The redistribution of virtualized computing and communication resources in data centers is a significant problem in the context of cloud technologies, making it difficult to ensure the stable functioning of services. These services must meet the criteria for quality of service, performance evaluation, and terms of service contracts imposed by cloud service providers. The main goal of the redistribution of virtualized computing and communication resources is the optimal placement of a subset of active virtual machines on a minimum number of physical machines, taking into account their multidimensional needs for computing and communication resources. Which will significantly improve the efficiency of a virtualized data center. The problem of redistributing computing and communication resources of a data processing center falls under the class of problems defined as "NP-hard" problems, since it involves a vast space of solutions. Therefore, more time is needed to find the optimal option. In previous studies of a number of such problems, it has been proven that metaheuristic strategies make it possible to find acceptable solutions in a suitable time. The article proposes to use a modified version of the ant colony metaheuristic algorithm to solve the problem of redistributing computing and communication resources between virtual machines of a data processing center, considered within the framework of the multidimensional vector packaging problem.
1. Бумажкина Н.Ю., Захарова И.Н., Кочкуров А.Е. К вопросу об использовании технологий живой миграции виртуальных машин в задаче оптимизации ресурсов центра обработки данных. В сборнике: Современные проблемы информатизации в области анализа и синтеза технологических и телекоммуникационных систем: Сборник статей по материалам 29-й международной открытой научной конференции, ноябрь 2023 года – январь 2024 года, Воронеж, Россия. Воронеж: Научная книга; 2024. С. 133–137.
2. Choudhary A., Govil M.Ch., Singh G., Awasthi L.K., Pilli E.S., Kapil D. A critical survey of live virtual machine migration techniques. Journal of Cloud Computing. 2017;6(1). https://doi.org/10.1186/s13677-017-0092-1
3. Mishra M., Sahoo A. On Theory of VM Placement: Anomalies in Existing Methodologies and Their Mitigation Using a Novel Vector Based Approach. In: 2011 IEEE 4th International Conference on Cloud Computing, 04-09 July 2011, Washington, DC, USA. IEEE; 2011. pp. 275–282. https://doi.org/10.1109/CLOUD.2011.38
4. Li X., Qian Z., Lu S., Wu J. Energy efficient virtual machine placement algorithm with balanced and improved resource utilization in a data center. Mathematical and Computer Modelling. 2013;58(5-6):1222–1235. https://doi.org/10.1016/j.mcm.2013.02.003
5. Murtazaev A., Oh S. Sercon: Server Consolidation Algorithm using Live Migration of Virtual Machines for Green Computing. IETE Technical Review. 2011;28(3):212–231.
6. Shen H., Chen L. A Resource Usage Intensity Aware Load Balancing Method for Virtual Machine Migration in Cloud Datacenters. IEEE Transactions on Cloud Computing. 2020;8(1):17–31. https://doi.org/10.1109/TCC.2017.2737628
7. Marzolla M., Babaoglu O., Panzieri F. Server consolidation in Clouds through gossiping. In: 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, 20-24 June 2011, Lucca, Italy. IEEE; 2021. pp. 1–6. https://doi.org/10.1109/WoWMoM.2011.5986483
8. Feller E., Morin C., Esnault A. A case for fully decentralized dynamic VM consolidation in clouds. In: 4th IEEE International Conference on Cloud Computing Technology and Science: Proceedings, 03-06 December 2012, Taipei, Taiwan. IEEE; 2012. pp. 26–33. https://doi.org/10.1109/CloudCom.2012.6427585
9. Caprara A., Toth P. Lower bounds and algorithms for the 2-dimensional vector packing problem. Discrete Applied Mathematics. 2001;111(3):231–262. https://doi.org/10.1016/S0166-218X(00)00267-5
10. Panteleev A.V. Metaheuristic algorithms for finding the global extremum. Moscow: Izdatel'stvo MAI-Print; 2009. 160 p. (In Russ.).
11. Blum Ch., Roli A. Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys. 2003;35(3):268–308. https://doi.org/10.1145/937503.937505
12. Reeves C.R. Modern Heuristic Techniques for Combinatorial Problems. New York: John Wiley & Sons, Inc.; 1993. 320 p.
13. Glover F.W., Kochenberger G.A. Handbook of Metaheuristics. Dordrecht: Kluwer Academic Publishers; 2003. 557 p. https://doi.org/10.1007/b101874
14. Nesmachnow S. An overview of metaheuristics: accurate and efficient methods for optimisation. International Journal of Metaheuristics. 2014;3(4):320–347. https://doi.org/10.1504/ijmheur.2014.068914
15. Alekseev V.E., Zakharova D.V. Teoriya grafov. Nizhny Novgorod: National Research Lobachevsky State University of Nizhny Novgorod; 2012. 57 p. (In Russ.).
16. Reeves C. Hybrid genetic algorithms for bin-packing and related problems. Annals of Operations Research. 1996;63(3):371–396. https://doi.org/10.1007/BF02125404
17. Kaaouache M.A., Bouamama S. Solving bin Packing Problem with a Hybrid Genetic Algorithm for VM Placement in Cloud. Procedia Computer Science. 2015;60:1061–1069. https://doi.org/10.1016/j.procs.2015.08.151
18. Junjie P., Dingwei W. An Ant Colony Optimization Algorithm for Multiple Travelling Salesman Problem. In: First International Conference on Innovative Computing, Information and Control (ICICIC'06): Volume I, 30 August 2006 - 01 September 2006, Beijing, China. IEEE; 2006. pp. 210–213. https://doi.org/10.1109/ICICIC.2006.40
19. Dorigo M., Stützle T. The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances. In: Handbook of Metaheuristics. Dordrecht: Kluwer Academic Publishers; 2003. pp. 250–285. https://doi.org/10.1007/0-306-48056-5_9
20. Dorigo M., Birattari M., Stutzle T. Ant colony optimization. IEEE Computational Intelligence Magazine. 2006;1(4):28–39. https://doi.org/10.1109/mci.2006.329691
Bumazhkina Natalya Yur'evna
The Federal Guard Service Academy
Oryol, Russia
