2025· International Journal of Data Engineering and Intelligent Computing· 0 citations
TL;DR
The proposed hybrid methodology integrates workload prediction, adaptive scheduling, and resource consolidation, demonstrating significant energy savings without compromising system performance is proposed.
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.
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
Shankar Kumar· International Journal of Cre...· 0 citations
This study investigates energy-efficient distributed machine learning techniques, including federated learning, model compression, adaptive resource management, dynamic task offloading, and communication-efficient optimization, and proposes a distributed learning framework that integrates local model training, adaptive communication scheduling, gradient compression, and workload balancing to minimize energy consumption while maintaining learning accuracy.
Venkatesh Iyer· International Journal of App...· 0 citations
Cloud computing has transformed the delivery of modern applications and services by providing scalable, flexible, and cost-effective access to computing resources. One of the most critical challenges in cloud environments is the efficient distribution of dynamic workloads across heterogeneous resources, commonly addressed through load balancing and task scheduling techniques. Efficient scheduling plays a vital role in maximizing resource utilization, minimizing response time, and maintaining acceptable Quality of Service (QoS), particularly under dynamic and large-scale workloads. Despite the progress achieved by traditional heuristics such as Min-Min and metaheuristic approaches like the Improved Sparrow Search Algorithm (ISSA), challenges related to scalability, adaptability, and computational overhead remain. Metaheuristic-based approaches often involve iterative optimization processes that may limit their efficiency in real-time scheduling scenarios. In this paper, we propose a lightweight Stochastic Predictive Energy-Aware Scheduling (SPES) algorithm that integrates predictive execution estimation, multi-resource awareness, and stochastic decision-making. Unlike deterministic scheduling strategies, SPES employs a Top K candidate selection mechanism combined with probabilistic weighting and epsilon-greedy exploration to enhance adaptability and avoid suboptimal resource allocation. The proposed method considers CPU, memory, and I/O demands to achieve balanced utilization across heterogeneous hosts while implicitly addressing energy efficiency through utilization-based modeling. The proposed algorithm is implemented and evaluated using the CloudSim 5.0 simulation framework under heterogeneous multi-region cloud environments with varying workload sizes. Experimental results demonstrate that SPES consistently outperforms ISSA and achieves makespan reductions of up to 23.8% while improving scalability, resource utilization, and scheduling efficiency under dynamic cloud workloads. These results indicate that SPES provides an effective lightweight scheduling solution for large-scale and energy-aware cloud computing environments and supports green computing objectives through improved resource efficiency.
M. Yacoub, Ahmed E. Abdel Raouf, Walaa K. Gad et al.· Electronics· 0 citations
The rapid growth of data-intensive applications in scientific computing, enterprise analytics, and cloud services has increased the demand for efficient distributed data processing systems. Traditional scheduling methods like FCFS, Round Robin, and heuristic approaches often fail to meet the dynamic and heterogeneous requirements of modern environments. This paper proposes an intelligent workflow scheduling framework that improves performance through adaptive decision-making, predictive analytics, and machine learning. The system dynamically allocates tasks based on resource availability, workflow dependencies, and historical execution data, enabling it to anticipate bottlenecks and reassign tasks proactively. It also incorporates resource heterogeneity modeling and dependency-aware scheduling to reduce idle time and optimize execution. Performance is evaluated using metrics such as makespan, throughput, resource utilization, and fault tolerance, showing significant improvements over traditional methods. The framework also addresses key challenges like load balancing, scalability, energy efficiency, and fault tolerance. Overall, the proposed approach enhances system efficiency and scalability while supporting integration with emerging technologies such as edge computing and hybrid cloud environments, paving the way for more autonomous and resilient distributed scheduling systems.
D. Parnas· International Journal of Dat...· 0 citations
The study concludes that intelligent edge computing architectures will play a vital role in supporting future real-time applications and next-generation 6G-enabled digital ecosystems.
Alan Bundy· International Journal of Mod...· 0 citations
A structured review of optimization models in cloud and data center environments using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided methodology covering literature from 2016 to 2025 reveals that the adaptive methods can improve throughput, reduce latency, and enhance energy efficiency under specific datasets, simulation settings, traffic models, and network configurations.
S. Alanazi· Journal of Advances in Infor...· 0 citations