Jul 2026· International Journal of Creative and Open Research in Engineering and Management· 0 citations
TL;DR
It is argued that energy-efficient algorithms combined with sustainable infrastructure practices provide a viable pathway toward environmentally responsible digital transformation.
Abstract
The exponential growth of data center operations and cloud computing infrastructure has resulted in unprecedented energy consumption, contributing significantly to global carbon emissions and environmental degradation. This paper presents a comprehensive investigation into energy-efficient algorithms and sustainable data center architectures as critical components of green computing. Existing energy optimization approaches including Dynamic Voltage and Frequency Scaling (DVFS), virtualization technologies, AI-driven workload distribution, and advanced cooling systems are analyzed in relation to their role in reducing data center power demand. A conceptual Energy-Aware Data Processing (EADP) algorithm is presented by integrating data management, task scheduling, and hardware optimization techniques derived from current literature. Simulated comparative results indicate meaningful reductions in energy consumption, improvements in processing time, and better Power Usage Effectiveness (PUE) and Carbon Usage Effectiveness (CUE) values under an energy-aware operating model. The study argues that energy-efficient algorithms combined with sustainable infrastructure practices provide a viable pathway toward environmentally responsible digital transformation.[1][2][3][4][5][6][7][8]
Keywords: green computing; energy-efficient algorithms; data centers; DVFS; PUE; sustainable computing; renewable energy
The rapid expansion of cloud computing and large-scale data centers has significantly increased energy consumption and carbon emissions, creating critical sustainability concerns for modern computing infrastructures. This paper proposes the Adaptive Carbon-Aware Virtualized Energy-efficient Scheduling (ACAVES) framework to improve resource utilization and reduce environmental impact in cloud environments. The framework combines workload monitoring, task classification, virtual machine consolidation, carbon-aware scheduling, and energy optimization within an integrated architecture. An adaptive scheduling mechanism allocates workloads according to utilization patterns, energy requirements, and carbon emission estimates. Experimental evaluation was performed using heterogeneous workloads containing 10,000 tasks executed over 50 physical servers and 200 virtual machines. Results demonstrate that the proposed ACAVES framework reduced energy consumption from 520 kWh to 385 kWh and carbon emissions from 310 kgCO2 to 215 kgCO2. Additionally, server utilization improved from 68% to 87%, while average task completion time decreased from 820 ms to 670 ms, confirming the effectiveness and scalability of the proposed sustainable scheduling framework.
S. K, Kishore Bitra, Usha Desai· 2026 International Conferenc...· 0 citations
The proposed hybrid methodology integrates workload prediction, adaptive scheduling, and resource consolidation, demonstrating significant energy savings without compromising system performance is proposed.
Seshagiri N· International Journal of Dat...· 0 citations
Environmental monitoring facilitates solutions to major worldwide challenges, including air pollution, climate change, and water resource degradation. Yet, conventional cloud-based IoT systems are unable to provide real-time solutions because of issues like latency, increased energy consumption, and limited scalability. This paper aims to present a positive environmental impact of edge computing for real-time environmental monitoring and provide a sustainable, energy-efficient, low-latency environmental monitoring solution. The Edge Computing Real-Time Environmental Monitoring (ECRM) framework of the paper achieves local data processing and decision-making through the integration of edge intelligence, collective, and low-power machine learning models at edge gateways. The framework achieves system responsiveness and low energy consumption through the integration of energy-aware task scheduling and adaptive data transmission strategies. Processed data for air quality (AQI), CO₂, humidity, and temperature levels substantially reduce the framework's reliance on cloud computing. The framework provides a 39% reduction in energy consumption and a 40% reduction in latency in comparison to established cloud system models. The reductions improve real-time system responsiveness and reduce network traffic. The research demonstrates that combining edge architecture and green computing is a potential solution for sustainable environmental monitoring. The proposed systems align with the goals of computing sustainability and future smart city solutions.
Dawakit Lepcha, Kanchan Thakur· 2026 4th International Confe...· 0 citations
: Green Cloud Computing has become a sustainable computing paradigm which seeks to minimize energy usage, carbon dioxide emissions and the cost of operating systems while keeping performance peak in Cloud Computing systems with a high level of services. Clustering of all cloud computing, big data analytics, Artificial Intelligence (AI) is putting enormous pressure on the world's economy and the environment to supply the energy needed for its escalating growth. Efficient use of the computer resources such as job scheduling, load balancing and fault tolerance have become essential to maximize cloud operations and ensure sustainable utilization of computer resources. Task scheduling allows to schedule tasks to the right resources to minimize execution time and energy, load balancing allows to distribute the tasks equally between servers to avoid hot spots and optimize the use of resources. To ensure service availability and system reliability, fault-tolerance mechanisms offer fault detection, prediction and recovery in the event of hardware, software and network failure. To solve these challenges, the Deep Learning (DL) techniques have been found to be effective solutions for the intelligent workload prediction, adaptive resource allocation, proactive fault detection and automatic decision making. Cloud systems can be designed to be environmentally friendly while providing good service quality and performance by integrating carbon emission factors with scheduling, load balancing and fault-tolerant decision making. Combining DL models with the job scheduling, load balancing and fault-tolerance features of the cloud computing systems, cloud service providers can optimize even further energy consumption, lower carbon footprint, enhance Quality of Service (QoS) and improve service dependability. Optimization algorithms, Reinforcement Learning (RL), model-based methods, Neural Networks and hybrid intelligent approaches have been the recent developments with great promise for developing sustainable and resilient cloud computing environments. This article discusses several cloud-based research efforts and offers a thorough overview and comparison of current strategies for task scheduling that is both energy efficient and aware of carbon emissions. By systematically reviewing existing resource management techniques, this study highlights critical gaps and opportunities for improvement. Researchers interested in innovative cloud computing and discovering ways to lower emissions of carbon in cloud settings will find the results useful.
M. Dhanalakshmi, S. Manoharan· International Journal of Sci...· 0 citations
The significant energy consumed by data centers has become a concern both for costs and associated carbon emissions. In particular, the energy efficiency of servers is a key consideration for data center operators, and understanding servers'power consumption under different operating conditions is an important aspect of it. In this paper, we present a measurement-based case study of high-throughput computing. We analyze power usage information of an operational data center, combined with focused measurements of power reduction techniques for a representative high-throughput workload. The study points out the obstacles encountered by data center operators in their efforts to minimize energy consumption and carbon emissions, and discusses the impact of server configuration adjustments on the energy consumption of processing jobs. We offer actionable recommendations for decreasing the energy usage of servers, while considering both performance and carbon emissions.
Damu Ding, Xinpeng Hong, A. Dewhurst et al.· 0 citations
The analysis proves that the combination of carbon awareness and reinforcement learning helps to create an intelligent, adaptive, and ecologically sustainable system for managing cloud resources.
Kirupavathy P., Hareeni C., Jayashri K. et al.· Journal of Ubiquitous Comput...· 0 citations
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