Journal of Information Processing Systems

  • 제16권1호
  • /
  • Pages.132-144
  • /
  • 2020
  • /
  • 1976-913X(pISSN)
  • /
  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
권호 표지
DOI QR코드

DOI QR Code

A Load-Balancing Approach Using an Improved Simulated Annealing Algorithm

  • Hanine, Mohamed (Laboratory of Information Technology and Modeling, Faculty of Sciences Ben M'sik, Hassan II University Mohammedia) ;
  • Benlahmar, El-Habib (Laboratory of Information Technology and Modeling, Faculty of Sciences Ben M'sik, Hassan II University Mohammedia)
  • 투고 : 2017.07.05
  • 심사 : 2018.12.02
  • 발행 : 2020.02.29
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Cloud computing is an emerging technology based on the concept of enabling data access from anywhere, at any time, from any platform. The exponential growth of cloud users has resulted in the emergence of multiple issues, such as the workload imbalance between the virtual machines (VMs) of data centers in a cloud environment greatly impacting its overall performance. Our axis of research is the load balancing of a data center's VMs. It aims at reducing the degree of a load's imbalance between those VMs so that a better resource utilization will be provided, thus ensuring a greater quality of service. Our article focuses on two phases to balance the workload between the VMs. The first step will be the determination of the threshold of each VM before it can be considered overloaded. The second step will be a task allocation to the VMs by relying on an improved and faster version of the meta-heuristic "simulated annealing (SA)". We mainly focused on the acceptance probability of the SA, as, by modifying the content of the acceptance probability, we could ensure that the SA was able to offer a smart task distribution between the VMs in fewer loops than a classical usage of the SA.

키워드

참고문헌

  1. G. Gaspard, R. Jachniewicz, J. Lacava, and V. Meslard, "Equilibrage de Charge et Haute Disponibilite," 2009; http://generation-linux.elob.fr/dl/haute_dispo_rapport.pdf.
  2. S. Nepal, S. Chen, J. Yao, and D. Thilakanathan, "DIaaS: data integrity as a service in the cloud," in Proceedings of 2011 IEEE 4th International Conference on Cloud Computing, Washington, DC, 2011, pp. 308-315.
  3. C. Curino, E. P. Jones, R. A. Popa, N. Malviya, E. Wu, S. Madden, H. Balakrishnan, and N. Zeldovich, "Relational cloud: a database-as-a-service for the cloud," in Proceedings of the 5th Biennial Conference on Innovative Data Systems Research, Asilomar, CA, 2011.
  4. S. Frenot and J. Ponge, "LogOS: an automatic logging framework for service-oriented architectures," in 2012 38th Euromicro Conference on Software Engineering and Advanced Applications, Cesme, Turkey, 2012, pp. 224-227.
  5. R. Hammad and C. S. Wu, "Provenance as a service: a data-centric approach for real-time monitoring," in Proceedings of 2014 IEEE International Congress on Big Data, Anchorage, AK, 2014, pp. 258-265.
  6. H. Al-Aqrabi, L. Liu, J. Xu, R. Hill, N. Antonopoulos, and Y. Zhan, "Investigation of IT security and compliance challenges in security-as-a-service for cloud computing," in Proceedings of 2012 IEEE 15th International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops, Shenzhen, China, 2012, pp. 124-129.
  7. Z. Zheng, J. Zhu, and M. R. Lyu, "Service-generated big data and big data-as-a-service: an overview," in Proceedings of 2013 IEEE International Congress on Big Data, Santa Clara, CA, 2013, pp. 403-410.
  8. B. Calder, J. Wang, A. Ogus, N. Nilakantan, A. Skjolsvold, S. McKelvie, et al., "Windows Azure Storage: a highly available cloud storage service with strong consistency," in Proceedings of the 23rd ACM Symposium on Operating Systems Principles, Cascais, Portugal, 2011, pp. 143-157.
  9. S. Sharma, S. Singh, and M. Sharma, "Performance analysis of load balancing algorithms," World Academy of Science, Engineering and Technology, vol. 38, no. 3, pp. 269-272, 2008.
  10. M. Mesbahi and A. M. Rahmani, "Load balancing in cloud computing: a state of the art survey," International Journal of Modern Education and Computer Science, vol. 8, no. 3, pp. 64-78, 2016. https://doi.org/10.5815/ijmecs.2016.03.08
  11. A. Aditya, U. Chatterjee, and S. Gupta, "A comparative study of different static and dynamic load balancing algorithm in cloud computing with special emphasis on time factor," International Journal of Current Engineering and Technology, vol. 5, no. 3, pp. 1898-1907, 2015.
  12. J. Vashistha and A. K. Jayswal, "Comparative study of load balancing algorithms," IOSR Journal of Engineering, vol. 3, no. 3, pp. 45-50, 2013.
  13. R. Lee and B. Jeng, "Load-balancing tactics in cloud," in Proceedings of 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, Beijing, China, 2011, pp. 447-454.
  14. P. Gupta, M. K. Goyal, and P. Kumar, "Trust and reliability based load balancing algorithm for cloud IaaS," in Proceedings of the 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad, India, 2013, pp. 65-69.
  15. O. Sarood, A. Gupta, and L. V. Kale, "Cloud friendly load balancing for HPC applications: preliminary work," in Proceedings of the 41st International Conference on Parallel Processing Workshops, Pittsburgh, PA, 2012, pp. 200-205.
  16. S. C. Wang, K. Q. Yan, W. P. Liao, and S. S. Wang, "Towards a load balancing in a three-level cloud computing network," in Proceedings of the 3rd International Conference on Computer Science and Information Technology, Chengdu, China, 2010, pp. 108-113.
  17. B. Mondal, K. Dasgupta, and P. Dutta, "Load balancing in cloud computing using stochastic hill climbing-a soft computing approach," Procedia Technology, vol. 4, pp. 783-789, 2012. https://doi.org/10.1016/j.protcy.2012.05.128
  18. S. Stattelmann and F. Martin, "On the use of context information for precise measurement-based execution time estimation," in Proceedings of the 10th International Workshop on Worst-Case Execution Time Analysis (WCET), Brussels, Belgium, 2010, pp. 64-76.
  19. G. Xu, J. Pang, and X. Fu, "A load balancing model based on cloud partitioning for the public cloud," Tsinghua Science and Technology, vol. 18, no. 1, pp. 34-39, 2013. https://doi.org/10.1109/TST.2013.6449405
  20. R. Wang, W. Le, and X. Zhang, "Design and implementation of an efficient load-balancing method for virtual machine cluster based on cloud service," in Proceedings of the 4th IET International Conference on Wireless, Mobile & Multimedia Networks (ICWMMN), Beijing, China, 2011, pp. 312-324.
  21. W. Tian, Y. Zhao, Y. Zhong, M. Xu, and C. Jing, "A dynamic and integrated load-balancing scheduling algorithm for Cloud datacenters," in Proceedings of 2011 IEEE International Conference on Cloud Computing and Intelligence Systems, Beijing, China, 2011, pp. 311-315.
  22. F. Ma, F. Liu, and Z. Liu, "Distributed load balancing allocation of virtual machine in cloud data center," in Proceedings of 2012 IEEE International Conference on Computer Science and Automation Engineering, Beijing, China, 2012, pp. 20-23.
  23. S. M. Ghafari, M. Fazeli, A. Patooghy, and L. Rikhtechi, "Bee-MMT: a load balancing method for power consumption management in cloud computing," in Proceedings of the 6th International Conference on Contemporary Computing (IC3), Noida, India, 2013, pp. 76-80.
  24. C. K. Teoh, A. Wibowo, and M. S. Ngadiman, "Review of state of the art for metaheuristic techniques in Academic Scheduling Problems," Artificial Intelligence Review, vol. 44, no. 1, pp. 1-21, 2015. https://doi.org/10.1007/s10462-013-9399-6
  25. K. Nishant, P. Sharma, V. Krishna, C. Gupta, K. P. Singh, and R. Rastogi, "Load balancing of nodes in cloud using ant colony optimization," in Proceedings of 2012 UKSim 14th International Conference on Computer Modelling and Simulation, Cambridge, UK, 2012, pp. 3-8.
  26. E. Ikonomovska, I. Chorbev, D. Gjorgjevik, and D. Mihajlov, "The adaptive tabu search and its application to the quadratic assignment problem," in Proceedings of the 9th International Multi-conference Information Society (IS 2006), Ljubljana, Slovenia, 2006, pp. 26-29.
  27. G. A. E. N. Said, A. M. Mahmoud, and E. S. M. El-Horbaty, "A comparative study of meta-heuristic algorithms for solving quadratic assignment problem," International Journal of Advanced Computer Science and Applications, vol. 5, no. 1, 2014.
  28. F. Neumann and C. Witt, Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity. Heidelberg: Springer, 2010.
  29. X. S. Yang, "A new metaheuristic bat-inspired algorithm," in Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Heidelberg: Springer, 2010, pp. 65-74.
  30. E. H. L. Aarts and P. J. M. van Laarhoven, "Simulated annealing: a pedestrian review of the theory and some applications," in Pattern Recognition Theory and Applications. Heidelberg: Springer, pp. 179-192.
  31. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, "Optimization by simulated annealing," Science, vol. 220, no. 4598, pp. 671-680, 1983. https://doi.org/10.1126/science.220.4598.671
  32. K. L. Du and M. N. S. Swamy, "Simulated annealing," in Search and Optimization by Metaheuristics. Cham: Birkhauser, 2016. pp. 29-36
  33. Y. Fahim, E. B. Lahmar, E. H. Labriji, A. Eddaoui, and S. Ouahabi, "The load balancing improvement of a data center by a hybrid algorithm in cloud computing," in Proceedings of the 3rd IEEE International Colloquium in Information Science and Technology (CIST), Tetouan, Morocco, 2014, pp. 141-144.
  34. Y. Fahim, E. B. Lahmar, E. H. Labriji, and A. Eddaoui, "The tasks allocation based on the pre-estimation of the processing time in the cloud environment," Journal of Theoretical and Applied Information Technology, vol. 75, no. 3, pp. 350-355, 2015.
  35. Y. Fahim, E. B. Lahmar, E. H. Labriji, and A. Eddaoui, "The load balancing based on the estimated finish time of tasks in cloud computing," in Proceedings of the 2nd World Conference on Complex Systems (WCCS), Agadir, Morocco, 2014, pp. 594-598.
  36. Y. Fahim, H. Rahhali, M. Hanine, E. H. Benlahmar, E. H. Labriji, M. Hanoune, and A. Eddaoui, "Load balancing in cloud computing using meta-heuristic algorithm," Journal of Information Processing Systems, vol. 14, no. 3, pp. 569-589, 2018. https://doi.org/10.3745/JIPS.01.0028
  37. S. Roy, S. Banerjee, K. R. Chowdhury, and U. Biswas, "Development and analysis of a three phase cloudlet allocation algorithm," Journal of King Saud University-Computer and Information Sciences, vol. 29, no. 4, pp. 473-483, 2017. https://doi.org/10.1016/j.jksuci.2016.01.003