Novel Framework: Meta-Heuristic Elastic Scheduling Approach in Virtual Machine Selection & Migration

Novel Framework: Meta-Heuristic Elastic Scheduling Approach in Virtual Machine Selection & Migration

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© 2023 by IJETT Journal
Volume-71 Issue-4
Year of Publication : 2023
Author : K. Tuli, M. Malhotra
DOI : 10.14445/22315381/IJETT-V71I4P237

How to Cite?

K. Tuli, M. Malhotra , "Novel Framework: Meta-Heuristic Elastic Scheduling Approach in Virtual Machine Selection & Migration," International Journal of Engineering Trends and Technology, vol. 71, no. 4, pp. 436-452, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I4P237

Abstract
Virtualization is a powerful technique that allows numerous applications can execute on a single cloud server. The process is carried out by cramming software into Virtual Machines (VMs), so that many programs may execute in parallel which leads to an increase in speed. It reduces the overall cost of the cloud data centers by applying migration, and load balancing techniques on the virtual machines. However, the associated energy consumption and Service Level Agreement (SLA) breaches have been extremely high because of increased network traffic and the bandwidth requirements of the applications. To address this issue, the current study presented a novel approach based on the food selection technique used by honey bees to allocate and utilize resources to the VMs. The proposed Optimal Meta-Heuristic Elastic Scheduling (OMES) integrates the Artificial Bee Colony algorithm with flower pollination to select VMs for specific clusters. The simulation is applied on 1000 VMs and analyzed based on VM migration, energy consumption, and SLA violation performance metrics. The comparative analysis performed against existing studies demonstrates highest unit improvement of 0.47 for VM migrations, 0.485 for power consumption, and 0.305 for SLA-V.

Keywords
Artificial Bee Colony (ABC), Cloud Computing, Energy Consumption, Service Level Agreements (SLAs), Virtual Machine (VM).

References
[1] Maede Yavari, Akbar Ghaffarpour Rahbar, and Mohammad Hadi Fathi, “Temperature and Energy-aware Consolidation Algorithms in Cloud Computing,” Journal of Cloud Computing, vol. 8, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Peiyun Zhang, Mengchu Zhou, and Xuelei Wang, “An Intelligent Optimization Method for Optimal Virtual Machine Allocation in Cloud Data Centers,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 4, pp. 1725–1735, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Anton Beloglazov, and Rajkumar Buyya, “Energy Efficient Resource Management in Virtualized Cloud Data Centers,” 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Longhao Shu, and Xi Li, “Temperature-aware Energy Minimization Technique through Dynamic Voltage Frequency Scaling for Embedded Systems,” 2010 2nd International Conference on Education Technology and Computer, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Patricia Arroba et al., “Dynamic Voltage and Frequency Scaling‐aware Dynamic Consolidation of Virtual Machines for Energy Efficient Cloud Data Centers,” Concurrency and Computation Practice and Experience, vol. 29, no. 10, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Mohammad Masdari, and Hemn Khezri, “Efficient VM Migrations using Forecasting Techniques in Cloud Computing: A Comprehensive Review,” Cluster Computing, vol. 23, pp. 2629-2658, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] G. Senthil Kumar, “Framework for Hypervisor in Grid,” SSRG International Journal of Electrical and Electronics Engineering, vol. 1, no. 1, pp. 9-15, 2014.
[CrossRef] [Publisher Link]
[8] Heba Nashaat, Nesma Ashry, and Rawya Rizk , “Smart Elastic Scheduling Algorithm for Virtual Machine Migration in Cloud Computing,” Journal of Supercomputing, vol. 75, pp.3842-3865, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Jiangtao Zhang, Hejiao Huang, and Xuan Wang, “Resource Provision Algorithms in Cloud Computing: A Survey,” Journal of Network and Computer Applications, vol.64, pp. 23-42, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Tiago C. Ferreto et al., “Server Consolidation with Migration Control for Virtualized Data Centers,” Future Generation Computer Systems, vol. 27, no. 8, pp. 1027-1034, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Anton Beloglazov, and Rajkumar Buyya, “Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 7, pp.1366-1379, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Xin Li et al., “Energy Efficient Virtual Machine Placement Algorithm with Balanced and Improved Resource Utilization in a Data Center,” Mathematical and Computer Modelling, vol. 58, no. 5-6, pp. 1222-1235, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Weijia Song et al., “Adaptive Resource Provisioning for the Cloud using Online Bin Packing,” IEEE Transactions on Computers, vol. 63, no. 11, pp. 2647-2660, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Inkwon Hwang, and Massoud Pedram, “Hierarchical Virtual Machine Consolidation in a Cloud Computing System,” 2013 IEEE Sixth International Conference on Cloud Computing, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Jiangtao Zhang et al., “SLA Aware Cost Efficient Virtual Machines Placement in Cloud Computing,” 2014 IEEE 33rd International Performance Computing and Communications Conference, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[16] A. Richard William, and J. Senthilkumar, “Auto-Scalability in Cloud: A Survey of Energy and Sla Efficient Virtual Machine Consolidation,” SSRG International Journal of Computer Science and Engineering, vol. 3, no. 11, pp. 7-11, 2016.
[CrossRef] [Publisher Link]
[17] Haibo Mi et al., “Online Self-reconfiguration with Performance Guarantee for Energy-efficient Large-scale Cloud Computing data Centers,” 2010 IEEE International Conference on Services Computing, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Jing Xu, and Jose A.B. Fortes, “Multi-objective Virtual Machine Placement in Virtualized Data Center Environments,” 2010 IEEE/ACM International Conference on Green Computing and Communications and International Conference on Cyber, Physical and Social Computing, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Sinong Wang, Huaxi Gu, and Gang Wu, “A New Approach to Multi-objective Virtual Machine Placement in Virtualized Data Center,” 2013 IEEE 8th International Conference on Networking, Architecture and Storage, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Xiaoli Wang, Yuping Wang, and Yue Cui, “A New Multi-objective Bi-level Programming Model for Energy and Locality Aware Multi-job Scheduling in Cloud Computing,” Future Generation Computer Systems, vol. 36, pp. 91-101, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Chao Liu et al., “A New Evolutionary Multi-objective Algorithm to Virtual Machine Placement in Virtualized Data Center,” 2014 IEEE 5th International Conference on Software Engineering and Service Science, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[22] A. Sathya Sofia, and P. Ganesh Kumar, “Multi-objective Task Scheduling to Minimize Energy Consumption and Makespan of Cloud Computing Using NSGA-II,” Journal of Network and Systems Management, vol. 26, pp. 463-485, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Montassar Riahi, and Saoussen Krichen, “A Multi-objective Decision Support Framework for Virtual Machine Placement in Cloud Data Centers: A Real Case Study,” Journal of Supercomputing, vol. 74, no. 7, pp. 2984-3015, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Amin Yousefipour, Amir Masoud Rahmani, and Mohsen Jahanshahi, “Energy and Cost‐aware Virtual Machine Consolidation in Cloud Computing,” Software: Practice and Experience, vol. 48, no. 10, pp.1758-1774, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Lizheng Guo et al., “Multi-objective Optimization for Data Placement Strategy in Cloud Computing,” International Conference on Information Computing and Applications, pp. 119-126, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Bo Xu et al., “Dynamic Deployment of Virtual Machines in Cloud Computing using Multi-objective Optimization,” Soft Computing, vol. 19, pp. 2265-2273, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Shangguang Wang et al., “Provision of Data-intensive Services through Energy-and QoS-Aware Virtual Machine Placement in National Cloud Data Centers,” IEEE Transactions on Emerging Topics in Computing, vol. 4, no. 2, pp. 290-300, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Seyed Ebrahim Dashti, and Amir Masoud Rahmani, “Dynamic VMs Placement for Energy Efficiency by PSO in Cloud Computing,” Journal of Experimental & Theoretical Artificial Intelligence, vol. 28, no. 1-2, pp. 97-112, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Hongjian Li et al., “Energy-efficient Migration and Consolidation Algorithm of Virtual Machines in Data Centers for Cloud Computing,” Computing, vol. 98, pp. 303-317, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Yongqiang Gao et al., “A Multi-Objective Ant Colony System Algorithm for Virtual Machine Placement in Cloud Computing,” Journal of computer and System Sciences, vol. 79, no. 8, pp. 1230-1242, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Md Hasanul Ferdaus et al., “Virtual Machine Consolidation in Cloud Data Centers using ACO Metaheuristic,” European Conference on Parallel Processing, pp. 306-317, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Wei-Tao Wen et al., “An ACO-based Scheduling Strategy on Load Balancing in Cloud Computing Environment,” 2015 9th International Conference on Frontier of Computer Science and Technology, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Mingzhe Tan et al., “An Energy-aware Virtual Machine Placement Algorithm in Cloud Data Center,” Proceedings of the 2nd International Conference on Intelligent Information Processing, pp. 1-9, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Mohammad-Hossein Malekloo, Nadjia Kara, and May El Barachi, “An Energy Efficient and Sla Compliant Approach for Resource Allocation and Consolidation in Cloud Computing environments,” Sustainable Computing: Informatics and Systems, vol. 17, pp. 9-24, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Zhihua Li et al., “Energy-aware and Multi-resource Overload Probability Constraint-based Virtual Machine Dynamic Consolidation Method,” Future Generation Computer Systems, vol. 80, pp. 139-156, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Fagui Liu et al., “A Virtual Machine Consolidation Algorithm Based on Ant Colony System and Extreme Learning Machine for Cloud Data Center,” IEEE Access, vol. 8, pp. 53-67, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[37] K. Karthikeyan et al., “Energy Consumption Analysis of VM Migration in Cloud Using Hybrid Swarm Optimization (ABC–BA),” The Journal of Supercomputing, vol. 76, pp. 3374–3390, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Jianhua Jiang et al., “DataABC: A Fast ABC Based Energy-efficient Live VM Consolidation Policy with Data-intensive Energy Evaluation Model,” Future Generation Computer Systems, vol. 74, pp. 132-141, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Xiao-Ke Li et al., “Virtual Machine Placement Strategy based on Discrete Firefly Algorithm in Cloud Environments,” 2015 12th International Computer Conference on Wavelet Active Media Technology and Information Processing, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Nidhi Jain Kansal, and Inderveer Chana, “Energy-aware Virtual Machine Migration for Cloud Computing-a Firefly Optimization Approach,” Journal of Grid Computing, vol. 14, pp. 327-345, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Boominathan Perumal, and Aramudhan Murugaiyan, “A Firefly Colony and Its Fuzzy Approach for Server Consolidation and Virtual Machine Placement in Cloud Datacenters,” Advance in Fuzzy Systems, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[42] Keng-Mao Cho et al., “A Hybrid Meta-heuristic Algorithm for VM Scheduling with Load Balancing in Cloud computing,” Neural Computing and Application, vol. 26, pp. 1297-1309, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[43] Sandeep G. Sutar, Pallavi J. Mali, and Amruta Y. More, “Resource Utilization Enhancement through Live VM Migration in Cloud using Ant Colony Optimization Algorithm,” International Journal of Speech Technology, vol. 23, pp. 79–85, 2020.
[CrossRef] [Publisher Link]
[44] Anurag Satpathy et al., “Crow Search Based virtual Machine Placement Strategy in Cloud Data Centers with Live Migration,” Computers and Electrical Engineering, vol. 69, pp. 334-350, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Garima Verma, “Secure VM Migration in Cloud: Multi-Criteria Perspective with Improved Optimization Model,” Wireless Personal Communication, vol. 124, pp. 75-102, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[46] S. Jenicka, and T. Grace Shalini, “Enhanced Hybrid Dual Channel Algorithm (EHDC) for Data Dissemination in VANETs,” SSRG International Journal of Computer Science and Engineering, vol. 8, no. 7, pp. 1-9, 2021.
[CrossRef] [Publisher Link]
[47] K. Manojkumar, and S. Devi, “Jamming Attack in Wireless Sensor Networks using Ant Colony Algorithm,” SSRG International Journal of Computer Science and Engineering, vol. 8, no. 2, pp. 6-9, 2021.
[CrossRef] [Publisher Link]
[48] Fei Zhang et al., “A Survey on Virtual Machine Migration: Challenges, Techniques, and Open Issues,” IEEE Communications Surveys & Tutorials, vol. 20, no. 2, pp. 1206-1243, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[49] Lei Shi et al., “Provisioning of Requests for Virtual Machine Sets with Placement Constraints in IaaS Clouds,” 2013 IFIP/IEEE International Symposium on Integrated Network Management, 2013.
[Google Scholar] [Publisher Link]