A fuzzy energy and security aware scheduling in cloud

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    The cloud computing is the one that deals with the trading of the resources efficiently in accordance to the user’s need. A Job scheduling is the choice of an ideal resource for any job to be executed with regard to waiting time, cost or turnaround time. A cloud job scheduling will be an NP-hard problem that contains n jobs and m machines and every job is processed with each of these m machines to minimize the make span. The security here is one of the top most concerns in the cloud. In order to calculate the value of fitness the fuzzy inference system makes use of the membership function for determining the degree up to which the input parameters that belong to every fuzzy set is relevant. Here the fuzzy is used for the purpose of scheduling energy as well as security in the cloud computing.

  • Keywords

    Cloud Computing; Job Scheduling; Cloud Job Scheduling; Fuzzy Inference System; Energy and. Security.

  • References

      [1] Konjaang, J., Ayob, F. H., & Muhammed, A. (2017). An Optimized Max-Min Scheduling Algorithm in Cloud Computing. Journal of Theoretical & Applied Information Technology, 95(9).

      [2] Chandran, K., Shanmugasudaram, V., & Subramani, K. (2016). Designing a fuzzy-logic based trust and reputation model for secure resource allocation in cloud computing. Int. Arab J. Inf. Technol., 13(1), 30-37.

      [3] Mann, Z. Á. (2015). Allocation of virtual machines in cloud data centers—a survey of problem models and optimization algorithms. ACM Computing Surveys (CSUR), 48(1), 11. https://doi.org/10.1145/2797211.

      [4] Muhuria PK, Shukla KK. Real-time scheduling of periodic tasks with processing times and deadlines as parametric fuzzy numbers. Appl Soft Comput 2009;9(3):936–46. https://doi.org/10.1016/j.asoc.2008.11.004.

      [5] Liu, G., Li, J., & Xu, J. (2013). An improved min-min algorithm in cloud computing. In Proceedings of the 2012 International Conference of Modern Computer Science and Applications (pp. 47-52). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-33030-8_8.

      [6] FazelMohammadi, D., Jamali, S., &Bekravi, M. (2014). Survey on Job Scheduling algorithms in Cloud Computing. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 2278-6856.

      [7] Pandey, P., & Singh, S. Fuzzy logic based job scheduling algorithm in cloud environment.

      [8] Fahmy, M. M. M. (2010). A fuzzy algorithm for scheduling non-periodic jobs on soft real-time single processor system. Ain Shams Engineering Journal, 1(1), 31-38. https://doi.org/10.1016/j.asej.2010.09.004.

      [9] Guo, F., Yu, L., Tian, S., & Yu, J. (2015). A workflow task scheduling algorithm based on the resources' fuzzy clustering in cloud computing environment. International Journal of Communication Systems, 28(6), 1053-1067. https://doi.org/10.1002/dac.2743.

      [10] Alla, H. B., Alla, S. B., Ezzati, A., &Mouhsen, A. (2017). A novel architecture with dynamic queues based on fuzzy logic and particle swarm optimization algorithm for task scheduling in cloud computing. In Advances in Ubiquitous Networking 2 (pp. 205-217). Springer Singapore. https://doi.org/10.1007/978-981-10-1627-1_16.

      [11] Xiaojun, W., Yun, W., Zhe, H., & Juan, D. (2015, June). The research on resource scheduling based on fuzzy clustering in cloud computing. In Intelligent Computation Technology and Automation (ICICTA), 2015 8th International Conference on (pp. 1025-1028). IEEE. https://doi.org/10.1109/ICICTA.2015.258.

      [12] Priya, V., & Babu, C. N. K. (2017). Moving average fuzzy resource scheduling for virtualized cloud data services. Computer Standards & Interfaces, 50, 251-257. https://doi.org/10.1016/j.csi.2016.10.011.

      [13] Singh, S., & Chana, I. (2016). EARTH: Energy-aware autonomic resource scheduling in cloud computing. Journal of Intelligent & Fuzzy Systems, 30(3), 1581-1600. https://doi.org/10.3233/IFS-151866.

      [14] Panda, S. K., & Jana, P. K. (2015). Efficient task scheduling algorithms for heterogeneous multi-cloud environment. The Journal of Supercomputing, 71(4), 1505-1533. https://doi.org/10.1007/s11227-014-1376-6.

      [15] Kumar, S. K., & Nagarajan, M. K. (2016). Fuzzy Logic Based Secure Data Transfer and Retrival Using PBSA and AES for Resource Allocation in Cloud. Fuzzy Systems, 8(7), 191-196.

      [16] Kaur, D., & Singh, S. (2014). An Efficient job scheduling Algorithm using MINMIN and Ant Colony Concept for grid computing. International Journal of Modern Education and Computer Science (IJMECS) ISSN, 2075-0161.

      [17] G. Jaspher W. Kathrine and MansoorIlaghi U, “Job Scheduling Algorithms in Grid Computing – Survey”, International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 7, September - 2012 ISSN: 2278-0181.

      [18] Brar, S. S., & Rao, S. (2015). Optimizing workflow scheduling using max-min algorithm in cloud environment. International Journal of Computer Applications, 124(4).

      [19] Mehranzadeh, A., & Hashemi, S. M. (2013). A novel-scheduling algorithm for cloud computing based on fuzzy logic. International Journal of Applied Information Systems (IJAIS), 5(7). https://doi.org/10.5120/ijais13-450939.

      [20] Chen, Z., Zhu, Y., Di, Y., & Feng, S. (2015). A dynamic resource scheduling method based on fuzzy control theory in cloud environment. Journal of Control Science and Engineering, 2015, 34. https://doi.org/10.1155/2015/383209.

      [21] Chen, Z., Zhu, Y., Di, Y., & Feng, S. (2015). A dynamic resource scheduling method based on fuzzy control theory in cloud environment. Journal of Control Science and Engineering, 2015, 34. https://doi.org/10.1155/2015/383209.

      [22] Zavvar, M., Rezaei, M., Garavand, S., & Ramezani, F. (2016). Fuzzy Logic-Based Algorithm Resource Scheduling for Improving The Reliability of Cloud Computing. Asia-Pacific Journal of Information Technology and Multimedia, 5(1).

      [23] Jabarzadeh, A., Rostami, M., Shahin, M., &Shahanaghi, K. (2017). Two-stage fuzzy-stochastic programming for parallel machine scheduling problem with machine deterioration and operator learning effect. Journal of Industrial and Systems Engineering, 10(3), 16-32.




Article ID: 9021
DOI: 10.14419/ijet.v7i1.2.9021

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.