Dynamic Self-Adaptation In Server Systems For Optimized Performance And Availability

The increasing demand for high-performance, fault-tolerant, and scalable server systems, especially in data-heavy applications like news and information platforms, necessitates adaptive solutions to manage varying workloads and unpredictable traffic patterns. This paper proposes a novel self-adaptive system leveraging the MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) framework to address the challenges of load expansion and extended wait times during peak traffic conditions. The system dynamically adjusts server configurations based on real-time metrics such as CPU usage, memory consumption, and network error rates. By continuously monitoring and analyzing these metrics, the system identifies potential bottlenecks and executes optimal resource allocation strategies to ensure high availability, optimal performance, and scalability. A utility-based decision-making model is employed to prioritize server configurations that meet the demands of fluctuating user traffic. This adaptive approach enhances the user experience by maintaining a consistent, reliable service even under varying operational conditions. The proposed solution showcases the benefits of adaptive computing in real-world server applications, offering a scalable blueprint for self-adjusting systems across diverse industries.