Adaptive Tuning and Load Balancing Using AI Agents
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I1P112Keywords:
Adaptive tuning, Load balancing, AI agents, Reinforcement learning, Deep learning, Resource optimization, Cloud computingAbstract
Adaptive tuning as well as load balancing are essential in any modern computer architecture such as cloud computing architecture, data centers or distributed networks. Conventional non-dynamic systems are often ineffective in handling dynamic workloads resulting in system inefficiency, latency and resource under utilization. In this paper, a new methodology will be suggested that builds on artificial intelligence (AI) agents to perform adaptive tuning and load balancing. The AI agents will keep watch over the parameters of the system constantly and draw conclusions about workload trends and can make smart decisions that will contribute to the optimal distribution of resources. The system dynamically balances the computational resources by combining reinforcement learning (RL), deep learning (DL), and heuristic algorithms to ensure that the system remains at its peak performance. It has also proposed predictive analytics to forecast the demand of the resources and proactive redistribution of the loads. Simulated and real world experimental results have shown that adaptive tuning with AI is able to achieve higher throughputs under shorter response time and more reliable systems overall than traditional load balancing algorithms. This paper has discussed extensively the architecture design, algorithmic strategies, and implementation challenges, and performance evaluation metrics, and has provided a strong outline of future research in intelligent adaptive systems
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