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SDN-based Approach to Generating and Optimizing Test Path for Cloud Application

Volume 13, Number 8, December 2017, pp. 1257-1267
DOI: 10.23940/ijpe.17.08.p8.12571267

Liqiong Chena,b, Yunxiang Liua, Guisheng Fanc

aDepartment of Computer Science and Information Engineering, Shanghai Institute of Technology,  Shanghai 200235,  China
bShanghai Key Laboratory of Computer Software Evaluating and Testing, Shanghai 201112, China
cDepartment of Computer Science and Engineering East China University of Science and Technology, Shanghai 200237, China

(Submitted on September 30, 2017; Revised on November 1, 2017; Accepted on November 17, 2017)



Abstract:

With the intensive and large-scale development of cloud computing, software testing has become one of the most important problems. How to evaluate and assess the testing process of a cloud computing system is a key to the deployment and use of it. This paper proposes a method to generate and optimize test paths of cloud application based on SDN, which separates the cloud service testing process from the underlying execution logic, thus improving the scalability of the testing process. The finite state machine (FSM) is used to establish the formal description language of the cloud application testing process, and is also used to model the basic elements, such as cloud services, jobs, test cases and cloud applications, and construct a test model for cloud application. The related theory of FSM is used to analyze the effectiveness and correctness of cloud application test model. Based on the actual mapping of the model, the generation method of test path is also given. In addition, we analyze the coverage of test cases and propose the test path optimization methods to improve the test efficiency. The specific examples and simulations show that this method can simplify the design and analysis of the cloud application testing process and effectively improve the generation of test paths.

 

References: 26

      1. M. Alansari and B. Bordbar, “Modelling and analysis of migration policies for autonomic management of energy consumption in cloud via Petri nets,” in 2014 International Conference on Cloud and Autonomic Computing g, pp. 121-130, September, 2014.
      2. A. Akella and K. Xiong, “Quality of Service (QoS)-Guaranteed Network Resource Allocation via Software Defined Networking (SDN),” in Proceeding of the 12th International Conference on Dependable, Autonomic and Secure Computing, pp.7-13, August, 2014.
      3. M. Alansari and B. Bordbar, “Modelling and Analysis of Migration Policies for Autonomic Management of Energy Consumption in Cloud via Petri-nets,” Mathematical Problems in Engineering, pp. 121 - 130, 2013.
      4. P. Arcaini, R. Holom and E. Riccobene. “ASM-based formal design of an adaptivity component for a Cloud system,” Formal Aspects of Computing, vol. 28, no.4, pp. 567 - 595, 2016.
      5. K. Alhazmi, A. Shami and A. Refaey, “Optimized provisioning of SDN-enabled virtual networks in geo-distributed cloud computing datacenters,” Journal of Communications and Networks, vol. 19, no. 4, pp. 402–415, 2017
      6. S. Bian, P. Zhang and Z. Yan, “A Survey on Software-Defined Networking Security,” in Eai International Conference on Mobile Multimedia Communications.Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering(ICST), pp. 190-198, June, 2016.
      7. T. Y. Chen and M. F. Lau, “A new heuristic for test suite reduction”, Information and Software Technology, vol. 40, no.5, pp. 347-354, 1998.
      8. P. Daniel and K. Y. Sim, “Spectrum-based fault localization tool with test case preprocessor,” in IEEE Conference on Open Systems (ICOS), pp.162-167, December 2013.
      9. G. Fan, H. Yu and L. Chen, “A formal aspect-oriented method for modeling and analyzing adaptive resource scheduling in cloud computing,” IEEE Transactions on Network and Service Management, vol. 13, no. 2, pp. 281-294, 2016
      10. G. Fan, H. Yu, L. Chen and D. Liu, “Petri net based techniques for constructing reliable service composition,” Journal of Systems and Software, vol. 86, no. 4, pp. 1089-1106, 2013
      11. H. Gharakheili, J. Bass, L. Exton and V. Sivaraman, “Personalizing the home network experience using cloud-based SDN,” in Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, pp.1-6, June, 2014.
      12. R. Ghosha, F. Longob and V. Naikc, “Modeling and Performance Analysis of Large Scale IaaS Clouds,” Future Generation Computer Systems, vol. 29, no. 5, pp. 1216-1234, 2013.
      13. M. J. Harrold, R. Gupta, and M. L. Soffa, “A methodology for controlling the size of a test suite”, ACM Transactions on Software Engineering and Methodology (TOSEM), vol.2, no.3, pp. 270-285, 1993.
      14. D. Hao, T. Xie, L. Zhang, and X. Wang, “Test input reduction for result inspection to facilitate fault localization,” Automated Software Engineering, vol. 17, no.1, pp. 5-31, 2010.
      15. D. Hao, L. Zhang, H. Zhong, H. Mei, and J. Sun, “Eliminating harmful redundancy for testing-based fault localization using test suite reduction: an experimental study”, in 21th IEEE International Conference on Software Maintenance (ICSM), pp.683-686, September 2005.
      16. R. Hierons, “Testing from Partial Finite State Machines without Harmonised Traces,” IEEE Transactions on Software Engineering, vol. 43, no. 11, pp. 1033 - 1043, 2017.
      17. G. Juve, A. Chervenak, E. Deelman, et al. Characterizing and profiling scientific workflows. Future Generation Computer Systems, 2013, 29(3):682-692.
      18. J. Li, W. Yao, Y. Zhang, H. Qian and J. Han, “Flexible and Fine-Grained Attribute-Based Data Storage in Cloud Computing,” IEEE Transactions on Services Computing, vol. 10, no. 5, pp. 785-796, 2017
      19. D. S. Linthicum, “Cloud Computing Changes Data Integration Forever: What's Needed Right Now,” IEEE Cloud Computing, vol. 4, no. 3, pp. 50-53, 2017
      20. B. Keshanchi, A. Souri and N. Navimipour, “An Improved Genetic Algorithm for Task Acheduling in the Cloud Environments Using the Priority Queues: Formal Verification, Simulation, and Statistical Testing,” Journal of Systems and Software, vol. 124, pp. 1-21, 2017.
      21. I. Ku, Y. Lu and M. Gerla, “Software-defined Mobile Cloud: Architecture, Services and Use Cases,” in The International Wireless Communications and Mobile Computing Conference (IWCMC 2014), pp.1-6, August , 2014.
      22. Y. Wang, B. I. Jun, and K. Zhang, “A tool for tracing network data plane via SDN/OpenFlow,” Journal of Science China Information Sciences, vol. 60, no. 2, pp. 022304:1-13, 2017
      23. S. Wang, J. Zhai, H. Zhu and X. Wang, “Parallel Ordinal Decision Tree Algorithm and Its Implementation in Framework of MapReduce,” Machine Learning and Cybernetics, Springer Berlin Heidelberg, pp. 241-251, 2014.
      24. H. U. Yannan, W. Wang and X. Gong, “On the feasibility and efficacy of control traffic protection in software-defined networks,” Journal of Science China Information Sciences, vol. 58, no. 12, pp. 1-19, 2015.
      25. Q. Yan, F. R. Yu and Q. Gong. “Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS)Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges”. IEEE Communications Surveys Tutorials, vol. 18, no.1, pp. 602-622, 2016.
      26. T. Yen and C. Su. “An SDN-based Cloud Computing Architecture and Its Mathematical Model”. in International Conference on Information Science. pp.1728-1731, 2014.

           

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