Optimizing Resource Allocation for Big Data Workloads in Cloud Computing Platforms

Abstract

This paper presents a rigorous examination of strategies for optimizing resource allocation in cloud computing platforms handling large-scale data-driven workloads. The problem of resource optimization becomes particularly crucial when heterogeneous clusters must accommodate intensive jobs with varying computational, storage, and networking demands. In this work, we analyze frameworks capable of dynamically distributing resources across a massive pool of nodes, focusing on performance metrics such as execution latency, throughput, and cost efficiency. We discuss approaches for predicting workload characteristics in real time, leveraging algorithmic and statistical models that guide scheduling policies to maximize utilization while avoiding excessive resource contention. Our discussion emphasizes practical issues that arise in live production clusters, including time-varying data arrival patterns and the effects of skewed job distributions on both performance and fault tolerance. We incorporate highly advanced mathematical modeling to characterize the cloud environment, applying theoretical insights to support adaptivity in resource provisioning. The performance of the proposed strategies is demonstrated through hypothetical yet carefully constructed results showing significant reductions in latency and improvements in overall computational throughput. While the methodologies exhibit robust behavior over a wide range of workloads, specific limitations arise from incomplete knowledge of future demand patterns and dependencies on accurate forecasting. The study concludes by outlining potential avenues for future refinements, ensuring broader applicability and resilience.