Energy Efficiency Of Data Centers Research Articles

Fuzzy Allocation Optimization Algorithm for High-Density Storage Locations with Low Energy Consumptions

The global demand for stored and processed data has surged due to the development of IoTs and similar computational structures, which has led to further energy consumption by concentrated data storage facilities and thus the demands of global energy and environmental needs. The current paper introduces Fuzzy Allocation Optimization Algorithm to mitigate energy consumption in high storage density settings. It uses the principles of Fuzzy logic to determine the best way to assign the tasks in relation to storage density necessity, urgency and energy consumption. Thus, the proposed approach incorporates fuzzy inference systems with multi-objective optimization methods where location of storage is dynamically assessed and assigned according to energy efficiency parameters. The findings of the simulation and case study prove that the algorithm is successful in saving energy while at the same time lowering storage I/O response time, which provides a viable solution to energy issues in evolving data centres. This work satisfies the lack of energy efficient algorithms in high density storage areas and responds to the recent calls for green technology and smart utilization of resources in the energy field. The findings are used in the promotion of significant IT infrastructures towards developing the next generation of energy efficient data centers with respect to Future Internet and evolving energy web environments.

Optimizing Energy Efficiency in Cloud Data center using an Enhanced Dualist Algorithm with Improved Exploration

Optimizing Energy Efficiency in Cloud Data center using an Enhanced Dualist Algorithm with Improved Exploration

Virtualization and energy management optimization of high speed computer network data centers based on optical switching and network technology

With the development of cloud computing and big data technology, the scale and complexity of data centers are increasing, and the thermal energy consumption management of computer processors can effectively improve the energy efficiency of data centers and reduce operating costs. Therefore, this study discusses the application of high-speed computer network architecture based on optical switching and network technology in data center virtualization. By constructing a high-speed computer network model based on optical switching technology, virtualization technology is used to dynamically schedule and manage resources, combined with real-time monitoring system, and the energy consumption data of the processor is collected. Using the data analysis method, the energy consumption performance under different load conditions is evaluated and the optimization strategy is proposed. The experimental results show that the network architecture using optical switching technology significantly reduces the latency of data center and improves the resource utilization. In the thermal management of the processor, dynamic resource scheduling successfully reduces the energy consumption and effectively maintains the stability and performance of the system. As a result, data center virtualization solutions based on optical switching and networking technologies can significantly optimize the power management of computer processors and improve overall energy efficiency.

Towards Energy-Efficient Data Centers: A Comprehensive Review of Passive and Active Cooling Strategies

With the rapid growth of cloud computing, the number of data centers (DCs) continuously increases, leading to a high-energy consumption dilemma. Cooling, apart from IT equipment, represents the largest energy consumption in DCs. Passive design (PD) and active design (AD) are two important approaches in architectural design to reduce energy consumption. However, for DC cooling, few studies have summarized AD, and there are almost no studies on PD. Based on existing international research (2005-2024), this paper summarizes the current state of cooling strategies for DCs. PD encompasses floors, ceilings, and layout and zoning of racks. Additionally, other passive strategies not yet studied in DCs are critically examined. AD includes air, liquid, free, and two-phase cooling. This paper systematically compares the performance of different AD technologies on various KPIs, including energy, economic, and environmental indicators. This paper also explores the application of different cooling design strategies through best-practice examples and presents advanced algorithms for energy management in operational DCs. This study reveals that free cooling is widely employed, with Artificial Neural Networks emerging as the most popular algorithm for managing cooling energy. Finally, this paper suggests four future directions for reducing cooling energy in DCs, with a focus on the development of passive strategies. This paper provides an overview and guide to DC energy-consumption issues, emphasizes the importance of implementing passive and active design strategies to reduce DC cooling energy consumption, and provides directions and references for future energy-efficient DC designs.

An Alternative Reinforcement Learning (ARL) control strategy for data center air-cooled HVAC systems

Energy efficiency of data center is of great concern globally due to their large amount of energy consumption and the foreseeable growth in the demand of digital services in the future. Advanced control strategies are needed to reduce energy consumption. However, the optimization of data center HVAC control is a challenge task due to the complexity of the thermal dynamic models of buildings and uncertainties associated with both server loads and outdoor temperature. In this paper, we propose a new control strategy called Alternately-Reinforcement Learning-control(ARL), which realizes the alternating control of RL and proportional, integral and derivative (PID) for optimizing the control of air-cooled HVAC systems in data centers. The control object of the ARL is the speed set of the compressor and the condensing fan, and the control goal is to minimize energy consumption while maintaining temperature stability. The applied RL algorithm is Deep deterministic policy gradient (DDPG) and Proximal policy optimization (PPO). We pre-train the ARL strategy in offline environment firstly and deploy it in real data center environment for online testing. The test results show that the ARL has significant advantages in maintaining temperature stability while reducing energy consumption, and the PPO algorithm performs better than the DDPG algorithm. Compared with the PID algorithm, the PPO algorithm can save energy by 5.27%, and the temperature control effect can be improved by 3.27%,which indicates the feasibility of the ARL for implementation in real data centers.

Analysis of refrigerant/lubricant distribution and operating characteristics of a rotary booster for data center free cooling

A booster-driven loop free cooling system plays a crucial role in enhancing data center energy efficiency and reducing emissions due to the special booster with low pressure ratio as a vital driving component. And the impact of lubricant and its fraction on the booster operation are the key points under low pressure ratio, as well as lubricant distribution. In this paper, the refrigerant/lubricant two-phase mixing flow in the working chamber of a booster was investigated under low pressure ratio for data center free cooling. The volume of fluid (VOF) multiphase flow model combining the working chamber of the rotary booster and discharge valve was built up to analyze the dynamic characteristics of the booster under free cooling conditions. The effect of lubricant fraction and its working frequency on the operating indexes (exhaust temperature/pressure, mass flow rate, volumetric efficiency, indicated specific work, energy efficiency ratio) were studied. The lubricant distribution and its changes for different angles and frequencies were presented. The results indicate that it can improve the thermal performance of the booster effectively with appropriate volume fraction of lubricant. The lubricant in the two-phase flow reduces the inner temperature and pressure in the booster working chamber, which changes the exhaust pressure and volumetric efficiency as well as the cooling capacity. The exhaust pressure and the mass flow rate increase notably for the refrigerant/oil mixture with the increasing lubricant volume fraction, while other performance indexes decrease. The exhaust pressure and the mass flow rate increase by 0.51 % and 47.14 %, separately, when the lubricant volume fraction raises from 0 to 5 % at operating frequency of 50 Hz. Whereas, the exhaust temperature, refrigerant mass flow rate, volumetric efficiency, indicated specific power, cooling capacity and EER decrease by 18.11 %, 4.81 %, 47.14 %, 38.94 %, 4.81 % and 26.36 %, respectively. The investigations can support the lubrication and sealing of the booster with low pressure ratio for free cooling to reach efficient and reliable operation.

Investigating thermal performance and energy efficiency in under-floor air distribution data centers: A case study

This study conducted on-site measurements of air temperature and airflow velocity at the cold aisle outlet, combined with infrared imaging, to derive optimized layouts for rack servers. It was found that installing blind plates effectively improves the uniformity of cold air distribution within the racks, as indicated by the temperature and airflow velocity at the server inlet and outlet under both blind and non-blind plate conditions. Finally, thermal performance indicators (i.e., SHI, β, RHI, RCI) and Reynolds number were used to assess the thermal performance of the data center, while energy efficiency indicators (i.e., PUE, WUE, CUE) were employed to evaluate its energy efficiency. The results revealed that the cold aisle has less air distribution at both ends and is more concentrated in the middle. It is advisable to position high-power servers in the middle racks and place racks with relatively lower power at the front and rear ends to ensure effective heat dissipation of the servers. The installation of blind plates can effectively enhance the uniformity of airflow within the racks and improve server cooling. Among the tested racks, 16 out of 17 exhibited low temperatures (RCILO) below 90%, indicating heat loss and energy waste. The PUE value of the data center is 1.19, and the CUE value is 0.77.

Expert and operator perspectives on barriers to energy efficiency in data centers

It was last estimated in 2016 that data centers (DCs) comprise approximately 2% of total US electricity consumption. However, this estimate is currently being updated to account for the massive increase in computing needs due to streaming, cryptocurrency, and artificial intelligence (AI). To prevent energy consumption that tracks with increasing computing needs, it is imperative we identify energy efficiency strategies and investments beyond the low-hanging fruit solutions. In a two-phased research approach, we ask: What non-technical barriers still impede energy efficiency (EE) practices and investments in the data center sector, and what can be done to overcome these barriers? In particular, we are focused on social and organizational barriers to EE. In Phase I, we performed a literature review and found that technical solutions are abundant in the literature, but fail to address the top-down cultural shifts that need to take place in order to adapt new energy efficiency strategies. In Phase II, reported here, we interviewed 16 data center operators/experts to ground-truth our literature findings. Our interview protocols focus on three aspects of DC decision-making: procurement practices, metrics and monitoring, and perceived barriers to energy efficiency. We find that vendors are the key drivers of procurement decisions, advanced efficiency metrics are facility-specific, and there is convergence in the design of advanced facilities due to the heat density of parallelized infrastructure. Our ultimate goals for our research are to design DC decarbonization policies that target organizational structure, empower individual staff, and foster a supportive external market.

Methodology to Characterize Row Manifolds for High Power Direct to Chip Liquid Cooling Data Centers

Abstract Demand is growing for the dense and high-performing IT computing capacity to support artificial intelligence, deep learning, machine learning, autonomous cars, the Internet of things, etc. This led to an unprecedented growth in transistor density for high-end CPUs and GPUs, creating thermal design power (TDP) of even more than 700 watts for some of the NVIDIA existing GPUs. Cooling these high TDP chips with air cooling comes with a cost of the higher form factor of servers and noise produced by server fans close to the permissible limit. Direct-to-chip cold plate-based liquid cooling is highly efficient and becoming more reliable as the advancement in technology is taking place. Several components are used in the liquid-cooled data centers for the deployment of cold plate based direct to chip liquid cooling like cooling loops, rack manifolds, CDUs, row manifolds, quick disconnects, flow control valves, etc. Row manifolds used in liquid cooling are used to distribute secondary coolant to the rack manifolds. Characterizing these row manifolds to understand the pressure drops and flow distribution for better data center design and energy efficiency is important. In this paper, the methodology is developed to characterize the row manifolds. Water-based coolant Propylene glycol 25% was used as the coolant for the experiments and experiments were conducted at 21 °C coolant supply temperature. Two, six-port row manifolds' P-Q curves were generated, and the value of supply pressure and the flow rate were measured at each port. The results obtained from the experiments were validated by a technique called Flow Network Modeling (FNM). FNM technique uses the overall flow and thermal characteristics to represent the behavior of individual components.

Stochastic optimization for capacity configuration of data center microgrid thermal energy management equipment considering flexible resources

The rapid development of Internet and cloud computing technologies has led to the expansion of the scale of data centers, resulting in a continuous increase in the demand for data processing and storage. This not only results in higher energy consumption but also makes thermal management a key factor of energy management in data centers. A capacity configuration method is proposed for the core thermal management equipment of data center microgrids, electric boilers, cooling systems, and heat pumps. This method not only considers the uncertainty factors of the source and load but also effectively utilizes the characteristics of various flexible resources in data centers, such as the adjustable ability of different types of batch processing loads, air thermal inertia, and waste heat recovery, significantly improving the energy utilization efficiency and enhancing the system’s economy and flexibility. In addition, a wind power scenario generation method based on conditional least squares generative adversarial networks was proposed to improve the generation quality of wind power scenarios, and a random optimization model was constructed. Considering a certain province’s data center microgrid as a case study, the effectiveness of the model was verified, and a sensitivity analysis of the relevant parameters provided important references for microgrid planning. This study provides a new perspective and tool for the thermal management of data center microgrids and comprehensive utilization of renewable energy, which is of great significance for promoting the improvement of data center energy efficiency and sustainable development.

Scientific Integration of Operations Research and Machine Learning for Data Centre Optimization

In this study, we explore how to optimize data center operations by combining Operations Research (OR) and Machine Learning (ML) methodologies with Python-based categorization algorithms. Using Scikit-learn and TensorFlow, two Python libraries, we investigate how ML algorithms might be integrated with OR techniques like queuing theory and linear programming to forecast workloads and allocate resources more effectively. Problems with scheduling workloads, allocating resources, and managing energy consumption are at the heart of our research into data center optimization. The goal of this comprehensive framework is to create more effective and environmentally friendly data centre operations by systematically evaluating Python-based categorization models in response to changing workload demands and environmental circumstances. Introduction: The backbone of our digital infrastructure, data centers stand tall in the ever-changing world of contemporary technology. A vast variety of online services, including social media platforms, e-commerce websites, cloud computing, and big data analytics, rely on the servers, storage devices, networking gear, and other essential components housed in these expansive facilities. Meeting the ever-increasing demands for computational resources while simultaneously enhancing performance, efficiency, and cost-effectiveness is a daunting task for data centers, which are already struggling to keep up with the exponential growth in both the amount and complexity of digital data. Objectives: Our goal in writing this article is to delve into the ways in which data center optimization intersects with Operations Research and Machine Learning. Data center optimization presents a wide range of problems, and this course will explore the theory, methods, and best practices for using OR and ML to solve these problems. To develop an integrated framework that combines operations research (OR) and machine learning (ML) techniques to optimize the performance, energy efficiency, and reliability of data centers. Methods: Optimization strategies that improve data center operations in terms of performance, efficiency, and sustainability. These proposed strategies make use of both OL and ML techniques. Data center operators can optimize resource allocation, workload management, temperature control, energy consumption, and anomaly detection in real-time by formally stating the optimization problem in a mathematical framework. This allows for informed decision-making, systematic analysis of trade-offs, and the implementation of adaptive control strategies. Results: The visualization depicts the projected energy usage in terms of bandwidth for both approaches, compared to the actual values. In general, although both methods demonstrate potential, additional refinement and optimization may be necessary to attain superior outcomes in real-life situations. This discussion presents an analysis of the performance of both procedures and offers insights into their respective strengths and shortcomings, which can serve as a foundation for further investigation or improvement of the approaches. Conclusions: By using a comprehensive and multidisciplinary approach, we can optimize data centres in a way that boosts efficiency and performance while simultaneously encouraging innovation, resilience, and sustainability in data centre operations. Also, Data center gains in resource operators can achieve considerable utilization, energy efficiency, and overall system performance by integrating optimization algorithms, predictive analytics, and adaptive control strategies

Green I.T and Datacenter: a Study of Environmental Management Indicators

Objective: Identify the environmental indicators from the ABNT ISO NBR 14031 standard applicable to data centers to improve energy efficiency and environmental management. Theoretical Framework: The study addresses concepts of Green IT, practices for data center efficiency, and the indicators from the ABNT NBR ISO 14031 standard, including environmental, managerial, operational, and condition performance indicators. Method: Bibliometric research to support the theme, through searches on the Scopus and IEEE platforms, with content analysis of the main references, including the NBR ISO 14031 standards. Results and Discussion: Operational indicators from the NBR ISO 14031 standard were identified and adapted for data centers, such as the quantity of reused materials, energy used and saved, and specific emissions. The practical application of these indicators was detailed. Research Implications: The research contributes to the application of Green IT practices and energy efficiency in data centers, providing a basis for the adoption of environmental management indicators. Originality/Value: Although there are various individual studies related to Green IT or data centers, there are few studies relating these themes together. Thus, this study fills this gap by promoting an integrated approach to Green IT and environmental management, and providing support for data center managers towards Green IT.

District heating utilizing waste heat of a data center: High-temperature heat pumps

Data centers are energy-intensive facilities with substantial low-grade waste heat. High-temperature heat pumps can be critical in boosting the data center’s waste heat for district heating, improving the system-level energy efficiency of data centers, and reducing CO2 emissions in district heating. This study built thermodynamic models to assess high-temperature heat pumps with six configurations using low global warming potential refrigerants to supply heat up to 120 °C. The heat pump configurations include single-stage or two-stage cycles with advanced components, such as internal heat exchanger, economizer, flash tank, or parallel compressor. The refrigerants include R1234ze(Z), R1233ed(E), R1224yd(Z), R600, and R600a, and R245fa is used as a reference. A case study was carried out to recover the waste heat from the Frontier high-performance computing data center and provide hot water for district heating at the US Department of Energy’s Oak Ridge National Laboratory campus. The optimized performance of high-temperature heat pumps is characterized with various effectiveness of internal heat exchangers, and the operating parameters of economizer or flash tank, as well as their combination. The results show that the configurations of two-stage cycles with internal heat exchanger + flash tank and internal heat exchanger + economizer/parallel-compressor provide the highest coefficient of performance under scenarios of the maximum allowable value and a fixed value (0.3) of the internal heat exchangers’ effectiveness, respectively. R1234ze(Z) and R600a are the most promising refrigerants, considering trade-offs between the coefficient of performance and the volumetric heating capacity. The single-stage cycle with internal heat exchanger + economizer/parallel-compressor using R1234ze(Z) is recommended for utilizing Fronter’s waste heat in district heating. A one mega-watt high-temperature heat pump will reduce 33,100–33,200 metric tons of CO2 emission annually, corresponding to 85.4 %–85.6 % of equivalent CO2 emissions from natural gas boilers. This study provides good guidelines for designing and deploying high-temperature heat pumps to support sustainable data centers and decarbonize district heating in the US.

An integrated optimization method to task scheduling and VM placement for green datacenters

In the realm of cloud computing, effective resource allocation can significantly enhance the energy efficiency of datacenters. Task scheduling and Virtual Machine Placement (VMP) are two pivotal aspects of resource allocation. However, in current research, they are often treated separately, overlooking the potential for integrated optimization. In this paper, we propose an integrated solution for task scheduling and VMP in energy-efficient datacenters, based on queueing theory and Deep Reinforcement Learning (DRL) methods. This novel and comprehensive approach provides an alternative perspective for resource scheduling strategies in datacenters. We construct a queueing theory model for task scheduling, aiming to minimize the number of VMs that need to be instantiated, while ensuring that Service Level Agreement (SLA) violation remains at a low level. Furthermore, we design a VMP algorithm based on DRL for real-time selection of Physical Hosts (PHs) for deploying VMs. Finally, we conduct a simulation evaluation using a small-scale datacenter. The experimental results demonstrate that our method consistently ensures a lower rate of SLA violation. Compared to existing algorithms, the DRL-based VMP algorithm enables a more balanced utilization of the various resources in the PHs and reduces the total power consumption of the datacenter by more than 10% on average.

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.