Dynamic Multi-objective Resource Optimization in Big Data Clusters

Authors: Kanagalakshmi Murugan

DOI: https://doi.org/10.37082/IJIRMPS.v9.i4.232618

Short DOI: https://doi.org/g9rv6z

Country: United States

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Abstract: Big data systems handle vast volumes of structured and unstructured information using distributed computing frameworks like Apache Hadoop, Spark, and Flink. These platforms rely heavily on computational resources, and CPU utilization becomes a critical factor in determining the performance and efficiency of data processing operations. Effective CPU usage directly impacts throughput, latency, and the ability to meet service-level objectives. In large-scale clusters, workloads vary from CPU-intensive tasks such as real-time analytics, data mining, and machine learning to more I/O-driven operations like data ingestion or storage management. Poor CPU distribution can lead to overloaded nodes while others remain underutilized, ultimately reducing system efficiency. Traditional scheduling mechanisms in big data platforms are often rule-based and may not respond well to dynamic or unpredictable workloads, which contributes to suboptimal CPU usage. When CPU utilization remains consistently high, it can trigger job slowdowns, task failures, and increased power consumption, negatively affecting overall performance. Conversely, low CPU utilization indicates underused resources, leading to waste in computational capacity and energy. Additionally, container technologies and control groups (cgroups) support fine-tuned CPU allocation and isolation, ensuring fair usage across concurrent users or tasks in multi-tenant environments. In more advanced implementations, machine learning algorithms are used to forecast CPU needs and schedule jobs more efficiently by learning from historical patterns and usage behavior. Reinforcement learning approaches have also shown potential in achieving balanced CPU usage by adapting policies based on feedback and environment changes. These intelligent mechanisms allow big data systems to optimize CPU allocation continuously, adapting to fluctuating demand without manual intervention. Efficient CPU management contributes significantly to faster job execution, reduced operational costs, and higher cluster reliability. As data volumes and complexity continue to grow, maintaining optimal CPU utilization remains a priority for organizations aiming to derive timely insights while maximizing infrastructure value. Basic CPU utilization approaches are exhibiting performance limitations. This paper addresses performance limitations related to CPU utilization.

Keywords: CPU, Utilization, Big Data, Performance, Optimization, Scheduler, Distributed Systems, Resource Allocation, Clusters, Workload, Throughput, Latency, Efficiency, Scalability, Data Processing

Paper Id: 232618

Published On: 2021-08-12

Published In: Volume 9, Issue 4, July-August 2021

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