Cost Optimization Strategies for Big Data Analytics in Public Cloud Infrastructures

Abstract

This paper presents a comprehensive investigation into cost optimization strategies for big data analytics deployed within public cloud infrastructures. The analysis focuses on how advanced resource allocation, elastic provisioning, and adaptable workload scheduling can collectively address the challenges of high operational expenses when handling massive datasets. By examining both the theoretical underpinnings and the practical mechanisms of allocating computing, storage, and networking resources in dynamic cloud environments, this paper contributes a methodological roadmap for reducing costs. A major emphasis is placed on nuanced resource management, where analytical workloads are partitioned, scheduled, and executed in ways that minimize overhead and idle times. Mathematical models are developed to illuminate the multi-dimensional nature of cost components, including on-demand pricing, data transfer fees, and potential penalties associated with performance degradation. Each proposed solution is evaluated under simulated operational loads that capture realistic traffic patterns and peak demands. The findings indicate that a combination of model-driven and heuristic optimization techniques can produce substantial cost savings without sacrificing the performance requirements mandated by large-scale analytics tasks. Limitations of the proposed models in heterogeneous environments and potential trade-offs between immediate cost savings and long-term sustainability are also considered. In conclusion, this paper underscores the importance of flexible strategies that balance cost with computational performance in modern big data workflows.