Adaptive CPU Resource Management in Distributed Systems - Sai Krishna Mylavarapu (International Journal of Intelligent Systems and Applications in Engineering, 2022)
Modern distributed systems rely on efficient resource management to handle increasing workloads and maintain high performance. Among various system resources, CPU utilization plays a critical role in determining processing efficiency and overall system responsiveness. CPU resource management becomes increasingly complex as distributed systems scale across multiple nodes. In many existing systems, static allocation strategies are used to assign workloads without considering real time CPU usage, leading to inefficient resource utilization. In static approaches, tasks are distributed uniformly across nodes regardless of their current processing state. As the cluster size increases from 3 to 5, 7, 9, and 11 nodes, CPU utilization per node tends to decrease due to uneven workload distribution. Some nodes become overloaded while others remain underutilized, resulting in inefficient use of available computational capacity. This imbalance reduces system performance and limits scalability, as additional nodes do not contribute proportionally to processing tasks. Load balanced approaches improve CPU utilization by distributing workloads more evenly. However, these methods rely on predefined allocation strategies and lack adaptability to dynamic workload variations. Differences in task complexity, execution time, and node performance still lead to uneven CPU usage, especially in larger clusters. Additionally, coordination overhead and communication delays further reduce effective CPU utilization. This paper addresses the problem of inefficient CPU resource management in distributed systems. It focuses on analyzing CPU usage behavior across cluster sizes of 3, 5, 7, 9, and 11 nodes and highlights the limitations of existing allocation strategies. The study emphasizes the need for adaptive mechanisms that dynamically allocate workloads based on real time CPU usage, enabling improved efficiency, scalability, and balanced resource utilization across distributed environments.