Energy Efficiency Of Data Centers Research Articles

Sustainable IT practices in Nigerian banking: Environmental perspectives review

This review paper aims to critically analyze sustainable information technology (IT) practices in the Nigerian banking sector, focusing on environmental perspectives. The primary objective is to identify and evaluate the extent to which Nigerian banks have integrated sustainable IT practices and the impact of these practices on environmental sustainability. The methodology involves a comprehensive review of existing literature, including academic journals, industry reports, and case studies, to gather insights into the current state of sustainable IT in Nigerian banking. Key findings reveal that while some Nigerian banks have begun to adopt green IT initiatives, such as paperless operations, energy-efficient data centers, and electronic banking services, the overall adoption rate is relatively low compared to global standards. The study identifies barriers to adoption, including lack of awareness, limited regulatory frameworks, and inadequate infrastructure. Despite these challenges, the paper highlights the potential benefits of sustainable IT practices, such as reduced carbon footprint, operational cost savings, and enhanced corporate reputation. The paper concludes that for Nigerian banks to fully embrace sustainable IT practices, there needs to be a concerted effort involving policy formulation, awareness campaigns, and investment in green technologies. It recommends that stakeholders, including government, regulatory bodies, and the banks themselves, collaborate to create an enabling environment for sustainable IT. This review contributes to the understanding of sustainable IT in the Nigerian banking sector and provides a foundation for future research and policy development in this area.

Energy savings in direct air-side free cooling data centers: A cross-system modeling and optimization framework

The advancement of intelligent technology has led to a significant increase in the electricity consumption of data centers. Efficient free cooling and cross-system optimization are major means to improve data center energy efficiency. This work aims to develop a cross-system modeling and optimization methodology to reduce the energy consumption within data centers equipped with direct air-side economizer. A series of physics-based models are built, accounting for free cooling costs, supply air conditions (including both temperature and humidity), and outdoor weather fluctuations. A holistic energy optimization framework is proposed to determine optimal system setpoints that accommodate changes in computing workload arrival rates and outdoor environments. Experimental results demonstrate that the optimal cost characteristic curve for free cooling can be represented as a piecewise function. The advantages of the proposed method can save up to more than 5% of overall energy, which occurs during summer weeks, and more than 46% of cooling energy during winter weeks. The highest share of cooling energy consumption is the supply air fan, reaching up to more than 92% in some situations. The operation of a direct air-side economizer is found to contain four optimal modes.

Climatic applicability of indirect evaporative cooling strategies for data centers in China

Modern digital developments have propelled data center (DC) to prominence, with the cooling requirement being an important driver of its energy consumption. This study explores the climate application potential of indirect evaporative cooling (IEC) technology, which utilizes water evaporation for cooling. The IEC technology is considered as a promising avenue for energy-efficient DC cooling system. This study reveals essential insights for optimizing DC cooling performance by analyzing different IEC operation modes, including dry mode, wet mode, and cascading hybrid mode. The selection of operation mode is closely related to climatic conditions, DC working conditions, and IEC cooling efficiency. Higher DC supply air temperatures extend dry mode cooling hours and decrease cascading hybrid mode hours. It enhances energy savings through better utilization of natural free cooling resources. Elevating IEC cooling efficiency expands dry mode cooling hours while reducing reliance on mechanical cooling, contributing to improved power usage effectiveness (PUE) and energy conservation. The analysis is based on a typical data center and considers representative cities in different climate zones of China, emphasizing the importance of reducing the PUE and energy consumption of the DC by adjusting the DC supply air temperature and improving the IEC cooling efficiency. This work provides a useful reference for customizing DC cooling strategies according to various climate differences, optimizing energy efficiency and promoting sustainable development.

Mitigating Carbon Footprint through Green Software: A Comprehensive Framework for Energy-Efficient Data Centers and User-Friendly Carbon Footprint Calculation

The carbon footprint of the world is increasing day by day by the activities caused by every citizen. This is because the demand for energy increases and due to the lack of awareness.The average carbon emission is around 4.72 tonnes per person. There are some free online tools which are available for calculating the carbon footprint of each citizen. But those work as a datacentre in which data must be entered manually by the user which makes it quite inefficient. The existing methods do not meet the requirement of ease of handling the data.This software and the datacentres were developed which includes the availability of a user-friendly interface.Data centers also play a major role in carbon footprint, they emit CO2 and consume more energy. Data centers and new technology adoptions are mainly causing this carbon emission.These Data Centres use cloud energy to serve the user-generated requests and this energy consumption is the basic cause of carbon emission. Therefore, the energy efficiency of the data center must be governed. To reduce it, the concept of green software is introduced. Though hardware is seen as the main culprit for the consumption of energy, software plays a tremendous role in determining the efficiency of the hardware. Therefore, software must be energy efficient to handle the data center in low cost, low consumption, and low pressure.This led to the development of GREEN Software. The implementation of green software involves various factors.To reduce it, data center simulators are being used. This simulator calculates the amount of energy consumed and the amount of CO2 emitted, which as a result computes the change in carbon footprint due to it. This green software design framework provides a separate track session on energy consumption by the data center and cloud service provider along with the acquired energy efficiency level by the specific center.

Sustainability Practices in IT Infrastructure for Healthcare

In recent years, sustainability has become a critical focus across industries, and healthcare is no exception. The growing digitalization in healthcare, while improving efficiency and patient care, has also raised concerns about the environmental impact of IT infrastructure. Healthcare organizations increasingly rely on advanced technologies such as electronic health records (EHR), telemedicine, and AI-driven diagnostics, all of which demand robust IT systems. However, the energy consumption and e-waste generated by these systems can be substantial. To address this, healthcare IT leaders are adopting sustainability practices that include energy-efficient data centers, cloud computing, and hardware recycling programs. Transitioning to green technologies, such as renewable energy-powered data centers and virtualization, helps reduce the carbon footprint of IT infrastructure. Additionally, implementing software solutions that optimize resource use, reducing energy consumption in hospitals through smart systems, and adopting sustainable procurement policies are becoming essential strategies. The healthcare sector is also focusing on reducing e-waste by extending the life of IT equipment through proper maintenance and disposal practices. By integrating sustainability into IT strategies, healthcare organizations not only contribute to environmental protection but also improve operational efficiency and reduce costs. As patient data continues to grow, scalable, environmentally conscious infrastructure will become key. This shift towards sustainable IT solutions is not just an ethical responsibility but also a smart business strategy, aligning healthcare operations with global sustainability goals. By 2024 and beyond, the healthcare industry is set to witness a significant transformation in its IT infrastructure, driven by both technological advancements and an increasing commitment to sustainability.

Dynamic Optimization of Energy Efficiency and Service Level Agreement violation in Cloud Data Centers via Virtual Machine Consolidation

With the escalating proliferation of cloud computing services, cloud data center providers are actively in pursuit of solutions not only to curtail energy consumption but also to efficiently cater to the unique demands of their users, thereby enhancing user satisfaction. The process of consolidating virtual machines emerges as a viable strategy for striking a balance between energy consumption and Service Level Agreement violation (SLAv), potentially surmounting this predicament faced by cloud service providers. This study aims to minimize energy consumption and SLAv to their utmost feasible extent by utilizing the First Median Threshold (FMT) technique for physical host selection and virtual machine allocation, in conjunction with the Maximum Ratio of CPU utilization to memory Utilization (MRCU) method for virtual machine selection within the framework of virtual machine consolidation. This dual-pronged approach not only translates into cost savings for cloud providers but also ensures the delivery of optimal services to users. Furthermore, a novel method has been introduced for selecting virtual machines for migration and appropriately choosing physical hosts to achieve equilibrium between two critical parameters: energy consumption and SLAv. The proposed methodology is implemented and evaluated using the CloudSim simulator, utilizing real-world PlanetLab data. The comprehensive analysis of the simulation results reveals that the proposed approach can significantly reduce energy consumption while effectively mitigating SLAv for users.

SafeCool: Safe and Energy-Efficient Cooling Management in Data Centers With Model-Based Reinforcement Learning

Optimizing the cooling system plays a central role for capping the data center power consumption. However, the performance of traditional cooling management strategies is not satisfactory due to the complexity of thermodynamic process. Recently, several works leveraged Reinforcement Learning (RL) to improve the energy efficiency of data center cooling system. While they demonstrated that it is possible to reduce the cooling power consumption via RL, there are still some key challenges that have to be addressed before field deployment, such as safe operation guarantee and sample complexity, etc. In this paper, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> , an actor-critic Model-Based Reinforcement Learning (MBRL) algorithm for data center cooling management. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> incorporates two system models, i.e., a transition model to predict the future system state and a risk model to estimate the negative effect of executing an action. The safety of proposed algorithm is ensured by Model Predictive Control (MPC) and risk-guided exploration. In addition, by employing the MBRL framework, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> achieves higher sample efficiency and accelerated convergence. Simulations using real-world workload trace reveal that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> saves up to 13.18% cooling power compared with state-of-the-art MBRL data center cooling solutions.

Optimal supply air temperature with respect to data center operational stability and energy efficiency in a row-based cooling system under fault conditions

This is the first study of data center row-based cooling strategies under fault conditions. An optimal supply air temperature (SAT) for row-based cooling is proposed to ensure energy efficiency and operational reliability. The appropriate SAT for emergency operation is determined by evaluating how long the allowable IT equipment's inlet air temperature (IAT) can be maintained by only running the fan of the computer room air handling (CRAH) unit when the chilled water supply is interrupted. Three cases with SAT values of 20 °C, 22 °C, and 24 °C are evaluated in a numerical analysis, and the maintenance potential of the IAT is assessed by analyzing 600 s of transients from the time of cooling failure. In all cases, the IAT is almost the same as the SAT under normal cooling, but in cooling failure, the time to reach downtime beyond the allowable IAT is 246s in Case 1, 133s in Case 2, and 85s in Case 3. Response strategies to cooling failure include increasing uninterruptible power supply (UPS) capacity and applying a thermal buffer tank. The study concludes that it is reasonable to set the SAT of row-based cooling to 22 °C or lower and to install a thermal buffer tank in the chilled water system for effective downtime delay. These results are based on a rack power density of 7 kW/rack.

A liquid desiccant-assisted free-cooling system for energy-efficient data centers in hot and humid climates

Air conditioning and cooling are essential to ensure the reliable operation of data centers. Several studies have been conducted regarding the use of free cooling for energy-efficient cooling. However, free cooling faces challenges when used in hot and humid environments, which can lead to a decline in cooling tower performance. To address this issue, we propose a cooling system that utilizes a liquid desiccant to lower the humidity and improve the performance of the cooling tower, thereby reducing the energy consumption of the refrigeration system. Simulations were conducted in areas where the data centers were located, considering hot and humid climates. According to the simulation results, it was possible to achieve an average reduction of 35.12 % in cooling energy consumption by improving the performance of the cooling tower.

Balanced comparative assessment of thermal performance and energy efficiency for three cooling solutions in data centers

As the demand for data centers rises, efficient cooling systems become increasingly vital to ensure uninterrupted operations and adaptability to IT service changes. The research focuses on quantitatively comparing the thermal performance and energy efficiency of cooling systems capable of handling high-density IT power. It aims to provide objective criteria for selecting suitable cooling solutions, including an assessment of three 150 kW-class IT and facility modules, a novel contribution not extensively explored in previous studies. The study evaluates room-based, row-based, and rack-based cooling options based on the configured ITE power density, ensuring that all three solutions meet ASHRAE's recommended IT operating environment through CFD analysis. The research reveals energy efficiency improvements, with PUEcooling decreasing from 1.33 for room-based cooling to 1.28 for rack-based cooling. These improvements are attributed to reduced fan power by CRAC/H type and decreased primary plant energy consumption by increasing COP through the supply chilled water temperature. The findings offer valuable insights for designing cooling solutions tailored to ITE power density and considering factors such as space requirements, thermal performance, energy efficiency, and cost for new hyperscale data centers. Overall, this research constitutes a significant academic contribution in the field of data center cooling solutions.

Flat ball topology basics multi‐transceivers 100 km/2.5 Tbps gigabit‐ethernet PON architecture for energy efficiency in data centers

AbstractExisting electronic data sources desire high‐power requisites, in order to obtain a large bandwidth, minimum power dissipation and low latency in networks. In this instance, a gigabit ethernet passive optical network (GEPON) architecture based on a dynamic Flat Ball topology incorporating multi‐transceivers to minimize network energy consumption via passive devices along with bandwidth reconfiguration potential and low latency is reported in this paper. The proposed architecture connected with 200 nodes exhibits 26 as well as 40 dB total loss in uplink as well as downlink considerably, compared to Sirius and Sandwich tree. Also, Flat ball GEPON architecture permits 9 and 10 dBm energy saving in downlink as well as uplink, respectively, than conventional GEPON architecture at 1 ms delay. The proposed architecture with 50 Erlang load allows energy ratio of 39% in downlink and 37% in uplink, at 25 × 100 Gbps traffic rate for 55 nodes. Besides this, the faithful fiber range of 100 km considering fiber no‐linearities and impairments can be obtained successfully. The architecture can be bordered with 1:64 splitting ratio with 35 ms in downlink as well as 31 ms in uplink transmission delay at bit error rate of 10−3. Additionally, on comparing the proposed topology with existing architectures depict its superiority with minimal implementation cost, low‐power dissipation, less traffic delay as well as high scalability and high flexibility.

High performance computing with inter-virtual-machine communication

ABSTRACT Most applications use Transport Layer Security (TLS) as a default to encrypt network traffic to increase privacy and security. The adversary, however, also used LS encryption to conceal attacks or command-and-control communications. Encryption is essential for deciphering and assessing risks associated with communications. This issue should be resolved in a way that provides effective and covert decryption without compromising overall security. As a result of server consolidation, virtualization has emerged as a key technique for increasing data center energy efficiency. Consolidation of servers relies on the location of virtual machines. The placement of virtual machines has been approached from many different perspectives in recent years. Virtual machine placement approaches, however, only take into account the energy consumption of the physical machines within a data center, not the energy consumption of the communication network. Through the application domain, CVM offers a model-driven, user-centered approach to design, synthesis, and communication solutions by utilizing a fundamentally new method for handling these issues. This proposal discusses the concept, architecture, model languages, design prototypes, and implementation of CVM, arguing that it is a significantly more effective approach to technical communication solutions.

Parameterized deep reinforcement learning with hybrid action space for energy efficient data center networks

To ensure the delivery of high-performance and reliable services, data center networks (DCNs) are often over-provisioned for peak workload and traffic bursts. However, in real-world data centers, network traffic seldom reaches peak capacity of the network, resulting in significant energy waste. Traditional energy conservation approaches either suffer from high computational complexity and low solution quality, or their strategies cannot be dynamically adjusted to accommodate changes in data center network traffic. Deep reinforcement learning (DRL) provides an effective way to deal with these issues. However, most of the existing DRL-based schemes only consider either a continuous action space or a discrete action space, which greatly restricts the optimality of decisions. To solve these problems, this paper proposes a novel DRL-based DCN energy optimization framework, named SmartDCN. Specifically, SmartDCN consists of a traffic prediction module (TPM) and an energy optimization module (EOM). TPM incorporates an improved LSTM model JANET with an attention mechanism providing a high prediction accuracy, while EOM integrates our newly proposed parameterized DRL algorithm, named PAS-DQN, combining with the discrete-continuous hybrid action space. PAS-DQN implements a two-level control mechanism for the network, using TPM to predict future traffic in the data center as input. It is devoted to dynamically aggregating current traffic and makes tradeoffs between energy efficiency, performance, and robustness to optimize the network’s power consumption by dynamically calculating the minimum required network subset and turning off the non-involved network devices to achieve power savings. Experimental results show that SmartDCN significantly outperforms the existing state-of-the-art schemes in terms of energy savings under various network conditions.

A Guide to Data Collection for Computation and Monitoring of Node Energy Consumption

The digital transition that drives the new industrial revolution is largely driven by the application of intelligence and data. This boost leads to an increase in energy consumption, much of it associated with computing in data centers. This fact clashes with the growing need to save and improve energy efficiency and requires a more optimized use of resources. The deployment of new services in edge and cloud computing, virtualization, and software-defined networks requires a better understanding of consumption patterns aimed at more efficient and sustainable models and a reduction in carbon footprints. These patterns are suitable to be exploited by machine, deep, and reinforced learning techniques in pursuit of energy consumption optimization, which can ideally improve the energy efficiency of data centers and big computing servers providing these kinds of services. For the application of these techniques, it is essential to investigate data collection processes to create initial information points. Datasets also need to be created to analyze how to diagnose systems and sort out new ways of optimization. This work describes a data collection methodology used to create datasets that collect consumption data from a real-world work environment dedicated to data centers, server farms, or similar architectures. Specifically, it covers the entire process of energy stimuli generation, data extraction, and data preprocessing. The evaluation and reproduction of this method is offered to the scientific community through an online repository created for this work, which hosts all the code available for its download.

A Survey of FPGA Optimization Methods for Data Center Energy Efficiency

This article provides a survey of academic literature about field programmable gate array (FPGA) and their utilization for energy efficiency acceleration in data centers. The goal is to critically present the existing FPGAs energy optimization techniques and discuss how they can be applied to such systems. To do so, the article explores current energy trends and their projection to the future with particular attention to the requirements set out by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">European Code of Conduct for Data Center Energy Efficiency</i> . The article then proposes a complete analysis of over ten years of research in energy optimization techniques, classifying them by purpose, method of application, and impacts on the sources of consumption. Finally, we conclude with the challenges and possible innovations we expect for this sector.

Modeling and Simulation of Real-Time Virtual Machine Allocation in a Cloud Data Center

For dynamic resource scheduling in cloud data centers, a novel lightweight simulation system is proposed; two existing simulation systems at the application level for cloud computing are reviewed; and results gained using the suggested simulation system are examined and discussed. The usage of resources and energy efficiency in cloud data centers can be improved by load balancing and the consolidation of virtual machines. An aspect of dynamic virtual machine consolidation that directly affects resource usage and the quality of service the system is delivering is the timing of when it is ideal to reallocate Virtual Machines from an overloaded host [1]. Because server overloads result in a lack of resources and a decline in application performance, they have an impact on quality of service. In order to determine the best answer, existing approaches to the problem of host overload detection typically rely on statistical analysis inspired by nature. These strategies' drawbacks include the fact that they provide less-than-ideal outcomes and prevent the explicit articulation of a Quality-of-Service target. By optimizing the mean inter-migration time under the defined Quality of Service target ideally, we present a novel method for detecting host overload for any stationary workload that is known and a particular state configuration [2]. We demonstrate that our technique exceeds the best benchmark algorithm and offers over 88%of the performance of the ideal offline algorithm through simulations with real-world workload traces from more than a thousand Virtual Machines.

Data Center Efficiency Model: A New Approach and the Role of Artificial Intelligence

Bioinformatics technologies play a significant and growing role in life science research, and as these technologies develop, so does the complexity of data. The challenge of biological data growth has given rise to a number of bioinformatics data centers that offer services and solutions ranging from large-scale biosystems analyze that accounts for entire OMICs to nanoscale experiments where molecular modeling can provide insight o structure and dynamics of molecular complexes of biological components. Obviously, this kind of research requires a highly specialized level of computational and statistical expertise, as well as high-performance resources. The importance of information technology is growing, as is the use of computer information systems throughout the world. There are more and more specialized data centers and they consume more energy. The development of new strategies for energy efficiency of data centers is becoming relevant. These strategies aim to reduce the amount of energy consumed by data centers and their environmental impact without sacrificing performance. The article examines performance metrics, proposes a new method for data center energy efficiency, and discusses the role of artificial intelligence techniques in achieving these goals.

Multi agent deep reinforcement learning for resource allocation in container‐based clouds environments

AbstractVirtualization enables the deployment of several virtual servers on the same physical layer, critical component of the cloud. As cloud services advance, more apps that use repositories are developed, which adds to the overburden. Containers have evolved into the most reliable and lightweight virtualization technology for cloud services thanks to their flexible sorting, mobility, and scalability. In container‐based clouds, containers can potentially cut data centre energy usage more than virtual machines (VMs) do. Containers are less energy intensive than VMs. Resource allocation is the most prevalent method in cloud systems. However, resource allocation in container‐based clouds (RAC) is innovative and complicated due to its two‐level architecture. This includes the pairing of virtual machines and physical computers with containers. In cloud container services, planner components are essential. This lowers expenses while improving the performance and variety of workloads using cloud resources. The cloud infrastructure resource allocation framework is gaining popularity since it is energy‐efficient and focuses on cloud data management to maximize income and minimize costs. In this paper, we proposed a deep learning‐based architecture capable of achieving high data centre energy efficiency and preventing Service Level Agreement (SLA) violations from deploying green cloud resources. This research describes a hybrid optimum and multi‐agent deep reinforcement learning (MADRL) technique for dynamic task scheduling (DTS) in a container cloud environment. The MADRL‐DTS model for the RAC problem considers VM overheads, VM types, and an affinity restriction. Then, to address the RAC issue, we develop a DTS hyper‐heuristic technique. MADRL‐RAC may give allocation rules by recognizing workload trends and VM types from previous workload traces. Compared to modern procedures, the results demonstrate a significant reduction in energy consumption. The evaluation for energy‐efficient resource allocation is tested in several virtualized environments to get a high power usage effectiveness and CPU usage.

A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks.

This paper presents a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves using neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to test the methodology, a case study was conducted in the telecommunications industry to address the problem of energy efficiency in data centers. The case study involved comparing four recurrent and sequential neural networks, including recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), and online sequential extreme learning machine (OS-ELM), to determine the best network in terms of prediction accuracy and computational time. The results show that OS-ELM outperformed the other networks in both accuracy and computational efficiency. The simulation was applied to real traffic data and showed potential energy savings of up to 12.2% in a single day. This highlights the importance of energy efficiency and the potential for the methodology to be applied to other industries. The methodology can be further developed as technology and data continue to advance, making it a promising solution for a wide range of prediction problems.

Escope: An Energy Efficiency Simulator for Internet Data Centers

Contemporary megawatt-scale data centers have emerged to meet the increasing demand for online cloud services and big data analytics. However, in such large-scale data centers, servers of different generations are installed gradually year by year, making the data center heterogeneous in computing capability and energy efficiency. Furthermore, due to different processor architectures, complex and diverse load dynamic changing, business coupling, and other reasons, operators pay great attention to processor hardware power consumption and server aggregation energy efficiency. Therefore, the simulation and analysis of the energy efficiency characteristics of data center servers under different processor architectures can help operators understand the energy efficiency characteristics of data centers and make the optimal task scheduling strategy. This is very beneficial for improving the energy efficiency of the production system and the entire data center. The Escope simulator designed in this study can simulate the online quantity (placement strategy) of different types of servers in the data center and the optimal operating range of the servers. The purpose of this is to analyze the energy efficiency characteristics of all servers in the data center and provide data center operators with the energy efficiency and energy proportionality characteristics of different servers, improve server utilization, and perform reasonable scheduling. Through the simulation experiment of Escope, it can be proved that running the server at the highest energy efficiency point or running the server under full load cannot improve the energy efficiency of the entire data center. The simulation algorithm provided by Escope can select the optimal set of servers and their corresponding utilization. Escope can set up a variety of simulation strategies, and data center operators can simulate data center energy efficiency according to their own needs. Escope can also calculate the power cost savings of introducing new servers in the data center, which provides an essential reference for operators to purchase servers and design data centers.