Hybrid Fuzzy Metaheuristic Technique for Efficient VM Selection and Migration in Cloud Data Centers
Abstract
The rapid growth in cloud computing has made it essential to maintain Quality of Service (QoS) across varying workloads. Virtual machine (VM) migration plays a pivotal role in enhancing service efficiency by effectively managing resources. Yet, frequent VM migrations can lead to higher energy use and reduced performance. To tackle these issues, a novel Fuzzy-based Hybrid Optimization Algorithm, FCSFFC, which combines Fuzzy Cuckoo Search with Fuzzy Firefly Colony Optimization has been developed. The proposed method strives to reduce power usage, resource waste, computation time, and migration expenses. Simulations validate our method's superiority over existing techniques in meeting our goals. Fuzziness in the algorithm helps manage the uncertainty and vagueness in predicting workloads and resource allocation, thus making the system more flexible and robust. With fuzzy logic, our model dynamically fine-tunes migration plans according to live conditions, improving the precision of decisions. The FCSFFC model, with its dynamic threshold-assisted load prediction, has been shown to minimize performance decline, downtime, and the number of VM migrations.
Full Text:
PDFReferences
Khan, M. S., and R. Santhosh. “Hybrid Optimization Algorithm for VM Migration in Cloud Computing.” Computers and Electrical Engineering, vol. 102, 2022, p. 108152.
Sait, S. M., A. Bala, and A. H. El-Maleh. “Cuckoo Search Based Resource Optimization of Datacenters.” Applied Intelligence, vol. 44, Apr. 2016, pp. 489-506.
Chamas, N., F. López-Pires, and B. Baran. “Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation Under Uncertainty.” 2017 XLIII Latin American Computer Conference (CLEI), 4 Sept. 2017, pp. 1-10. IEEE.
Chamas, N., F. López-Pires, and B. Baran. “Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation Under Uncertainty.” 2017 XLIII Latin American Computer Conference (CLEI), 4 Sept. 2017, pp. 1-10. IEEE.
Hosseini Shirvani, M. “Bi-Objective Web Service Composition Problem in Multi-Cloud Environment: A Bi-Objective Time-Varying Particle Swarm Optimisation Algorithm.” Journal of Experimental & Theoretical Artificial Intelligence, vol. 33, no. 2, 4 Mar. 2021, pp. 179-202.
Rukmini, S., and S. Shridevi. “An Optimal Solution to Reduce Virtual Machine Migration SLA Using Host Power.” Measurement: Sensors, vol. 25, 1 Feb. 2023, p. 100628.
Al-Mahruqi, A. A., G. Morison, B. G. Stewart, and V. Athinarayanan. “Hybrid Heuristic Algorithm for Better Energy Optimization and Resource Utilization in Cloud Computing.” Wireless Personal Communications, vol. 118, May 2021, pp. 43-73.
Rashida, S. Y., M. Sabaei, M. M. Ebadzadeh, and A. M. Rahmani. “A Memetic Grouping Genetic Algorithm for Cost Efficient VM Placement in Multi-Cloud Environment.” Cluster Computing, vol. 23, June 2020, pp. 797-836.
Sonklin, C., and K. Sonklin. “A Multi-Objective Grouping Genetic Algorithm for Server Consolidation in Cloud Data Centers.” 2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE), 28 June 2023, pp. 421-426. IEEE.
Zhang, B., X. Wang, and H. Wang. “Virtual Machine Placement Strategy Using Cluster-Based Genetic Algorithm.” Neurocomputing, vol. 428, 7 Mar. 2021, pp. 310-316.
Wang, X., H. Lou, Z. Dong, C. Yu, and R. Lu. “Decomposition-Based Multi-Objective Evolutionary Algorithm for Virtual Machine and Task Joint Scheduling of Cloud Computing in Data Space.” Swarm and Evolutionary Computation, vol. 77, 1 Mar. 2023, p. 101230.
Gharehpasha, S., and M. Masdari. “A Discrete Chaotic Multi-Objective SCA-ALO Optimization Algorithm for an Optimal Virtual Machine Placement in Cloud Data Center.” Journal of Ambient Intelligence and Humanized Computing, vol. 12, Oct. 2021, pp. 9323-9339.
Perumal, B., M. Aramudhan, and R. K. Saravanaguru. “Fuzzy Bio-Inspired Hybrid Techniques for Server Consolidation and Virtual Machine Placement in Cloud Environment.” Cybernetics and Information Technologies, vol. 17, no. 4, Nov. 2017, pp. 52-68.
Gaggero, M., and L. Caviglione. “Model Predictive Control for Energy-Efficient, Quality-Aware, and Secure Virtual Machine Placement.” IEEE Transactions on Automation Science and Engineering, vol. 16, no. 1, 7 May 2018, pp. 420-432.
Sharma, S., S. Kumar, S. Mohapatra, and R. Rani. “Discrete Gravitational Search Algorithm for Virtual Machine Placement in Cloud Computing.” International Journal of Advanced Science and Technology, vol. 29, no. 8s, Apr. 2020, pp. 1261-1277.
Wang, M., and G. Lu. “A Modified Sine Cosine Algorithm for Solving Optimization Problems.” IEEE Access, vol. 9, 9 Feb. 2021, pp. 27434-27450.
Xing, H., J. Zhu, R. Qu, P. Dai, S. Luo, and M. A. Iqbal. “An ACO for Energy-Efficient and Traffic-Aware Virtual Machine Placement in Cloud Computing.” Swarm and Evolutionary Computation, vol. 68, 1 Feb. 2022, p. 101012.
Chalabi, N. E., A. Attia, A. Bouziane, and M. Hassaballah. “An Improved Marine Predator Algorithm Based on Epsilon Dominance and Pareto Archive for Multi-Objective Optimization.” Engineering Applications of Artificial Intelligence, vol. 119, 1 Mar. 2023, p. 105718.
Abdel-Basset, M., R. Mohamed, and S. Mirjalili. “A Novel Whale Optimization Algorithm Integrated with Nelder–Mead Simplex for Multi-Objective Optimization Problems.” Knowledge-Based Systems, vol. 212, 5 Jan. 2021, p. 106619.
Ding, Z., L. Cao, L. Chen, D. Sun, X. Zhang, and Z. Tao. “Large-Scale Multimodal Multiobjective Evolutionary Optimization Based on Hybrid Hierarchical Clustering.” Knowledge-Based Systems, vol. 266, 22 Apr. 2023, p. 110398.
Xiang, Z., G. Zhou, Y. Zhou, and Q. Luo. “Golden Sine Cosine Salp Swarm Algorithm for Shape Matching Using Atomic Potential Function.” Expert Systems, vol. 39, no. 2, Feb. 2022, p. e12854.
Singh, N., L. H. Son, F. Chiclana, and J. P. Magnot. “A New Fusion of Salp Swarm with Sine Cosine for Optimization of Non-Linear Functions.” Engineering with Computers, vol. 36, Jan. 2020, pp. 185-212.
Salami, H. O., A. Bala, S. M. Sait, and I. Ismail. “An Energy-Efficient Cuckoo Search Algorithm for Virtual Machine Placement in Cloud Computing Data Centers.” The Journal of Supercomputing, vol. 77, no. 11, Nov. 2021, pp. 13330-13357.
Farshin, A., and S. Sharifian. “A Modified Knowledge-Based Ant Colony Algorithm for Virtual Machine Placement and Simultaneous Routing of NFV in Distributed Cloud Architecture.” The Journal of Supercomputing, vol. 75, 1 Aug. 2019, pp. 5520-5550.
Sharma, S. K., and W. Ghai. “Artificial Bee Colony Optimized VM Migration and Allocation Using Neural Network Architecture.” International Journal of Advanced Technology and Engineering Exploration, vol. 10, no. 102, 1 May 2023, pp. 590.
Patra, M. K., S. Misra, B. Sahoo, and A. K. Turuk. “GWO-Based Simulated Annealing Approach for Load Balancing in Cloud for Hosting Container as a Service.” Applied Sciences, vol. 12, no. 21, 2 Nov. 2022, p. 11115.
Vijaya, C., and P. Srinivasan. “Multi-Objective Meta-Heuristic Technique for Energy Efficient Virtual Machine Placement in Cloud Data Centers.” Informatica, vol. 48, no. 6, 27 Feb. 2024.
Gupta, N., K. Gupta, A. M. Qahtani, D. Gupta, F. S. Alharithi, A. Singh, and N. Goyal. “Energy-Aware Live VM Migration Using Ballooning in Cloud Data Center.” Electronics, vol. 11, no. 23, 28 Nov. 2022, p. 3932.
Pande, S. K., S. K. Panda, S. Das, K. S. Sahoo, A. K. Luhach, N. Z. Jhanjhi, R. Alroobaea, and S. Sivanesan. “A Resource Management Algorithm for Virtual Machine Migration in Vehicular Cloud Computing.” Computers, Materials & Continua, vol. 67, no. 2, 1 May 2021.
Gupta, A., and S. Namasudra. “A Novel Technique for Accelerating Live Migration in Cloud Computing.” Automated Software Engineering, vol. 29, no. 1, May 2022, p. 34.
Kaur, A., S. Kumar, D. Gupta, Y. Hamid, M. Hamdi, A. Ksibi, H. Elmannai, and S. Saini. “Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm.” Sensors, vol. 23, no. 13, 3 July 2023, p. 6117.
Booba, B., X. J. Anitha, C. Mohan, and S. Jeyalaksshmi. “Hybrid Approach for Virtual Machine Allocation in Cloud Computing.” Sustainable Computing: Informatics and Systems, vol. 41, 1 Jan. 2024, p. 100922.
Aydilek, Ibrahim Berkan. “A Hybrid Firefly and Particle Swarm Optimization Algorithm for Computationally Expensive Numerical Problems.” Applied Soft Computing, vol. 66, 2018, pp. 232-249.
Thirugnanasambandam, K., R. Rajkumar, A. S. Alghamdi, S. S. Alshamrani, K. Maharajan, and M. Rashid. “Energy Efficient Virtual Machine Placement in Distributed Cloud Using NGSA III Algorithm.” Journal of Cloud Computing, vol. 12, 2023, p. 124.
Basset, M. A., L. Abdle-Fatah, and A. K. Sangaiah. “An Improved Levy Based Whale Optimization Algorithm for Bandwidth-Efficient Virtual Machine Placement in Cloud Computing Environment.” Cluster Computing, vol. 22, Jan. 2018, pp. 8319-8334.
Bhatt, C., and S. Singhal. “Hybrid Metaheuristic Technique for Optimization of Virtual Machine Placement in Cloud.” International Journal of Fuzzy Logic and Intelligent Systems, vol. 23, no. 3, Sept. 2023, pp. 353-364.
Azizi, S., M. Zandsalimi, and D. Li. “An Energy Efficient Algorithm for Virtual Machine Placement Optimization in Cloud Data Centers.” Cluster Computing, vol. 23, Mar. 2020, pp. 3421-3434.
Gopu, A., and N. Venkataraman. “Virtual Machine Placement Using Multi-Objective Bat Algorithm with Decomposition in Distributed Cloud: MOBA/D for VMP.” Applied Metaheuristic Computing, vol. 12, no. 4, Oct. 2021, pp. 62-77.
Boominathan, P., and M. Aramudan. “A Firefly Colony and Its Fuzzy Approach for Server Consolidation and Virtual Machine Placement in Cloud Datacenters.” Advances in Fuzzy Systems, vol. 2016, Article ID: 6734161, pp. 1-15. https://doi.org/10.1155/2016/6734161.
Govardhan, P., and P. Srinivasan. “Multilevel Controller‐Assisted Intrinsically Modified Ant Colony Optimization Heuristic‐Based Load‐Balancing Model for Mega Cloud Infrastructures.” International Journal of Communication Systems, vol. 35, no. 6, Jan. 2022, DOI: 10.1002/dac.5091.
Dhal, K. G., A. Das, and J. Galvez. “A Novel Fuzzy Logic-Based Improved Cuckoo Search Algorithm.” International Journal of Applied Metaheuristic Computing, vol. 13, no. 1. https://orcid.org/0000-0002-6748-0569.
Gu, H., J. Wang, J. Yu, D. Wang, B. Li, X. He, and X. Yin. “Towards Virtual Machine Scheduling Research Based on Multi-Decision AHP Method in Cloud Computing Platform.” PeerJ Computer Science, Nov. 2023, p. 22, DOI: 10.7717/peerj-cs.1675.
Mukhija, L., and R. Sachdeva. “An Optimal Cuckoo Search Algorithm for VM Selection for Energy Efficient Migration in Cloud Computing.” Eur. Chem. Bull., vol. 12, no. 7, 2023, pp. 1596-1607.
SeyyedSalehi, S. M., and M. M. Khansari. “Virtual Machine Placement Optimization for Big Data Applications in Cloud Computing.” IEEE Access, vol. 10, 2022, pp. 96112-96127. https://doi.org/10.1109/ACCESS.2022.3203057.
Liu, P., and S. Zhang. “A Novel Cuckoo Search Algorithm and Its Application.” Applied Sciences, vol. 11, pp. 1071-1081.
Park, K., and V. S. Pai. “CoMon: A Mostly-Scalable Monitoring System for PlanetLab.” ACM SIGOPS Operating Systems Review, vol. 40, no. 1, 2006, pp. 65-74.
Vijaya, C., and P. Srinivasan. “A Hybrid Technique for Server Consolidation in Cloud Computing Environment.” Cybernetics and Information Technologies, 27 Mar. 2020, vol. 20, issue 1, pp. 36-52.
DOI: https://doi.org/10.31449/inf.v48i20.6549
This work is licensed under a Creative Commons Attribution 3.0 License.