A Hybrid Optimization Algorithm for Efficient Virtual Machine Migration and Task Scheduling Using a Cloud-Based Adaptive Multi-Agent Deep Deterministic Policy Gradient Technique
Keywords:
Adaptive Multi-Agent System (AMS), Deep Deterministic Policy Gradient (DDPG), Iterative Concept of War and Rat Swarm (ICWRS), Rat Swarm Optimizer (RSO), System Optimization, War Strategy Optimization (WSO)Abstract
To achieve optimal system performance in the quickly developing field of cloud computing, efficient resource management—which includes accurate job scheduling and optimized Virtual Machine (VM) migration—is essential. The Adaptive Multi-Agent System with Deep Deterministic Policy Gradient (AMS-DDPG) Algorithm is used in this study to propose a cutting-edge hybrid optimization algorithm for effective virtual machine migration and task scheduling. An sophisticated combination of the War Strategy Optimization (WSO) and Rat Swarm Optimizer (RSO) algorithms, the Iterative Concept of War and Rat Swarm (ICWRS) algorithm is the foundation of this technique. Notably, ICWRS optimizes the system with an amazing 93% accuracy, especially for load balancing, job scheduling, and virtual machine migration. The VM migration and task scheduling flexibility and efficiency are greatly improved by the AMS-DDPG technology, which uses a powerful combination of deterministic policy gradient and deep reinforcement learning. By assuring the best possible resource allocation, the Adaptive Multi-Agent System method enhances decision-making even more. Performance in cloud-based virtualized systems is significantly enhanced by our hybrid method, which combines deep learning and multi-agent coordination. Extensive tests that include a detailed comparison with conventional techniques verify the effectiveness of the suggested strategy. As a consequence, our hybrid optimization approach is successful. The findings show significant improvements in system efficiency, shorter job completion times, and optimum resource utilization. Cloud-based systems have unrealized potential for synergistic optimization, as shown by the integration of ICWRS inside the AMS-DDPG framework. Enabling a high-performing and sustainable cloud computing infrastructure that can adapt to the changing needs of modern computing paradigms is made possible by this strategic resource allocation, which is attained via careful computational utilization.
Downloads
References
T. Atieh, ‘The next generation cloud technologies: a review on distributed cloud, fog and edge computing and their opportunities and challenges’, ResearchBerg Review of Science and Technology, vol. 1, no. 1, pp. 1–15, 2021.
Katal, S. Dahiya, and T. Choudhury, ‘Energy efficiency in cloud computing data centers: a survey on software technologies’, Cluster Computing, vol. 26, no. 3, pp. 1845–1875, 2023.
K. J. Rankin, ‘Composting: a Visceral Geography’, 2019.
J. Chen, H. Xing, Z. Xiao, L. Xu, and T. Tao, ‘A DRL agent for jointly optimizing computation offloading and resource allocation in MEC’, IEEE Internet of Things Journal, vol. 8, no. 24, pp. 17508–17524, 2021.
Y. Han et al., ‘Cardinality estimation in DBMS: A comprehensive benchmark evaluation’, arXiv preprint arXiv:2109. 05877, 2021.
L. Mao, R. Chen, H. Cheng, W. Lin, B. Liu, and J. Z. Wang, ‘A resource scheduling method for cloud data centers based on thermal management’, Journal of Cloud Computing, vol. 12, no. 1, pp. 1–18, 2023.
M. Hs, P. Gupta, and G. McArdle, ‘A Harris Hawk Optimisation system for energy and resource efficient virtual machine placement in cloud data centers’, Plos one, vol. 18, no. 8, p. e0289156, 2023.
S. S. Gill et al., ‘AI for next generation computing: Emerging trends and future directions’, Internet of Things, vol. 19, p. 100514, 2022.
Z. Chiba, M. S. E. K. Alaoui, N. Abghour, and K. Moussaid, ‘Automatic building of a powerful IDS for the cloud based on deep neural network by using a novel combination of simulated annealing algorithm and improved self-adaptive genetic algorithm’, International Journal of Communication Networks and Information Security, vol. 14, no. 1, pp. 93–117, 2022.
R. Daid, Y. Kumar, Y.-C. Hu, and W.-L. Chen, ‘An effective scheduling in data centres for efficient CPU usage and service level agreement fulfilment using machine learning’, Connection Science, vol. 33, no. 4, pp. 954–974, 2021.
A. Ibrahim, M. Noshy, H. A. Ali, and M. Badawy, ‘PAPSO: A power-aware VM placement technique based on particle swarm optimization’, IEEE Access, vol. 8, pp. 81747–81764, 2020.
K. Chaudhary and N. Gupta, ‘E-learning recommender system for learners: a machine learning based approach’, International Journal of Mathematical, Engineering and Management Sciences, vol. 4, no. 4, p. 957, 2019.
M. S. Ajmal, Z. Iqbal, F. Z. Khan, M. Ahmad, I. Ahmad, and B. B. Gupta, ‘Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers’, Computers and Electrical Engineering, vol. 95, p. 107419, 2021.
P. Porambage et al., ‘INtelligent Security and PervasIve tRust for 5G and Beyond’, INSPIRE-5Gplus Consortium, WP3, vol. 3, 2019.
S. Mishra and A. K. Tyagi, ‘The role of machine learning techniques in internet of things-based cloud applications’, Artificial intelligence-based internet of things systems, pp. 105–135, 2022.
A. S. Azad, M. S. A. Rahaman, J. Watada, P. Vasant, and J. A. G. Vintaned, ‘Optimization of the hydropower energy generation using Meta-Heuristic approaches: A review’, Energy Reports, vol. 6, pp. 2230–2248, 2020.
G. Dhiman, M. Garg, A. Nagar, V. Kumar, and M. Dehghani, ‘A novel algorithm for global optimization: rat swarm optimizer’, Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 8457–8482, 2021.
F. S. Gharehchopogh, ‘Quantum-inspired metaheuristic algorithms: comprehensive survey and classification’, Artificial Intelligence Review, vol. 56, no. 6, pp. 5479–5543, 2023.
D. A. Shafiq, N. Z. Jhanjhi, A. Abdullah, and M. A. Alzain, ‘A load balancing algorithm for the data centres to optimize cloud computing applications’, IEEE Access, vol. 9, pp. 41731–41744, 2021.
X. Qiu et al., ‘Synergistic effects of hydrogen bonds and the hybridized excited state observed for high-efficiency, deep-blue fluorescent emitters with narrow emission in OLED applications’, Journal of Materials Chemistry C, vol. 7, no. 18, pp. 5461–5467, 2019.
N. Mansouri, R. Ghafari, and B. M. H. Zade, ‘Cloud computing simulators: A comprehensive review’, Simulation Modelling Practice and Theory, vol. 104, p. 102144, 2020.
J. Kumar, D. Saxena, A. K. Singh, and A. Mohan, ‘Biphase adaptive learning-based neural network model for cloud datacenter workload forecasting’, Soft Computing, vol. 24, pp. 14593–14610, 2020.
N. Wu, Y. Xie, and C. Hao, ‘Ironman: Gnn-assisted design space exploration in high-level synthesis via reinforcement learning’, in Proceedings of the 2021 on Great Lakes Symposium on VLSI, 2021, pp. 39–44.
Matoušová, P. Trojovsk`y, M. Dehghani, E. Trojovská, and J. Kostra, ‘Mother optimization algorithm: a new human-based metaheuristic approach for solving engineering optimization’, Scientific Reports, vol. 13, no. 1, p. 10312, 2023.
V. Ziegler, H. Viswanathan, H. Flinck, M. Hoffmann, V. Räisänen, and K. Hätönen, ‘6G architecture to connect the worlds’, IEEE Access, vol. 8, pp. 173508–173520, 2020.
A. Nandwal, M. R. Jain, and P. Nandwal, ‘Dynamic Load Balancing for Improved Resource Allocation in Cloud Environments’, Tuijin Jishu/Journal of Propulsion Technology, vol. 44, no. 3, pp. 969–976, 2023.
V. Ziegler, H. Viswanathan, H. Flinck, M. Hoffmann, V. Räisänen, and K. Hätönen, ‘6G architecture to connect the worlds’, IEEE Access, vol. 8, pp. 173508–173520, 2020.
H. Allioui and Y. Mourdi, ‘Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey’, Sensors, vol. 23, no. 19, p. 8015, 2023.
D. Lymperis and C. Goumopoulos, ‘SEDIA: A Platform for Semantically Enriched IoT Data Integration and Development of Smart City Applications’, Future Internet, vol. 15, no. 8, p. 276, 2023.
S. Mishra and A. K. Tyagi, ‘The role of machine learning techniques in internet of things-based cloud applications’, Artificial intelligence-based internet of things systems, pp. 105–135, 2022.
Y. Huang, H. Xu, H. Gao, X. Ma, and W. Hussain, ‘SSUR: an approach to optimizing virtual machine allocation strategy based on user requirements for cloud data center’, IEEE Transactions on Green Communications and Networking, vol. 5, no. 2, pp. 670–681, 2021.
S. M. Mirmohseni, A. Javadpour, and C. Tang, ‘LBPSGORA: create load balancing with particle swarm genetic optimization algorithm to improve resource allocation and energy consumption in clouds networks’, Mathematical Problems in Engineering, vol. 2021, pp. 1–15, 2021.
P. Schratz, J. Muenchow, E. Iturritxa, J. Richter, and A. Brenning, ‘Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data’, Ecological Modelling, vol. 406, pp. 109–120, 2019.
T. Su, J. Wang, and Z. Su, ‘Benchmarking automated gui testing for android against real-world bugs’, in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2021, pp. 119–130.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
