Username   Password       Forgot your password?  Forgot your username? 

An Approach to Resource Scheduling based on User Expectation in Cloud Testing

Volume 13, Number 8, December 2017, pp. 1206-1218
DOI: 10.23940/ijpe.17.08.p4.12061218

Zhongsheng Qian, Xiaojin Wang

School of Information Technology, Jiangxi University of Finance & Economics, Nanchang 330013, China

(Submitted on October 8, 2017; Revised on November 6, 2017; Accepted on November 23, 2017)


Cloud testing, with the features of automatic deployment, parallel submission, on-demand distribution and timely response, has been widely favored by many users. Therefore, it is crucial to reduce energy consumption, satisfy user requirement and timely response to user requests for resources, which are guaranteed by a good resource scheduling scheme. The requirements and benefits between user and provider of cloud testing are comprehensively measured in this work. On one hand, in order to meet the expectations of different users for the finish time and cost of their tasks, the definition of user expectation is introduced and then a dynamic pricing model is constructed to achieve the flexible conversion between time and cost. On the other hand, genetic algorithm is employed to implement resource scheduling in cloud testing, which can shorten the running time of all tasks on the cloud testing platform to improve the efficiency and reduce the load as greatly as possible. Finally, comparative experiments show that the scheme proposed in this work is feasible and efficient.


References: 29

      1. I. Casas, J. Taheri, R. Ranjan, L. Wang, and A. Y. Zomaya, “GA-ETI: An Enhanced Genetic Algorithm for the Scheduling of Scientific Workflows in Cloud Environments,” Journal of Computational Science, Available online 4 September 2016 (
      2. R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. D. Rose, and R. Buyya, “CloudSim: a Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms,” Software Practice & Experience, vol. 41, no. 1, pp. 23-50, August 2011
      3. S. Chaisiri, B. S. Lee, and D. Niyato, “Optimization of Resource Provisioning Cost in Cloud Computing,” IEEE Transactions on Services Computing, vol. 5, no. 2, pp. 164-177, February 2011
      4. D. Ergu, G. Kou, Y. Peng, Y. Shi, and Y. Shi, “The Analytic Hierarchy Process: Task Scheduling and Resource Allocation in Cloud Computing Environment,” The Journal of Supercomputing, vol. 64, no. 3, pp. 1-14, June 2013
      5. J. Gao, X. Bai, W. T. Tsai, and T. Uehara, “Testing as a Service (TaaS) on Clouds,” in IEEE Seventh International Symposium on Service-Oriented System Engineering, pp. 212-223, Redwood, USA, March 2013
      6. H. Hallawi, J. Mehnen, and H. He, “Multi-Capacity Combinatorial Ordering GA in Application to Cloud Resources Allocation and Efficient Virtual Machines Consolidation,” Future Generation Computer Systems, vol. 69, pp. 1-10, November 2016
      7. J. Huang, “The Workflow Task Scheduling Algorithm Based on the GA Model in the Cloud Computing Environment,” Journal of Software, vol. 9, no. 4, pp. 873-880, April 2014
      8. J. Huang, R. J. Kauffman, and D. Ma, “Pricing Strategy for Cloud Computing: A Damaged Services Perspective, ” Decision Support Systems, vol. 78, pp. 80-92, November 2015
      9. D. Jung, T. Suh, H. Yu, and J. M. Gil, “A Workflow Scheduling Technique Using Genetic Algorithm in Spot Instance-Based Cloud,” Ksii Transactions on Internet & Information Systems, vol. 8, no. 9, pp. 3126-3145, September 2014
      10. A. K. Kar and A. Rakshit, “Flexible Pricing Models for Cloud Computing Based on Group Decision Making Under Consensus,” Global Journal of Flexible Systems Management, vol. 16, no. 2, pp. 191-204, June 2015
      11. A. V. Katherine and D. K. Alagarsamy, “Conventional Software Testing Vs. Cloud Testing,” International Journal of Scientific & Engineering Research, vol. 3, no. 9, pp. 1-5, September 2012
      12. R. Kumar and S. Singh, “Cloud Testing: Perspective and Challenges,” International Journal of Computer Applications, vol. 106, no. 17, pp. 975-8887, November 2014
      13. P. Liu, “Cloud Computing 3th ed.,” Publishing House of Electronics Industry, Beijing, China, August 2015(in Chinese)
      14. P. Mell and T. Grance, “The NIST Definition of Cloud Computing,” Communications of the ACM, vol. 53, no. 6, pp. 50-50, June 2010
      15. B. Narula and V. Beniwal, “Cloud Testing- Types, Service Platforms and Advantages,” International Journal of Computer Applications, vol. 72, no. 20, pp. 1-6, June 2013
      16. C. Papagianni, A. Leivadeas, S. Papavassiliou, V. Maglaris, C. Cervello-Pastor, and A. Monje, “On the Optimal Allocation of Virtual Resources in Cloud Computing Networks,” IEEE Transactions on Computers, vol. 62, no. 6, pp. 1060-1071, February 2013
      17. B. Qiao, R. Cai, D. Chen, H. Wang, Y. Chen, and G. Wang, “Resource Scheduling Optimization Algorithm for Xen Virtual Machines, ” Journal of Software, vol. 25, no. S2, pp. 201-202, December 2014 (in Chinese with English abstract)
      18. X. Shi and K. Xu, “Utility Maximization Model of Virtual Machine Scheduling in Cloud Environment,” Chinese Journal of Computers, vol. 36, no. 2, pp. 252-262, February 2013(in Chinese with English abstract)
      19. G. Tian, D. Meng, and J. Zhang, “Reliable Resource Provision Policy for Cloud Computing,” Chinese Journal of Computers, vol. 33, no. 10, pp. 1859-1872, October 2010 (in Chinese with English abstract)
      20. J. T. Tsai, J. C. Fang, and J. H. Chou, “Optimized Task Scheduling and Resource Allocation on Cloud Computing Environment Using Improved Differential Evolution Algorithm,” Computers & Operations Research, vol. 40, no. 12, pp. 3045-3055, December 2013
      21. T. Wang, Z. Liu, Y. Chen, Y. Xu, and X. Dai, “Load Balancing Task Scheduling Based on Genetic Algorithm in Cloud Computing,” in IEEE International Conference on Dependable, Autonomic and Secure Computing, pp. 146-152, Chengdu, China, July 2014
      22. X. Wu, J Hou, S. Zhuo, and W. Zhang. “Dynamic Pricing Strategy for Cloud Computing with Data Mining Method,” Communications in Computer & Information Science, vol. 207, pp. 40-54, January 2013
      23. W. Wei, Y. Liu, and W. Yang, “A Fast Approximation Algorithm for the General Resource Placement Problem in Cloud Computing Platform,” Journal of Computer Research and Development, vol. 53, no. 3, pp. 697-703, March 2016 (in Chinese with English abstract)
      24. Z. Xiao, W. Song, and Q. Chen, “Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment, ” IEEE Transactions on Parallel & Distributed Systems, vol. 24, no. 6, pp. 1107-1117, June 2013
      25. P. Xiao and Z. Tang, “Game Theory-based Resource Pricing Model in Cloud Platforms,” International Journal of Communication Networks & Distributed Systems, vol. 14, no. 3, pp. 256-271, January 2015
      26. Y. Yuan, C. Wang, C. Wang, T. Ren, and B. Liu, “An Uncompleted Information Game Based Resources Allocation Model for Cloud Computing,” Journal of Computer Research and Development, vol. 53, no. 6, pp. 1342-1351, June 2016 (in Chinese with English abstract)
      27. X. Zhao, B. Zhang, and C.Zhang, “Service Selection Based Resource Allocation for SBS in Cloud Environments,” Journal of Software, vol. 26, no. 4, pp. 867-885, April 2015 (in Chinese with English abstract)
      28. Y. Zheng, L. Cai, S. Huang, and Z. Wang, “VM Scheduling Strategies Based on Artificial Intelligence in Cloud Testing,” in IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 1-7, Las Vegas, NV, USA, June 2014
      29. Y. Zheng, L. Cai, S. Huang, J. Lu, and P. Liu, “Cloud Testing Scheduling Based on Improved ACO,” in International Symposium on Computers and Informatics, pp. 569-578, Istanbul, Turkey, January 2015.


          Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

          This site uses encryption for transmitting your passwords.