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Adaptive Job-Scheduling Algorithm based on Queuing Theory in a Hybrid Cloud Environment

Volume 15, Number 6, June 2019, pp. 1580-1590
DOI: 10.23940/ijpe.19.06.p9.15801590

Yanpei Liua, Xiaoni Chena, Ying Hua, and Qiang Caib

aSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
bBeijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, 102488, China

 

(Submitted on March 20, 2019; Revised on April 3, 2019; Accepted on June 5, 2019)

Abstract:

To resolve the problem of unreasonable resource allocations caused by the continuous arrival of different types of jobs in a hybrid cloud environment, an adaptive job-scheduling algorithm based on queuing theory is proposed. This paper analyses job load types, and the jobs are classified according to the logistic regression method. A resource utility is used to classify the nodes in a private cloud cluster by considering the heterogeneity of the private cloud resources. Based on the job classification and the resource classification, a queuing model is established, and an adaptive genetic algorithm is used to manage the job queue's arrival rate that becomes the basis of the resource allocation. The proposed algorithm is compared with some existed similar algorithms to verify its performance in terms of job response times and throughput.

 

References: 11

  1. J. G. Chen, K. L. Li, Z. Tang, and K. Bilal, “A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment,” IEEE Transactions on Parallel & Distributed Systems, Vol. 28, No. 4, pp. 919-933, 2017
  2. C. Napoli, G. Pappalardo, and E. Tramontana, “A Cloud-Distributed GPU Architecture for Pattern Identification in Segmented Detectors Big-Data Surveys,” Computer Journal, Vol. 59, No. 3, pp. 338-352, 2018
  3. C. W. Yang, Q. Y. Huang, and Z. L. Li, “Big Data and Cloud Computing: Innovation Opportunities and Challenges,” International Journal of Digital Earth, Vol. 10, No. 1, pp. 13-53, 2017
  4. Y. H. Moon and C. H. Youn, “Multihybrid Job Scheduling for Fault-Tolerant Distributed Computing in Policy-Constrained Resource Networks,” Computer Networks, Vol. 82, No. 3, pp. 81-95, 2015
  5. A. Rasooli and D. G. Down, “COSHH: A Classification and Optimization based Scheduler for Heterogeneous Hadoop Systems,” Future Generation Computer Systems, Vol. 36, No. 3, pp. 1-15, 2014
  6. W. Chongdarakul, P. Sophatsathit, and C. Lursinsap, “Efficient Task Scheduling based on Theoretical Scheduling Pattern Constrained on Single I/O Port Collision Avoidance,” Simulation Modelling Practice and Theory, Vol. 56, No. 67, pp. 171-190, 2016
  7. M. Khan, Y. Liu, and M. Li, “Data Locality in Hadoop Cluster Systems,” in Proceedings of 2014 International Conference on Fuzzy Systems and Knowledge Discovery, pp. 720-724, 2014
  8. W. Tian, G. Luo, and L. Tian, “On Dynamic Job Ordering and Slot Configurations for Minimizing the Makespan of Multiple MapReduce Jobs,” IEEE Transactions on Service Computing, Vol. 9, No. 1, pp. 1-6, 2016
  9. C. T. Chen, L. J. Hung, and S. Y. Hsieh, “Heterogeneous Job Allocation Scheduler for Hadoop MapReduce using Dynamic Grouping Integrated Neighboring Search,” IEEE Transactions on Cloud Computing, pp. 1-14, 2017
  10. O. Komori, S. Eguchi, and S. Ikeda, “An Asymmetric Logistic Regression Model for Ecological Data,” Methods in Ecology & Evolution, Vol. 7, No. 2, pp. 249-260, 2016
  11. S. Spicuglia and L. Y. Chen, “On Load Balancing: A Mix-Aware Algorithm for Heterogeneous Systems,” in Proceedings of 2013 International Conference on PERFORMANCE Engineering, pp. 71-76, 2013

 

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