DESIGN AND EVALUATION OF ENERGY AWARE SCHEDULING ALGORITHMS FOR GREEN COMPUTING IN DATA-INTENSIVE HIGH-PERFORMANCE SYSTEMS

With the exponential growth of data-driven applications, High-Performance Computing (HPC) systems now face substantial challenges related to energy consumption.Green computing, with its emphasis on reducing environmental impact, is becoming an essential paradigm.This paper presents a detailed examination of energy-aware scheduling algorithms aimed at improving energy efficiency in data-intensive HPC systems.By leveraging Dynamic Voltage and Frequency Scaling (DVFS), virtual machine placement, and intelligent workload scheduling, these algorithms balance performance with sustainability.Through literature review and evaluation of prominent scheduling strategies, this study contributes a critical perspective on trends, techniques, and future pathways for sustainable computing.

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Publication Details

Published
2025-05-24
DOI
https://doi.org/10.34218/ijscm_02_01_004
Primary Topic
Cloud Computing and Resource Management
Type
article
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article

DESIGN AND EVALUATION OF ENERGY AWARE SCHEDULING ALGORITHMS FOR GREEN COMPUTING IN DATA-INTENSIVE HIGH-PERFORMANCE SYSTEMS

Vivekanandhan Raja
Cloud Computing and Resource Management
article

DESIGN AND EVALUATION OF ENERGY AWARE SCHEDULING ALGORITHMS FOR GREEN COMPUTING IN DATA-INTENSIVE HIGH-PERFORMANCE SYSTEMS

Vivekanandhan Raja
article en

Abstract

With the exponential growth of data-driven applications, High-Performance Computing (HPC) systems now face substantial challenges related to energy consumption.Green computing, with its emphasis on reducing environmental impact, is becoming an essential paradigm.This paper presents a detailed examination of energy-aware scheduling algorithms aimed at improving energy efficiency in data-intensive HPC systems.By leveraging Dynamic Voltage and Frequency Scaling (DVFS), virtual machine placement, and intelligent workload scheduling, these algorithms balance performance with sustainability.Through literature review and evaluation of prominent scheduling strategies, this study contributes a critical perspective on trends, techniques, and future pathways for sustainable computing.

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