Energy-Efficient Data Processing Techniques in Distributed Computing

Abstract

The rapid growth of distributed computing paradigms such as cloud, edge computing, and large-scale data centers has significantly increased global energy consumption. As organizations increasingly rely on these systems for large-scale data processing, the need for energy-efficient methods has become critical due to rising operational costs and environmental concerns like carbon emissions. This paper analyzes energy-efficient data processing techniques in distributed environments, focusing on system-level optimization, algorithmic strategies, and resource management. It identifies major sources of energy consumption, including computation, data transfer, storage, and cooling, and highlights inefficiencies such as data redundancy, poor scheduling, network congestion, and underutilized resources. To address these challenges, the paper examines approaches such as energy-aware task scheduling, data locality optimization, dynamic voltage and frequency scaling (DVFS), virtualization, and workload consolidation. It also explores machine learning-based predictive models for adaptive resource allocation. A key contribution is the classification of these techniques across hardware, middleware, and application layers, along with a comparative analysis of their effectiveness. The proposed hybrid methodology integrates workload prediction, adaptive scheduling, and resource consolidation, demonstrating significant energy savings without compromising system performance. Overall, the study emphasizes the importance of coordinated, multi-layered strategies for achieving sustainable and energy-efficient distributed computing systems.