Abstract

Energy efficiency optimization is becoming increasingly critical in cloud-based big data processing infrastructures to address the growing energy consumption and environmental impact associated with large-scale data processing. This paper investigates strategies and techniques for optimizing energy efficiency in cloud-based big data processing infrastructures. We examine various approaches, including resource consolidation, workload scheduling, dynamic provisioning, and power management techniques, aimed at minimizing energy consumption while maintaining performance and scalability. Additionally, we explore the impact of factors such as data locality, task parallelism, and hardware heterogeneity on energy efficiency optimization efforts. Through empirical evaluation and case studies, we assess the effectiveness of different energy optimization techniques in reducing energy consumption and operational costs in real-world cloud-based big data processing environments. Furthermore, we discuss challenges, trade-offs, and future research directions in the pursuit of sustainable and energy-efficient big data processing infrastructures in the cloud. This study aims to provide insights and guidelines for practitioners and researchers in effectively addressing energy efficiency concerns in cloud-based big data processing infrastructures.

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