Optimizing System Resources and Adaptive Load Balancing Framework Leveraging ACO and Reinforcement Learning Algorithms

• Published in: Journal of Electrical Systems Vol. 20; no. 1s; pp. 244 - 256
• Main Authors: Shahakar, Minal, Mahajan, S A, Patil, Lalit
• Format: Journal Article
• Language: English
• Published: Paris Engineering and Scientific Research Groups 28.03.2024
• Subjects: Algorithms; Ant colony optimization; Load balancing; Machine learning
• ISSN: 1112-5209; 1112-5209
• DOI: 10.52783/jes.768

In today's constantly changing computer settings, the most important things for improving speed and keeping stability are making the best use of system resources and making sure that load balancing works well. To achieve flexible load balancing and resource optimization, this study suggests a new system that combines the Ant Colony Optimization (ACO) and Reinforcement Learning (RL) methods. The structure is meant to help with the problems that come up when tasks and resource needs change in big spread systems. ACO is based on how ants find food and is used to change how jobs are distributed among computer nodes based on local knowledge and scent tracks. This autonomous method makes it easy to quickly look for solutions and adjust to new situations. In addition to ACO, RL methods are used to learn about and adjust to how the system changes over time. By planning load balancing as a series of decisions, RL agents are able to keep improving their rules so that the system works better and resources are used more efficiently. Agents learn the best ways to divide up tasks and use resources by interacting with the world and getting feedback. The suggested system works in a spread way, which makes it scalable and reliable in a variety of settings. The system changes its behavior on the fly to react to changing tasks and resource availability by using the group intelligence of ACO and the flexibility of RL. The system can also handle different improvement goals and limitations, which makes it flexible and usable in a range of situations. The suggested approach works better than standard load balancing methods at improving system performance, lowering reaction times, and making the best use of resources, as shown by the results of experiments. Using the strengths of the ACO and RL algorithms, this structure looks like a good way to deal with the complexity of current computer systems and make good use of resources in changing settings.