksiak, W. Piatek, J Weglarz, "Hierarchical scheduling strategies for parallel tasks and advance reservations in grids," Journal of Scheduling, Vol. 16, No. 4, pp. 349-368, 2011.   DOI
10 Y. Hao, M. Xia, N. Wen, "Parallel task scheduling under multi-Clouds," Ksii Transactions on Internet & Information Systems, Vol. 11, No. 1, 2017.
11 L.Xiaocheng, Z. Yabing, Y. Quanjun, P. Yong , Q. Long, "Scheduling parallel jobs with tentative runs and consolidation in the cloud," Journal of Systems and Software, Vol. 104, pp. 141-151, 2015.
12 Y. Xia, X. Li, Z. Shan, "Parallelized Fusion on Multisensor Transportation Data: A Case Study in CyberITS," International Journal of Intelligent Systems, Vol. 28, No. 6, pp. 540-564, 2013.   DOI
13 H.Ting, C. Shiyao, H. Kim, L. Tong, "To Migrate or to Wait: Bandwidth-Latency Tradeoff In Opportunistic Scheduling of Parallel Tasks," in Proc. 31st Annual IEEE International Conference on Computer Communications: Mini-Conference, 2012.
14 Y. Hao, L. Wang, M. Zheng, "An adaptive algorithm for scheduling parallel jobs in meteorological Cloud," Knowledge-Based Systems, Vol. 98, pp. 226-240, 2016.   DOI
15 Rafaelli de C. Coutinho, Lúcia M.A. Drummond, Yuri Frota, Daniel de Oliveira, "Optimizing virtual machine allocation for parallel scientific workflows in federated clouds," Future Generation Computer Systems, Vol. 46, pp. 51-68, 2015.
16 R. S. Chang, C.-Y. Lin, and et al, "An Adaptive Scoring Job Scheduling algorithm for grid computing," Information Sciences, Vol. 207, p. 79-89, 2012.   DOI
17 Hadoop fair scheduler, http://hadoop.apache.org/common/docs/r0.20.1/fair_scheduler.html.
18 W. Wang, Y. Chang, and et al, "Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid Cloud environments," The Journal of Supercomputing, Vol. 66, No. 2, pp. 783-811, 2013.   DOI
19 L. Jiayin, Q. Meikang, Mi. Zhong, Q. Gang, Q. Xiao, G. Zonghua, "Online optimization for scheduling preemptable tasks on IaaS Cloud systems," Journal of Parallel and Distributed Computing, Vol. 72, No. 5, pp. 666-677, 2012.   DOI
20 X. Qiu, Y. Dai, Y. Xiang, and L. Xing, "A hierarchical correlation model for evaluating reliability, performance, and power consumption of a cloud service," IEEE Transactions on Systems Man & Cybernetics Systems, Vol. 46, No.3, pp. 401-412, 2016.   DOI
21 C. Liuhua, S. Patel, S. Haiying and Z. Zhongyi, "Profiling and Understanding Virtualization Overhead in Cloud," in Proc. Parallel Processing (ICPP), 2015 44th International Conference on, Beijing, pp. 31-40, 2015.
22 A. Goscinski, and M. Brock, "Toward dynamic and attribute based publication, discovery and selection for Cloud computing," Future Generation Computer Systems, Vol.26, No.7, pp. 947-970, 2010.   DOI
23 W. Wang, Y. Jiang, W. Wu, "Multiagent-Based Resource Allocation for Energy Minimization in Cloud Computing Systems," IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol.47, No.2, pp. 205-220, 2017.   DOI
24 Y. Hao, M. Xia, N. Wen, R. Hou, H. Deng, L. Wang, Q. Wang, "Parallel task scheduling under multi-Clouds," KSII Transactions on Internet and Information Systems, Vol. 11, No.1, pp. 39-60, 2017.   DOI
25 E. Filiopoulou, P. Mitropoulou, A. Tsadimas, C. Michalakelis, M. Nikolaidou and D. Anagnostopoulos, "Integrating cost analysis in the cloud: A SoS approach," in Proc. Innovations in Information Technology (IIT), 2015 11th International Conference on Dubai, pp. 278-283, 2015.
26 Z. Longxin, L. Kenli, X. Yuming, M. Jing, Z. Fan, L. Keqin, "Maximizing reliability with energy conservation for parallel task scheduling in a heterogeneous cluster," Information Sciences, Vol. 319, pp. 113-131, 2015.   DOI
27 Y. Xia, M. Zhou, X. Luo, S. Pang and Q. Zhu, "A Stochastic Approach to Analysis of Energy-Aware DVS-Enabled Cloud Datacenters," IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 45, No. 1, pp. 73-83, 2015.   DOI
28 Y. Xia, T. Zhang T, S. Wang, "A Generic Methodological Framework for Cyber-ITS: Using Cyber-infrastructure in ITS Data Analysis Cases," IOS Press, 2014.
29 W. Jiayin, "Building Efficient Large-Scale Big Data Processing Platforms," Graduate Doctoral Dissertations, 2017.
30 Q. Kalim, M. Babar, H. K. Jawad and A. M. Sajjad, "Task partitioning, scheduling and load balancing strategy for mixed nature of tasks," The Journal of Supercomputing, Vol. 59, No. 3, pp. 1348-1359, 2012.   DOI