Self-Adaptive Distributed Computing Models for High-Performance Analytics

Abstract

The rapid growth of Big Data, IoT, cloud computing, edge intelligence, and AI has increased the demand for scalable and efficient analytical infrastructures. Traditional distributed computing systems often rely on fixed resource allocation and execution strategies, leading to performance issues, resource underutilization, higher latency, and limited scalability under dynamic workloads. To address these challenges, self-adaptive distributed computing models enable systems to autonomously monitor, analyze, and optimize operations in real time. The proposed framework integrates adaptive resource management, intelligent workload scheduling, dynamic task migration, predictive analytics, and machine learning-based optimization to improve computational efficiency and responsiveness. The architecture includes monitoring layers, decision engines, adaptation controllers, distributed resource managers, and analytics execution frameworks. Machine learning techniques such as reinforcement learning, deep neural networks, and predictive modeling support proactive adaptation by forecasting workload demands and optimizing scheduling decisions. Experimental results demonstrate significant improvements in resource utilization, throughput, scalability, fault tolerance, and execution time compared to traditional approaches. Self-healing capabilities further enhance resilience against node failures and network disruptions. Overall, self-adaptive distributed computing provides a scalable, intelligent, and resilient foundation for next-generation high-performance analytics and data-intensive applications.