Viji Vinod; V. J. Chakravarthy; N. Jayashri; Bala Dhandayuthapani V.; Shabeen Taj G. A.; A. V. G. A. Marthanda

Enhancing Energy Efficiency in Cloud Data Centers through Deformable Graph Convolutional Network-Aware Virtual Machine Placement with Hybrid Swarm Bipolar Walk-Spread Optimization

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Author(s)

Viji Vinod ^1,* V. J. Chakravarthy ¹ N. Jayashri ¹ Bala Dhandayuthapani V. ² Shabeen Taj G. A. ³ A. V. G. A. Marthanda ⁴

1. Faculty of Computer Applications, Dr. M. G. R. Educational and Research Institute, Maduravoyal, Chennai, Tamil Nadu 600095, India

2. Department of Information Technology, College of Computing and Information Sciences, University of Technology and Applied Sciences, Shinas campus, Oman

3. Department of Computer Science Engineering, Government Engineering College Ramanagar, B.M. Road, Ramanagara, Karnataka 562159, India

4. Department of Electrical and Electronics Engineering, Lakireddy Balireddy College of Engineering, Mylavaram, Andhra Pradesh, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2026.04.10

Received: 15 Mar. 2025 / Revised: 20 Jul. 2025 / Accepted: 15 Sep. 2025 / Published: 8 Aug. 2026

Index Terms

Deformable Graph Convolutional Network, Google Cluster Dataset, Swarm Bipolar Optimization, Virtual Machine Placement and Walk-Spread Algorithm

Abstract

In big cloud data centers, the best physical machine (PM) is selected using the Virtual Machine Placement (VMP) process. To address this issue, a number of approaches have been proposed. Nevertheless, the existing solutions only take into account a small number of resource categories, which leads to an uneven load and ultimately, the activation of superfluous physical computers within the data center. The aim of this research is to maximize resource usage while lowering power use and carbon footprints by integrating a Hybrid Swarm Bipolar with Walk-Spread Algorithm with a unique Deformable Graph Convolutional Network (DGCN-SB-WSA)-aware virtual machine placement architecture. To ensGoogleent VM scheduling and management, real-world cloud workloads are analyzed using the Google Cluster Dataset (GCD). The Deformable Graph Convolutional Network (DGCN) dynamically models cloud infrastructure as a graph that captures intricate relationships among PMs and VMs, enabling adaptive placement choices. The Hybrid Swarm Bipolar with Walk-Spread Algorithm (SB-WSA) then uses a dual-phase search approach to balance local exploitation with global exploration, minimizing premature convergence and increasing performance while optimizing Virtual machine (VM) allocation. Comparing the proposed method to conventional VM placement techniques, experimental results show that it dramatically 27 KW lowers power consumption, improves 98% resource usage and 180 kg CO₂ decreases carbon emissions.

Cite This Paper

Viji Vinod, V. J. Chakravarthy, N. Jayashri, Bala Dhandayuthapani V., Shabeen Taj G. A., A. V. G. A. Marthanda, "Enhancing Energy Efficiency in Cloud Data Centers through Deformable Graph Convolutional Network-Aware Virtual Machine Placement with Hybrid Swarm Bipolar Walk-Spread Optimization", International Journal of Computer Network and Information Security(IJCNIS), Vol.18, No.4, pp. 187-209, 2026. DOI:10.5815/ijcnis.2026.04.10

Reference

[1]Shanmugam, V., Ling, H.C., Gopal, L., Eswaran, S. and Chiong, C.W., 2024. Network-aware virtual machine placement using enriched butterfly optimisation algorithm in cloud computing paradigm. Cluster Computing, pp.1-19.
[2]Elsedimy, E.I., Herajy, M. and Abohashish, S.M., 2025. Energy and QoS-aware virtual machine placement approach for IaaS cloud datacenter. Neural Computing and Applications, pp.1-27.
[3]Çavdar, M.C., Korpeoglu, I. and Ulusoy, Ö., 2024. A utilization based genetic algorithm for virtual machine placement in cloud systems. Computer Communications, 214, pp.136-148.
[4]Gopu, A., Thirugnanasambandam, K., AlGhamdi, A.S., Alshamrani, S.S., Maharajan, K. and Rashid, M., 2023. Energy-efficient virtual machine placement in distributed cloud using NSGA-III algorithm. Journal of Cloud Computing, 12(1), p.124.
[5]Singh, A.K., Swain, S.R. and Lee, C.N., 2023. A metaheuristic virtual machine placement framework toward power efficiency of sustainable cloud environment. Soft Computing, 27(7), pp.3817-3828.
[6]Sheeba, A. and Uma Maheswari, B., 2023. An efficient fault tolerance scheme based enhanced firefly optimization for virtual machine placement in cloud computing. Concurrency and Computation: Practice and Experience, 35(7), p.e7610.
[7]Shi, F. and Lin, J., 2022. Virtual machine resource allocation optimization in cloud computing based on multiobjective genetic algorithm. Computational Intelligence and Neuroscience, 2022(1), p.7873131.
[8]Balaji, K., Sai Kiran, P. and Sunil Kumar, M., 2023. Power aware virtual machine placement in IaaS cloud using discrete firefly algorithm. Applied Nanoscience, 13(3), pp.2003-2011.
[9]Wang, N., Osmani, A. and Mirzaei, S., 2022. Dynamic placement of virtual machines using an improved multi‐objective teaching‐learning based optimization algorithm in cloud. Transactions on Emerging Telecommunications Technologies, 33(9), p.e4529.
[10]Shirvani, M.H., 2023. An energy-efficient topology-aware virtual machine placement in cloud datacenters: a multi-objective discrete jaya optimization. Sustainable Computing: Informatics and Systems, 38, p.100856.
[11]Nabavi, S.S., Gill, S.S., Xu, M., Masdari, M. and Garraghan, P., 2022. TRACTOR: Traffic‐aware and power‐efficient virtual machine placement in edge‐cloud data centers using artificial bee colony optimization. International Journal of Communication Systems, 35(1), p.e4747.
[12]Ghasemi, A., Toroghi Haghighat, A. and Keshavarzi, A., 2024. Enhancing virtual machine placement efficiency in cloud data centers: a hybrid approach using multi-objective reinforcement learning and clustering strategies. Computing, 106(9), pp.2897-2922.
[13]Gabhane, J.P., Pathak, S. and Thakare, N., 2024. An improved multi-objective eagle algorithm for virtual machine placement in cloud environment. Microsystem Technologies, 30(5), pp.489-501.
[14]Nabavi, S., Wen, L., Gill, S.S. and Xu, M., 2023. Seagull optimization algorithm based multi-objective VM placement in edge-cloud data centers. Internet of Things and Cyber-Physical Systems, 3, pp.28-36.
[15]Belgacem, A., Mahmoudi, S. and Ferrag, M.A., 2023. A machine learning model for improving virtual machine migration in cloud computing. The Journal of Supercomputing, 79(9), pp.9486-9508.
[16]Singh, A.K., Swain, S.R., Saxena, D. and Lee, C.N., 2023. A bio-inspired virtual machine placement toward sustainable cloud resource management. IEEE Systems Journal, 17(3), pp.3894-3905.
[17]Karmakar, K., Banerjee, S., Das, R.K. and Khatua, S., 2022. Utilization aware and network I/O intensive virtual machine placement policies for cloud data center. Journal of Network and Computer Applications, 205, p.103442.
[18]Swain, S.R., Parashar, A., Singh, A.K. and Lee, C.N., 2025. An intelligent virtual machine allocation optimization model for energy-efficient and reliable cloud environment. The Journal of Supercomputing, 81(1), pp.1-26.
[19]Saxena, D. and Singh, A.K., 2022. A high availability management model based on VM significance ranking and resource estimation for cloud applications. IEEE Transactions on Services Computing, 16(3), pp.1604-1615.
[20]Aghasi, A., Jamshidi, K. and Bohlooli, A., 2022. A thermal-aware energy-efficient virtual machine placement algorithm based on fuzzy controlled binary gravitational search algorithm (FC-BGSA). Cluster Computing, pp.1-19.
[21]Swain, S.R., Parashar, A., Singh, A.K. and Lee, C.N., 2024. Efficient straggler task management in cloud environment using stochastic gradient descent with momentum learning-driven neural networks. Cluster Computing, 27(4), pp.4673-4685.
[22]Singh, G., Sengupta, P., Mehta, A. and Bedi, J., 2024. A feature extraction and time warping based neural expansion architecture for cloud resource usage forecasting. Cluster Computing, pp.1-20.
[23]Kumar, J., Saxena, D., Kumar, J., Bhadoria, A., Anjali, A. and Singh, A.K., 2025. A hybrid neural network and cooperative PSO model for dynamic cloud workloads prediction. Computing, 107(3), pp.1-48.
[24]Park, J., Yoo, S., Park, J. and Kim, H.J., 2022, June. Deformable graph convolutional networks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 7, pp. 7949-7956).
[25]Kusuma, P.D. and Dinimaharawati, A., 2024. Swarm Bipolar Algorithm: A Metaheuristic Based on Polarization of Two Equal Size Sub Swarms. International Journal of Intelligent Engineering & Systems, 17(2).
[26]Kusuma, P.D. and Prasasti, A.L., 2023. Walk-Spread Algorithm: A Fast and Superior Stochastic Optimization. International Journal of Intelligent Engineering & Systems, 16(5).

International Journal of Computer Network and Information Security (IJCNIS)