Energy Efficiency Of Data Centers Research Articles

Integrating project management with advanced cooling solutions for data centers: A path to enhanced energy efficiency

Data centers play a crucial role in modern digital infrastructure, but their energy consumption and associated carbon footprint are significant concerns. Among the various energy-consuming components of data centers, cooling systems stand out as major contributors. This paper explores the integration of project management principles with advanced cooling solutions to enhance energy efficiency in data centers. By effectively managing projects and implementing innovative cooling technologies, data center operators can achieve significant reductions in energy consumption and operational costs. The integration of project management practices with advanced cooling solutions involves several key steps. First, project management methodologies such as Agile or Waterfall are applied to plan and execute the implementation of advanced cooling technologies. This includes assessing current cooling systems, identifying suitable advanced solutions, and developing a timeline for implementation. Next, advanced cooling solutions such as liquid cooling, containment systems, or free cooling are deployed to reduce the energy required for cooling data center equipment. One of the primary benefits of integrating project management with advanced cooling solutions is enhanced energy efficiency. Advanced cooling technologies can significantly reduce the energy consumed by data center cooling systems, leading to lower operational costs and a smaller carbon footprint. Additionally, the use of project management methodologies ensures that the implementation of these technologies is carried out efficiently and effectively. Furthermore, this integration can lead to improved data center performance and reliability. By optimizing cooling systems, data center operators can ensure that IT equipment operates within optimal temperature ranges, reducing the risk of overheating and downtime. Additionally, the implementation of advanced cooling solutions can future-proof data centers against increasing heat loads from high-density computing equipment. In conclusion, integrating project management with advanced cooling solutions offers a promising path to enhance energy efficiency in data centers. By adopting this approach, data center operators can achieve significant energy savings, reduce their environmental impact, and improve overall operational efficiency.

Optimizing Data Centre Energy Efficiency with Dynamic Resource Allocation and Intelligent Cooling Management through Machine Learning

Our research aims to improve energy efficiency in data centres by combining cloud computing infrastructure with machine learning techniques. We propose that dynamic resource assignment, combined with intelligent optimisation of cooling systems, can reduce power waste and operational costs. Real-time sensor data from various data centre components such as servers, cooling systems, and power distribution units is collected and fed into machine learning models for analysis. In this way, we can create power arrangements that are tailored to the resources and needs of various applications. The experimental results show that energy consumption has been reduced by an average of 30% compared to traditional methods. Furthermore, our machine learning models are quite accurate in predicting cooling system performance. For example, an Artificial Neural Network (ANN) has an accuracy rate of 98.78%. This result demonstrates the efficacy of our approach in promoting energy efficiency and operational performance in data centres: it not only provides a scalable, cost-effective solution to industry energy efficiency challenges, but it also improves day-to-day data centre operations by reducing electrical consumption. Our approach, which is based on dynamic allocation of computational resources and real-time data analysis for optimising cooling systems, not only saves energy but also improves operational efficiency. With energy in mind, we must all work towards a more sustainable and green approach to data centre management. Future research can look into other potential optimisations, as well as issues with scalability and application in real-world data centre environments.

Design of Energy Efficient & Sustainable Buildings for ISRO Data Centers Using E-Quest

Saving energy is the same as producing new energy. Sustainable development has elevated the importance of energy performance and energy efficiency. Energy efficiency is a crucial part of sustainability, which includes all sectors of the economy from agriculture to manufacturing to services to infrastructure, including buildings used for business and research activities. Developing countries face unique difficulties in achieving sustainability because they must balance two phenomena that are diametrically opposed to one another: rapid economic growth and long-term prosperity. Commercial, information technology (IT), real estate development and construction are important economic drivers. Energy is, of course, a crucial consideration in these fields. In this research, we propose a design strategy for improving energy efficiency in an ISRO Data center by using sustainable green building technology [1]. This paper discusses the initiatives of ISRO in this direction. As maximum operation & maintenance cost of the buildings is due to electrical energy, this study shows how to design the most energy efficient institutional buildings for IMGEOS (Integrated Multi mission Ground Segment for Earth Observation satellites) of ISRO, Hyderabad. The research work discuses about the strategies / techniques to be considered since the planning & designing stage of the building infrastructure itself. This research gives the complete details of the materials with their detailed technical specifications and the methodology to be adopted, for achieving the required energy efficiency level in the data centers / similar IT infrastructure buildings. The research work also explains the process of finding & selecting the most energy efficient model by using different combination of green building materials, with respect to the base line model of ASHRAE using the e-Quest building simulation software and the results obtained thereof. Thus, this detailed research study investigates the process of designing the energy efficient data center buildings for ISRO in the context of emerging countries.

Characterization of a Rack-Level Thermosyphon-Based Cooling System

Abstract This study aims to improve the combined energy efficiency of data center cooling systems and heating/cooling systems in surrounding premises by implementing a modular cooling approach on a 42 U IT rack. The cooling solution uses a close-coupled technique where the servers are air-cooled, and the air in turn is cooled within the rack enclosure using an air-to-refrigerant heat exchanger. The refrigerant passively circulates in a loop as a thermosyphon, making the system self-sustaining during startup and shutdown, self-regulating under varying heat loads, and virtually maintenance-free by eliminating mechanical parts (other than the cabinet fans). A heat load range of 2 kW–7.5 kW is tested on a prototype system. Experimental results reveal stable thermosyphon operation using R1233zd(E) as the working fluid, a maximum evaporator pressure drop of 21.5 kPa at the highest heat load and a minimum thermosyphon resistance of 6.8 mK/W at a heat load of 5.7 kW. The air temperature profile across the load banks (server simulators) and evaporator follow the same profiles with varying heat loads. Heat losses from the cabinet due to natural convection and radiation are of the order of several Watts for heat loads below 4 kW and rise sharply to 1 kW at the highest heat load tested. The system time constant is determined to be 25 min. The heat recovery process can be financially and environmentally beneficial depending on the downstream application.

DILTS: Dragonfly-inspired lazy task scheduling algorithm for efficient energy consumption control in IoT applications

In this paper, we present a novel DILTS algorithm that uses a new approach inspired by the energy efficiency of dragonflies. The algorithm optimizes the energy-harvesting mechanisms in IoT devices, inspired by the way dragonflies use wind energy to fly. A sophisticated algorithm optimizes power consumption during task execution, saving energy and speeding up tasks while maintaining the application throughput. The algorithm leverages lazy task scheduling (LTS) to enhance task execution performance. The proposed algorithm evaluates the energy levels of each task and implements an LTS method. This LTS approach improves performance and task management by streamlining scheduling data and reducing overhead. The LTS model reliably optimizes the energy across microbenchmarks and real-time IoT devices. To assess the efficiency and practicality of our algorithm, we compared it to four alternatives. Our novel algorithm outperformed the others with a chip area of 856 μm2, performance speed of 7.11 ns, scheduling accuracy of 94%, and response time of 2.61 ns. Our simulations showed that our proposed method reduced energy consumption by up to 10.02% compared to existing methods. We evaluated the performance of the algorithms on a Zynq 7000 FPGA using the Xilinx Vivado platform via simulations. Our novel algorithm can improve the energy efficiency of green data centers.

Optimizing energy efficiency in data center cooling towers through predictive maintenance and project management

Optimizing energy efficiency in data center cooling towers is crucial for reducing operational costs and environmental impact. This review explores the integration of predictive maintenance and project management to achieve this goal. By leveraging predictive maintenance techniques, data center operators can anticipate and address potential issues before they lead to costly downtime or inefficiencies. Project management plays a key role in coordinating these efforts, ensuring that maintenance activities are carried out efficiently and effectively. Predictive maintenance relies on data analytics and machine learning algorithms to monitor the condition of cooling towers in real-time. By analyzing data such as temperature, pressure, and flow rates, these algorithms can detect anomalies and predict potential failures. This proactive approach allows data center operators to schedule maintenance activities during planned downtime, minimizing disruptions to operations. Project management practices, such as Agile or Waterfall methodologies, are essential for coordinating predictive maintenance efforts. Project managers oversee the planning, execution, and monitoring of maintenance activities, ensuring that they are completed on time and within budget. They also facilitate communication between stakeholders, including maintenance teams, data analysts, and management, to ensure that everyone is aligned and working towards the same goals. By integrating predictive maintenance with project management, data center operators can achieve significant improvements in energy efficiency. By addressing maintenance issues proactively, operators can reduce energy consumption, extend the lifespan of cooling equipment, and minimize downtime. Additionally, project management practices ensure that these efforts are coordinated and effective, maximizing the benefits of predictive maintenance. In conclusion, optimizing energy efficiency in data center cooling towers requires a holistic approach that combines predictive maintenance and project management. By leveraging predictive maintenance techniques and project management practices, data center operators can achieve significant improvements in energy efficiency, reduce operational costs, and enhance environmental sustainability.

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.

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.