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Ez-Zahout, Abderrahmane
- Optimizing Virtual Machines Placement in a Heterogeneous Cloud Data Center System
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Authors
Affiliations
1 Intelligent Processing and Security of Systems Team, Faculty of Sciences, Mohammed V University, Rabat, MA
1 Intelligent Processing and Security of Systems Team, Faculty of Sciences, Mohammed V University, Rabat, MA
Source
International Journal of Computer Networks and Applications, Vol 11, No 1 (2024), Pagination: 1-12Abstract
In a cloud computing environment, good resource management remains a major challenge for its good operation. Implementing virtual machine placement (VMP) on physical machines helps to achieve various objectives, such as resource allocation, load balancing, energy consumption, and quality of service. VMP (virtual machine placement) in the cloud is critical, so it's important to audit its implementation. It must take into account the resources of the physical server, including CPU, RAM, and storage. In this paper, a metaheuristic algorithm based on the Grey Wolf Optimization (GWO) method is used to optimize the placement of virtual machines in a cloud environment, effectively minimizing the number of active virtual machines used to host virtual servers. Experimental results demonstrate the effectiveness of the proposed method, called Grey Wolf Optimization for Virtual Machine Placement (GWOVMP). The method reduces power consumption by 20.99 and resource wastage by 1.80 compared with existing algorithms.Keywords
Cloud Computing, GWO Algorithm, Metaheuristics Algorithm, Optimization, Virtual Machine Placement, Data Center, Power Consumption.Full Text
References
- S. Abohamama and E. Hamouda, “A hybrid energy–Aware virtual machine placement algorithm for cloud environments,” Expert Syst Appl, vol. 150, p. 113306, 2020, doi: 10.1016/j.eswa.2020.113306.
- S. P. M. Ziyath and S. Senthilkumar, “MHO: meta heuristic optimization applied task scheduling with load balancing technique for cloud infrastructure services,” J Ambient Intell Humaniz Comput, vol. 12, no. 6, pp. 6629–6638, Jun. 2021, doi: 10.1007/s12652-020-02282-7.
- A. Ndayikengurukiye, A. Ez-Zahout, and F. Omary, “A Big Data-Driven Model for Secured Systems: A Blockchain-Based Architecture for Cloud Computing and IoT Security,” in Advances on Smart and Soft Computing, F. Saeed, T. Al-Hadhrami, E. Mohammed, and M. Al-Sarem, Eds., Singapore: Springer Singapore, 2022, pp. 409–418.
- A. Ndayikengurukiye, A. Ez-Zahout, A. Aboubakr, Y. Charkaoui, and O. Fouzia, “Resource Optimisation in Cloud Computing: Comparative Study of Algorithms Applied to Recommendations in a Big Data Analysis Architecture,” Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 2021, no. 4, pp. 65–75, 2021, doi: 10.14313/JAMRIS/4-2021/28.
- S. Omer, S. Azizi, M. Shojafar, and R. Tafazolli, “A priority, power and traffic-aware virtual machine placement of IoT applications in cloud data centers,” Journal of Systems Architecture, vol. 115, May 2021, doi: 10.1016/j.sysarc.2021.101996.
- U. Chourasia, R. Gandhi, P. Vishwavidyalaya, and D. Sanjay, “Optimal Load Balancing in Cloud using ANFIS and MGWO based Polynomial Neural Network”, doi: 10.21203/rs.3.rs-529618/v1.
- M. S. Kumar and G. R. Karri, “EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework,” Sensors, vol. 23, no. 5, Mar. 2023, doi: 10.3390/s23052445.
- Y. Li, X. Lin, and J. Liu, “An improved gray wolf optimization algorithm to solve engineering problems,” Sustainability (Switzerland), vol. 13, no. 6, Mar. 2021, doi: 10.3390/su13063208.
- J. Too, A. R. Abdullah, N. M. Saad, N. M. Ali, and W. Tee, “A new competitive binary grey wolf optimizer to solve the feature selection problem in EMG signals classification,” Computers, vol. 7, no. 4, Dec. 2018, doi: 10.3390/computers7040058.
- E. G. Dada, S. B. Joseph, D. O. Oyewola, A. A. Fadele, H. Chiroma, and S. M. Abdulhamid, “Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons,” Gazi University Journal of Science, vol. 35, no. 2, pp. 485–504, Jun. 2022, doi: 10.35378/gujs.820885.
- G. Shial, S. Sahoo, and S. Panigrahi, “An Enhanced GWO Algorithm with Improved Explorative Search Capability for Global Optimization and Data Clustering,” Applied Artificial Intelligence, vol. 37, no. 1, 2023, doi: 10.1080/08839514.2023.2166232.
- N. M. Sallam, A. I. Saleh, H. Arafat Ali, and M. M. Abdelsalam, “An Efficient Strategy for Blood Diseases Detection Based on Grey Wolf Optimization as Feature Selection and Machine Learning Techniques,” Applied Sciences (Switzerland), vol. 12, no. 21, Nov. 2022, doi: 10.3390/app122110760.
- Y. Hou, H. Gao, Z. Wang, and C. Du, “Improved Grey Wolf Optimization Algorithm and Application,” Sensors, vol. 22, no. 10, May 2022, doi: 10.3390/s22103810.
- H. M. R. Al-Khafaji, “Improving Quality Indicators of the Cloud-Based IoT Networks Using an Improved Form of Seagull Optimization Algorithm,” Future Internet, vol. 14, no. 10, Oct. 2022, doi: 10.3390/fi14100281.
- P. Hu, J. S. Pan, and S. C. Chu, “Improved Binary Grey Wolf Optimizer and Its application for feature selection,” Knowl Based Syst, vol. 195, May 2020, doi: 10.1016/j.knosys.2020.105746.
- S. Azizi, M. Shojafar, J. Abawajy, and R. Buyya, “GRVMP: A Greedy Randomized Algorithm for Virtual Machine Placement in Cloud Data Centers,” IEEE Syst J, vol. 15, no. 2, pp. 2571–2582, Jun. 2021, doi: 10.1109/JSYST.2020.3002721.
- A. Al-Moalmi, J. Luo, A. Salah, and K. Li, “Optimal virtual machine placement based on grey wolf optimization,” Electronics (Switzerland), vol. 8, no. 3, Mar. 2019, doi: 10.3390/electronics8030283.
- S. Gharehpasha, M. Masdari, and A. Jafarian, “Virtual machine placement in cloud data centers using a hybrid multi-verse optimization algorithm,” Artif Intell Rev, vol. 54, no. 3, pp. 2221–2257, Mar. 2021, doi: 10.1007/s10462-020-09903-9.
- A. Yousefipour, A. M. Rahmani, and M. Jahanshahi, “Improving the load balancing and dynamic placement of virtual machines in cloud computing using particle swarm optimization algorithm,” International Journal of Engineering Transactions C: Aspects, vol. 34, no. 6, pp. 1419–1429, Jun. 2021, doi: 10.5829/ije.2021.34.06c.05.
- B. Liang, D. Wu, P. Wu, and Y. Su, “An energy-aware resource deployment algorithm for cloud data centers based on dynamic hybrid machine learning,” Knowl Based Syst, vol. 222, Jun. 2021, doi: 10.1016/j.knosys.2021.107020.
- S. S. Sefati, M. Mousavinasab, and R. Zareh Farkhady, “Load balancing in cloud computing environment using the Grey wolf optimization algorithm based on the reliability: performance evaluation,” Journal of Supercomputing, vol. 78, no. 1, pp. 18–42, Jan. 2022, doi: 10.1007/s11227-021-03810-8.
- J. Lu, W. Zhao, H. Zhu, J. Li, Z. Cheng, and G. Xiao, “Optimal machine placement based on improved genetic algorithm in cloud computing,” Journal of Supercomputing, vol. 78, no. 3, pp. 3448–3476, Feb. 2022, doi: 10.1007/s11227-021-03953-8.
- J. Luo, W. Song, and L. Yin, “Reliable Virtual Machine Placement Based on Multi-Objective Optimization with Traffic-Aware Algorithm in Industrial Cloud,” IEEE Access, vol. 6, pp. 23043–23052, Mar. 2018, doi: 10.1109/ACCESS.2018.2816983.
- A. Ndayikengurukiye, A. Ez-Zahout, and F. Omary, “An overview of the different methods for optimizing the virtual resources placement in the Cloud Computing,” J Phys Conf Ser, vol. 1743, p. 012030, Jan. 2021, doi: 10.1088/1742-6596/1743/1/012030.
- H. Li, L. Wen, Y. Liu, and Y. Shen, “More than Meets One Core: An Energy-Aware Cost Optimization in Dynamic Multi-Core Processor Server Consolidation for Cloud Data Center,” Electronics (Switzerland), vol. 11, no. 20, Oct. 2022, doi: 10.3390/electronics11203377.
- J. Wang, H. Gu, J. Yu, Y. Song, X. He, and Y. Song, “Research on virtual machine consolidation strategy based on combined prediction and energy-aware in cloud computing platform,” Journal of Cloud Computing, vol. 11, no. 1, Dec. 2022, doi: 10.1186/s13677-022-00309-2.
- S. Nabavi, L. Wen, S. S. Gill, and M. Xu, “Seagull optimization algorithm based multi-objective VM placement in edge-cloud data centers,” Internet of Things and Cyber-Physical Systems, vol. 3, pp. 28–36, 2023, doi: 10.1016/j.iotcps.2023.01.002.
- H. Talebian et al., “Optimizing virtual machine placement in IaaS data centers: taxonomy, review and open issues,” Cluster Comput, vol. 23, no. 2, pp. 837–878, Jun. 2020, doi: 10.1007/s10586-019-02954-w.
- M. Ghetas, “A multi-objective Monarch Butterfly Algorithm for virtual machine placement in cloud computing,” Neural Comput Appl, vol. 33, no. 17, pp. 11011–11025, Sep. 2021, doi: 10.1007/s00521-020-05559-2.
- T. B. Hewage, S. Ilager, M. A. Rodriguez, and R. Buyya, “CloudSim express: A novel framework for rapid low code simulation of cloud computing environments,” Softw Pract Exp, 2023, doi: 10.1002/spe.3290.
- I. Behera and S. Sobhanayak, “Task scheduling optimization in heterogeneous cloud computing environments: A hybrid GA-GWO approach,” J Parallel Distrib Comput, vol. 183, Jan. 2024, doi: 10.1016/j.jpdc.2023.104766.
- M. A. Khan, A. Paplinski, A. M. Khan, M. Murshed, and R. Buyya, “Dynamic virtual machine consolidation algorithms for energy-efficient cloud resource management: A review,” in Sustainable Cloud and Energy Services: Principles and Practice, Springer International Publishing, 2017, pp. 135–165. doi: 10.1007/978-3-319-62238-5_6.
- A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing,” Future Generation Computer Systems, vol. 28, no. 5, pp. 755–768, May 2012, doi: 10.1016/j.future.2011.04.017.
- M. Masdari, S. S. Nabavi, and V. Ahmadi, “An overview of virtual machine placement schemes in cloud computing,” Journal of Network and Computer Applications, vol. 66. Academic Press, pp. 106–127, May 01, 2016. doi: 10.1016/j.jnca.2016.01.011.
- D. Zhu, “Max–min Bin Packing Algorithm and its application in nano-particles filling,” Chaos Solitons Fractals, vol. 89, pp. 83–90, Aug. 2016, doi: 10.1016/j.chaos.2015.09.024.
- SOAVMP: Multi-Objective Virtual Machine Placement in Cloud Computing Based on the Seagull Optimization Algorithm
Abstract Views :227 |
Authors
Aristide Ndayikengurukiye
1,
Rim Doukha
1,
Eric Niyukuri
2,
Eric Muheto
3,
Abderrahmane Ez-zahout
4,
Fouzia Omary
4
Affiliations
1 Intelligent Processing and Security of Systems Team, Faculty of Sciences, Mohammed V University, Rabat, MA
2 Faculty of Engineering Sciences, University of Burundi, Bujumbura, BI
3 Morgan Stanley, Montréal, CA
4 Intelligent Processing and Security of Systems Team, Faculty of Sciences, Mohammed V University, Rabat, IN
1 Intelligent Processing and Security of Systems Team, Faculty of Sciences, Mohammed V University, Rabat, MA
2 Faculty of Engineering Sciences, University of Burundi, Bujumbura, BI
3 Morgan Stanley, Montréal, CA
4 Intelligent Processing and Security of Systems Team, Faculty of Sciences, Mohammed V University, Rabat, IN
Source
International Journal of Computer Networks and Applications, Vol 11, No 3 (2024), Pagination: 375-389Abstract
Virtual machine placement (VMP) involves selecting the most appropriate physical machine (PM) to run a virtual machine (VM) in cloud data centers (CDCs). Unfortunately, current VMP methods only consider limited resources, resulting in load imbalance and unnecessary activation of certain PMs in the data center (DC). This paper proposes a new approach called Multi-Objective Seagull Optimization Algorithm Virtual Machine (MOSOAVMP) to address these issue s and enhance resource management in CDCs. The aim is to optimize resource utilization, minimize energy consumption, reduce SLA violations, and improve overall DC efficiency. The aim is to achieve an optimal deployment that will meet these different objectives while minimizing the costs associated with operating the CDCs. The results show the proposed MOSOAVMP's efficiency compared with existing algorithms for the different measurements considered. These experimental results show that MOSOAVMP reduces resource wastages, and energy consumption by 5.44%, improves average CPU usage by 14.84%, memory usage by 11.54%, average storage usage by 5.37%, and average bandwidth usage by 6.88%.Keywords
Cloud Computing, Seagull Optimization Algorithm, Metaheuristics Algorithm, SLA, Virtual Machine Placement, Data Center, Power ConsumptionFull Text