An Energy Efficient Particle Swarm Optimization based VM Allocation for Cloud Data Centre: EEVMPSO


  • Abhishek Kumar Pandey Madan Mohan Malaviya University of Technology image/svg+xml
  • Sarvpal Singh Madan Mohan Malaviya University of Technology image/svg+xml



Particle Swarm Optimization (PSO), Cloud computing, cloud data center, virtual machine placement, service level agreements


Virtual Machine (VM) allocation are the crucial problems because cloud computing enables the rapid growth of data centres and compute centres. Power consumption and network expenses have increased as cloud computing becomes more and more prevalent. System instability may result from repeated requests for computing resources. One of the most important and difficulties facing virtualization technology is finding the best way to stack virtual machines on top of physical machines in cloud data centres. The host must move virtual machines from overloaded to underloaded hosts as part of load balancing, which has an impact on energy consumption. The proposed energy efficient particle swarm optimization algorithm (EEVMPSO) for Virtual Machine allocation to maximize the load balancing. System resources including CPU, storage, and memory are optimized using EEVMPSO. This research article suggests energy-aware virtual machine migration using the Particle Swarm Optimization Algorithm for dynamic VMs placement, energy efficient cloud data centres as a solution to this issue. The experimental result shown in the proposed method, consumption energy in comparison to the PAPSO, KHA, EALBPSO, and RACC-MDT algorithm by 10.86%, 18.22%, 25.8%, and 31.34% respectively, it demonstrated the improvements in the energy service level agreements violation 5.77%, 15.3%, 26.19%, and 30.4%, as well as the average CPU utilization 2.2%, 24%, 22.6%, and 14.6%.



Garg, H., ‘‘A hybrid PSO-GA algorithm for constrained optimization problems,’’ Appl. Math. Comput., 2016, vol. 274, pp. 292–305.

Ding W, Luo F, Han L, Gu C, Lu H, Fuentes J. "Adaptive virtual machine consolidation framework based on performance-to-power ratio in cloud data centers." Future Generation Computer Systems, 2020, vol.111, pp. 254-270.

Patwal, R. S., N. Narang, and H. Garg, ''A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units,'' energy, 2018, vol. 142, pp. 822–837.

Braiki, K., Youssef, H.: Multi-objective virtual machine placement algorithm based on particle swarm optimization. In 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), IEEE, 2018, pp. 279–284.

M. Xu, W. Tian, and R. Buyya, ‘‘A survey on load balancing algorithms for virtual machines placement in cloud computing,’’ Concurrency Comput., Pract. Exper., 2017, vol. 29(12), e4123.

Sun, G., Liao, D., Anand, V., Zhao, D., Yu, H.: A new technique for efficient live migration of multiple virtual machines. Future Gener. Comput. Syst. 2016, vol. 55, pp. 74–86.

Yan, J., Zhang, H., Xu, H., Zhang, Z.: Discrete PSO-based workload optimization in virtual machine placement. Pers. Ubiquit. Comput., 2018, vol. 22(3), pp. 589–596.

Addya, S.K., Turuk, A.K., Sahoo, B., Sarkar, M., Biswash, S.K.: Simulated annealing based VM placement strategy to maximize the profit for Cloud Service Providers. Eng. Sci. Technol. Int. J., 2017, vol. 20(4), pp. 1249–1259.

Gharehpasha, S., Masdari, M., Jafarian, A.: The placement of virtual machines under optimal conditions in cloud datacenter. Inform. Technol. Control, 2019, vol. 48(4), pp. 545–556.

Masdari, M., Khoshnevis, A.: A survey and classification of the workload forecasting methods in cloud computing. Clust. Comput., 2020, vol. 23(4), pp. 2399–2424.

Masdari, M., Zangakani, M.: Efficient task and workflow scheduling in inter-cloud environments: challenges and opportunities. J. Supercomput., 2020, vol. 76(1), pp. 499–535.

Soltanshahi, Minoo, Reza Asemi, and Nazi Shafiei. "Energy-aware virtual machines allocation by krill herd algorithm in cloud data centers." Heliyon 2019, Vol. 5(7), e02066.

Shabeera, T., Kumar, S.M., Salam, S.M., Krishnan, K.M.: Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithm. Eng. Sci. Technol. Int. J., 2017, vol. 20(2), pp. 616–628.

Liu, X.-F., Zhan, Z.-H., Deng, J.D., Li, Y., Gu, T., Zhang, J.: An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Trans. 2016, vol. 22(1), pp.113–128.

Masdari, M., Zangakani, M.: Green cloud computing using proactive virtual machine placement: challenges and issues. J. Grid Comput, 2020, vol. 18(4), pp. 727–759.

Masdari, M., Gharehpasha, S., Ghobaei-Arani, M., Ghasemi, V.: Bio-inspired virtual machine placement schemes in cloud computing environment: taxonomy, review, and future research directions. Clust. Comput., 2020, vol. 23.4, pp.2533-2563.

S.Y. Hsieh, C.-S. Liu, R. Buyya, and A. Y. Zomaya, ‘‘Utilization prediction-aware virtual machine consolidation approach for energy efficient cloud data centers,’’ J. Parallel Distrib. Comput., 2020, vol. 139, pp. 99–109.

Shadravan, S., Naji, H., Bardsiri, V.K.: The Sailfish Optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng. Appl. Artif. Intell., 2019, vol. 80, pp. 20–34.

Maciel, O., Cuevas, E., Navarro, M.A., Zaldıvar, D., Hinojosa, S.: Side-blotched lizard algorithm: a polymorphic population approach. Appl. Soft Comput., 2020, vol. 88, pp. 106039.

Masoudi, Javad, Behnam Barzegar, and Homayun Motameni. "Energy-aware virtual machine allocation in DVFS-enabled cloud data centers." IEEE Access, 2021, vol. 10, pp. 3617-3630.

Sayantani Basu, G. Kannayaram, Somula Ramasubbareddy, C. Venkatasubbaiah, Improved genetic algorithm for monitoring of virtual machines in cloud environment, in: S.C. Satapathy, et al. (Eds.), Smart Intelligent Computing and Applications, Smart Innovation, Systems and Technologies, 105, Springer Nature Singapore Pte Ltd, 2019, vol. 2, pp. 319-326.

Fares Alharbi, Yu-Chu Tian, Maolin Tang, Wei-Zhe Zhang, Chen Peng, Minrui Fei, An Ant colony system for energy-efþcient dynamic virtual machine placement in data centers, Exp. Sys. Appl., 2019, vol. 120, pp. 228-238.

Xinqian Zhang, Tingming Wu, Mingsong Chen, Tongquan Wei, Junlong Zhou, Shiyan Hu, Rajkumar Buyya, Energy-aware virtual machine allocation for cloud with resource reservation, J. Syst. Softw., 2019, vol. 147, pp. 147–161.

Donyagard Vahed, M. Ghobaei-Arani, and A. Souri, “Multi-objective virtual machine placement mechanisms using nature-inspired metaheuristic algorithms in cloud environments: A comprehensive review,” in Proc. Int. J. Commun. Syst., 2019, vol. 32(14), e. 4068.

Singh, A. K., and J. Kumar, “Secure and energy aware load balancing framework for cloud data centre networks,” Electron. Lett., 2019, vol. 55, pp. 540–541.

Tseng, F.-H., X. Wang, L.-D. Chou, H.-C. Chao, and V. C. Leung, “Dynamic resource prediction and allocation for cloud data center using the multi-objective genetic algorithm,” IEEE Syst. J., 2017, vol. 12(2), pp. 1688–1699.

Saxena, D., and A. K. Singh, “A proactive autoscaling and energy-efficient vm allocation framework using online multi-resource neural network for cloud data center,” Neurocomputing, 2021, vol. 426, pp. 248–264.

Sharma, N. K., and G. R. M. Reddy, “Multi-objective energy efficient virtual machines allocation at the cloud data center,” IEEE Trans. Services Comput., 2016, vol. 12(1), pp. 158–171.

Shun Yao, G., Y. Sheng Ding and K. Rong Hao, “Multi objective workflow scheduling in cloud system based on cooperative multi-swarm optimization algorithm,” Journal of Central South University, 2017, vol. 24(5), pp. 1050– 1062.

Dashti, S. E., and A. M. Rahmani, ``Dynamic VMs placement for energy efficiency by PSO in cloud computing,'' J. Experim. heor. Artif. Intell., 2016, vol. 28(1), pp. 97-112.

Tharwat, A., Elhoseny, M., Hassanien, A.E., Gabel, T., Kumar, A.: Intelligent Bezier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm. Clust. Comput., 2019, vol. 22(2), pp. 4745–4766.

Beloglazov, A., and R. Buyya, ``Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers,'' Concurrency Comput., Pract. Exper., 2012, vol. 24(13), pp. 1397-1420.

Ibrahim, Abdelhameed, et al. "PAPSO: A power-aware VM placement technique based on particle swarm optimization." IEEE Access, 2020, vol., 8 pp. 81747-81764.

Magotra, Bhagyalakshmi, and Deepti Malhotra. "Resource-Efficient VM Placement in the Cloud Environment Using Improved Particle Swarm Optimization." International Journal of Applied Metaheuristic Computing, 2022, vol.13(1), pp. 1-32.

Gomathi, B., et al. "Multi-Objective Optimization of Energy Aware Virtual Machine Placement in Cloud Data Center." INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2022, vol. 33.3, pp. 1771-1785.

Al-Moalmi, A., et al.: A whale optimization system for energy efficient container placement in data centers. Expert Syst., 2021, vol. 164, pp. 113719.

Liu, X.-F., et al., An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Transactions on Evolutionary Computation, 2016, vol. 22(1): pp. 113-128.

Li, Z., et al.: Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method. Future Gener. Comput. Syst., 2018, vol. 80, pp. 139–156.

Kim, M., Hong, J., Kim, W.: An efficient representation using harmony search for solving the virtual machine consolidation. Sustainability,2019, vol.11(21), pp. 6030.

Riahi, M., Krichen, S.: A multi-objective decision support framework for virtual machine placement in cloud data centers: a real case study. J. Supercomput, 2018, vol. 74(7), pp. 2984–3015.

Gharehpasha, Sasan, Mohammad Masdari, and Ahmad Jafarian. "Power efficient virtual machine placement in cloud data centers with a discrete and chaotic hybrid optimization algorithm." Cluster Computing, 2021, vol. 24, pp. 1293-1315.

Yavari, M., Rahbar, A.G., Fathi, M.H.: Temperature and energy aware consolidation algorithms in cloud computing. J. Cloud Comput., 2019, vol. 8(1), pp. 1-16.

Wang C, Sun B, Du KJ, Li JY, Zhan ZH, Jeon SW, Wang H, Zhang J. ‘‘A novel evolutionary algorithm with column and sub-block local search for sudoku puzzles”. IEEE Transactions on Games., 2023.

Yang JQ, Yang QT, Du KJ, Chen CH, Wang H, Jeon SW, Zhang J, Zhan ZH. "Bi-Directional Feature Fixation-based Particle Swarm Optimization for Large-Scale Feature Selection”. IEEE Transactions on Big Data. 2022.

Ge YF, Zhan ZH, Cao J, Wang H, Zhang Y, Lai KK, Zhang J. DSGA: "a distributed segment-based genetic algorithm for multi-objective outsourced database partitioning”. Information Sciences. 2022, Vol. 612, pp. 864-86.

Li JY, Du KJ, Zhan ZH, Wang H, Zhang J. "Distributed differential evolution with adaptive resource allocation”. IEEE transactions on cybernetics. 2022.

Ge YF, Orlowska M, Cao J, Wang H, Zhang Y. "MDDE multitasking distributed differential evolution for privacy-preserving database fragmentation”. The VLDB Journal. 2022, Vol.31(5), pp. 957-75.




How to Cite

Pandey AK, Singh S. An Energy Efficient Particle Swarm Optimization based VM Allocation for Cloud Data Centre: EEVMPSO. EAI Endorsed Scal Inf Syst [Internet]. 2023 Aug. 1 [cited 2024 Jul. 22];10(5). Available from: