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In this work, we mainly apply the cloud infrastructure (IaaS) and virtualization technology to provide the construction services of a green energy management system. First of all, we used MySQL Cluster database technology to build a data storage system which can solve the challenge of large demand. Digital electricity meter data and environmental information are collected efficiently and quickly in the proposed green energy management system. Next, a virtualized user-interface is provided by graphical presentation to facilitate data analysis. Finally, we control the electricity equipment to reduce Power Usage Effectiveness (PUE) and the overall power consumption target-based on this virtualized user-interface of the data analysis.

Development of renewable energy (RE) and mitigation of carbon dioxide, as the two largest climate action initiatives are the most challenging factors for new generation green data center (GDC). Reduction of conventional electricity consumption as well as cost of electricity (COE) with preferred quality of service (QoS) has been recognized as the interesting research topic in Information and Communication Technology (ICT) sector. Moreover, it becomes challenging to design a large-scale sustainable GDC with standalone RE supply. This paper gives spotlight on hybrid energy supply solution for the GDC to reduce grid electricity usage and minimum net system cost. The proposed framework includes RE source such as solar photovoltaic, wind turbine and non-renewable energy sources as Disel Generator (DG) and Battery. A hybrid optimization model is designed using HOMER software for cost assessment and energy evaluation to validate the effectiveness of the suggested scheme focusing on eco-friendly implication.

Energy-efficient Cloud Infrastructure Resource Allocation Framework is getting popularity as it is paying effective attention to cloud data management with a view to achieve maximize revenue and minimize cost. This infrastructure can encourage for both cloud providers and users for allocating cloud infrastructure resources for fulfilling not only good energy efficiency measured in Power Usage Effectiveness (PUE) and data center Infrastructure Efficiency (DCiE) but also high CPU utilization. Therefore, in this paper we proposed a framework which can show effective performance for achieving the high data center energy efficiency and preventing Service Level Agreement (SLA) violation respectively with the aim of green cloud resources deployment. The framework accomplishes cloud infrastructure resource allocation on the basic of Reinforcement Learning mechanism and Fuzzy Logic for green solutions. The evaluation for Energy-efficient Resource Allocation is experimented on the traces of the PlanetLab virtualized environment for gaining good PUE and CPU utilization.

Data centers have operational challenges due to high cooling costs and the necessity of ensuring physical security to keep data safe. The activities of malicious organizations in countries without sophisticated missile defence systems also necessitate enhancing data center security. The expending of significant power for cooling degrades data center power usage effectiveness (PUE). The use of high altitude platform data centers (HAP-DCs) is proposed to address these challenges. The HAP-DC incorporates a location mechanism which ensures that HAP-DCs have a dynamic location making them difficult targets for aerial missiles. The paper also proposes a stratospheric scheduling mechanism to enhance the HAP-DC’s PUE. The scheduling mechanism ensures that HAP-DCs sited where the stratosphere has best cooling ability are used for workload execution. Analysis shows that the location mechanism enhances safety against missile threats by at least 6.7% and up to 35.4% on average. The scheduling mechanism enhances the PUE by at least 43.8% and up to 81.1% on average.

With the rapid development of technologies such as big data and cloud computing, data communication and data computing in the form of exponential growth have led to a large amount of energy consumption in data centers. Globally, data centers will become the world’s largest users of energy consumption, with the ratio rising from 3% in 2017 to 4.5% in 2025. Due to its unique climate and energy-saving advantages, the high-latitude area in the Pan-Arctic region has gradually become a hotspot for data center site selection in recent years. In order to predict and analyze the future energy consumption and carbon emissions of global data centers, this paper presents a new method based on global data center traffic and power usage effectiveness (PUE) for energy consumption prediction. Firstly, global data center traffic growth is predicted based on the Cisco’s research. Secondly, the dynamic global average PUE and the high latitude PUE based on Romonet simulation model are obtained, and then global data center energy consumption with two different scenarios, the decentralized scenario and the centralized scenario, is analyzed quantitatively via the polynomial fitting method. The simulation results show that, in 2030, the global data center energy consumption and carbon emissions are reduced by about 301 billion kWh and 720 million tons CO2 in the centralized scenario compared with that of the decentralized scenario, which confirms that the establishment of data centers in the Pan-Arctic region in the future can effectively relief the climate change and energy problems. This study provides support for global energy consumption prediction, and guidance for the layout of future global data centers from the perspective of energy consumption. Moreover, it provides support of the feasibility of the integration of energy and information networks under the Global Energy Interconnection conception.

The increasing growth in the demand for cloud computing services, due to the increasing digital transformation and the high elasticity of the cloud, requires more efforts to improve the electrical energy efficiency of cloud data centers. In this paper, an energy-efficient hybrid (EEH) framework for improving the efficiency of consuming electrical energy in data centers is proposed and evaluated. The proposed framework is based on both the request scheduling and servers consolidation approaches rather than depending only on one approach as in the existing related works. The EEH framework sorts the customers' requests (tasks) according to their time and power needs before performing the scheduling. It has a scheduling algorithm that considers power consumption when taking its scheduling decisions. It also has a consolidation algorithm that determines the underloaded servers to be slept or hibernated, the overloaded servers, the virtual machines to be migrated and the servers that will receive migrated virtual machines. In addition, the EEH framework includes a migration algorithm for transferring migrated virtual machines to new servers. Results of simulation experiments indicate the superiority of the EEH framework to the utilization of one approach only to reduce power consumption in terms of power usage effectiveness (PUE), data center energy productivity (DCEP), average execution time, throughput and cost saving.

The power consumption of cloud data centers has a considerable impact on the environment and climate change nowadays. Researchers are seeking to find practical solutions to reduce power consumption in data centers while guaranteeing the desired level of services and service level objectives. With the establishment of the data center industry, the demand for computation and data storage has been continually rising. Energy efficiency is one of the most significant issues faced by these big data centers to meet such high computational requirements. There are many industry acceptable metrics available such as PUE, DCiE, DCP, etc. Power Usage Effectiveness (PUE) metric has proven to be the most popular in measuring energy efficiency; however, it measures the power efficiency alone with no consideration for CO2 emissions and the costs involved in total power usage across data centers. In this article, we proposed a novel Power Efficiency Measurement Calculator (PEMC) that combines and calculates the power efficiency, CO2 emissions, and the total annual costs incurred. The pseudocode and algorithm to perform these specific PUE, DCiE, and CO2 emission functions are given to explain the working of proposed work. Finally, the proposed PEMC calculator was tested and validated through a case study performed in one of the tier-level data centers in Malaysia and the results demonstrate its effectiveness compared with Power Usage Effectiveness (PUE) and other known calculators.

The need to improve the operational efficiency of terrestrial computing platforms by lowering the power usage effectiveness (PUE) figure is an important research concern. In addition, it is also important to reduce the water footprint of terrestrial computing platforms that use water for cooling and have a high water footprint. This paper proposes a network architecture that achieves the goal of reducing the PUE figure and data centre water footprint. The proposed network architecture identifies terrestrial data centres with high PUE values and replaces them with low PUE value space based data centres. This considers the case where realizing data processing via terrestrial data centres with low PUE values are infeasible. The use of terrestrial and low PUE space data centres enables the reduction of PUE (improving efficiency) the water footprint. Analysis shows that the PUE is enhanced by 30.5% on average. The water footprint is reduced by 58.1%.

This study adopts the computational fluid dynamics software−ANSYS FLUENT to predict the airflow and temperature distribution in a next-generation overhead downward flow (ODF)-type data center (DC). The study's primary objective is to minimize the entropy generation rate of the heat exchanger (HX) used in the ODF system in order to achieve energy savings. The ANSYS DesignXplorer optimization tool is employed to find the optimal design point with the smallest entropy generation rate of the HX. The predicted total exergy destruction of the DC at the optimal design point shows that the proposed A1-grade DC defined by ASHRAE TC 9.9 can save energy effectively. Moreover, the predicted power usage effectiveness (PUE) is lower than the average PUE reported in 2020 and all of the thermal performance metrics fall within the range of ideal conditions. The numerical results show that two approaches can effectively save energy in the ODF-type DC: one is to suitably increase the air-supply temperature; and the other is to suitably increase the temperature difference between the air-supply and return ports. Moreover, the separation of cold and hot aisles can effectively avoid the mixing of cold and hot airflows, resulting in an insignificant exergy destruction in the airspace.

With the development of digital economy and the arrival of 5g era, the number and scale of data centers (DCs) are developing rapidly, followed by the rapid growth of energy consumption. Compared with the traditional energy supply system, the integrated energy system (IES) realizes the cascade utilization of energy through combined cooling, heating and power (CCHP) generation, which has the characteristics of high efficiency, environmental protection and flexible operation. In this paper, an IES is designed for green DC and a multi-objective optimization mathematical model with the goal of minimizing annual total cost and carbon emission is established. The case study is tested in a green DC in East China considering different scenarios. The results show that the configuration of IES for green DC can effectively reduce the carbon emissions and the energy consumption of DC. Meanwhile, IES can reduce the index of DC's power usage effectiveness (PUE) to make it greener.

If an enterprise develops a data centre application, then it has to afford constantly increasing expenditure for its trade because the price of power consumption always increases and never decreases. If an enterprise wants to handle a power-methodical data centre, then it needs an exceptional observation for all the connections in the transmission series of power which begins from the usefulness grid attachment via the framework of data centre such as the transmission of power, support strategy, cooling, and allotment. Then they are attached to the server. If an enterprise uses conventional procedures to measure the competence of the data centre, then it provides many inappropriate outcomes. Such procedures can comprise in having ‘nameplate’ or an equipment’s estimated power numerals and reckoning the competence of the different power and ingredients of the framework of cooling for assessing the failure of power. This research provides a better straightway reckoning procedure which is named power usage effectiveness or PUE which introduces a more appropriate depiction of the competence of the data centre.

Many organizations are moving towards the cloud to meet sudden spikes due to a flash crowd. The exponential growth in requests and the offloading of the computations have increased data centers which in turn increases energy consumption and carbon footprint. This paper aims to propose an energy-efficient workload management scheme for the green cloud. The novelty of the work entails i) Developing a three-staged scheduling scheme that maps Virtual Machines with the active Physical Machines resulting in the consolidation of workload. ii) Deploying Fuzzy Decision Maker to analyze under-utilized and over-utilized servers based on the cores and the type of workload. The efficiency of the proposed system is tested through a trace-based simulation. The Power Usage Effectiveness benchmarked with standard algorithms claims that this work averagely conserves 1.9% energy by triggering 60% lesser migrations and reduces the execution time of cloud instances averagely by 6 clock cycles with a minimum Service Level Agreement Violations of 0.1%

Data centre sector is emerging as one of the largest and fastest growing industrial sectors, accounting for 3% of the global electricity supply and contributing to 4% of total greenhouse gas emissions. A framework for waste heat energy recovery and its accompanying decision support system is presented with the ability to evaluate the waste heat (source) and potential demand (sink) compatibility, perform exergy and temporal availability analysis, and undertake cost-benefits and environmental impact analysis of available heat recovery technologies. This four stages framework is implemented through the case study of a medium-sized commercial data centre to provide evidence of its applicability. The results demonstrated that the framework and decision support system can deliver a streamlined and optimised heat recovery strategy for reducing overall energy requirement in the data centre. About 68% of the inevitable waste heat from IT equipment can be recovered by the recommended solution, allowing the recovered waste heat in the form of warm water to be fed back into the facility for applications such as space heating, achieving about 10% improvement in data centre power usage effectiveness. The solutions given by the decision support system are generally accessible for most data centres as the technologies are readily available and often lead to short payback time and substantial energy savings.

The high-density computing requirements of machine learning (ML) is a challenging performance bottleneck. Limited by the sequential instruction execution system, traditional general purpose processors are not suitable for efficient ML. In this work, we present an ML system design methodology based on GPU and FPGA to tackle this problem. The core idea of our proposal is when designing an ML platform, we leverage the graphics processing unit (GPU)’s high-density computing to perform model training and exploit field programmable gate array (FPGA)’s low-latency to perform model inferencing. In between, we define a model converter, which enable transforming the model used by the training module to one that is used by inferencing module. We evaluated our approach through two use cases. The first is a handwritten digit recognition with convolutional neural network while the second use case is for predicting data center’s power usage effectiveness with deep neural network regression algorithm. The experimental results indicate that our solution can take advantages of GPU and FPGA’s parallel computing capacity to improve the efficiency of training and inferencing significantly. Meanwhile, the solution preserves the accuracy and the mean square error while converting the models between the different frameworks.

In past two decades, global data centres had become a major sector of energy consumption. In 2010, the consuming power of global data centres accounted for about 1.5% in the global electricity; however, by 2020, it was estimated that the power consumption of global data centres will account for over 8% of the global power usage. Although many outstanding practices had been conduted to improving the PUE (Power usage effectiveness) of data centres, the traditional approaches seem to have reached a ceiling by far. Meanwhile, the remarkable improvement in power efficacy of nanogenerators (NGs) in past five years, especially the discovery of triboelectric nanogenerators (TENGs), could offer intriguing prospects to further improve the PUE of data centres. Through an interdisciplinary perspective, an exploratory energy saving approach for GDCs (greening data centres) based on nanogenerators is proposed, which is defined as DEHNs (distributed energy harvesting via nanogenerators). In the proposed DEHNs, the different types of NGs are supposed to flexibly integrated into established or new data centres to further improve energy usageefficiency. Basically, as a potential ingredient in contributing to GDCs, DEHNs could offer a moderate promise, but also have limitations and challenges.

Currently, the vast amount of heat generated by electronic equipment in the data canters is removed using high energy consumption mechanical systems. Hence, there is a good potential to use waste heat recovery systems for space heating purposes in order to reduce the energy consumption, energy cost as well as pollutant emissions. In present study, the feasibility of using the air source heat pump (ASHP), as a waste heat recovery system operating with different refrigerants, in a 54-racks data center in Mashhad city of Iran has been investigated to save the electrical energy consumption of the mechanical cooling as well as the natural gas fuel consumption of the adjoining office building with area of 416 m2. The energy-environmental-economic analysis results indicated that around 35000 m3/year natural gas saving, 20.8 MW h/year electrical energy saving, 121 tons/year CO2 emission reduction and 25000$/year cost saving can be achieved through using the proposed waste heat recovery system. Furthermore, the proposed system, with a payback period of 2.5 years, can improve the power usage effectiveness (PUE), energy reuse factor (ERF) and energy reuse effectiveness (ERE) of the data center.

In this study, a thermo-economic analysis was carried out for using Air-Side Economizer (ASE), Water-Side Economizer (WSE) and Combined Air or Water Economizer (CAWE) operation mode in order to cool a data center in ten cities of Iran with various weather conditions. The energy and cost saving potentials in Mashhad municipality's data center was studied as a case study and the findings were generalized for all cities during a year. The results showed that there are energy and cost savings potential by employing CAWE operation mode for all cities except coastal cities. In addition, the Power Usage Effectiveness (PUE) of the data center can be improved by a range from 10% to 12% using CAWE cooling mode for most cities. Finally, it was found that economic index of relative net present value (RNPV) for ASE, WSE and CAWE will be positive if their relative annual potential (RAP) are higher than 12%, 23% and 35%, respectively.

The energy consumptions of data centers are increasing dramatically and cooling systems using for controlling the operating temperature usually consume more than half of these energy. Thus, effective strategies on thermal management in data centers may have benefit on energy savings. In order to improve the power usage effectiveness (PUE) of data centers, the effects of air supply flowrate by computer room air handler (CRAH), intake flowrate of rack cooling fans, air supply grilles and heat load distribution on the cooling performance of a typical small container data center having overhead air supply system are investigated in this paper. The rack cooling index (RCI) and supply heat index (SHI) are incorporated to evaluate the local and average cooling performance of data racks. It is found that the intake flowrate of rack cooling fans plays dominant role, which is suggested to be greater than the air supply flowrate in normal operation. Meanwhile, the volumetric flowrate distribution of grilles is studied. The additional front grille before entering the cold aisle may improve the RCI and SHI of front rack (rack A ), in which severe hot-air recirculation may occur in typical operation. Conversely, additional rear grille at the exit of cold aisle has negative effect on cooling performance of the last rack (rack E). Based on the measurement of local temperature distribution, the non-uniform heat load distribution in racks is proposed. Result shows that the RCI can be improved to 64% if more power is placed at lower half part, which is eligible for energy saving. However, the non-uniform heat load distribution shows limited improvement on the rear rack (rack E).

The thermal efficiency of cascading crossflow heat exchangers (HEX), networked in a parallel fashion to a cold plate, is investigated experimentally and computationally. A microcontroller is connected to the manifold system that dynamically branches coolant flow to multiple heat exchangers based on package power and its consequential junction temperature rise (Tj rise ) feedback from the heat dissipating device. Branching the recirculating coolant flow dynamically to multiple heat exchangers on a single cold plate for a heater dissipating 1kW of power, yielded a temperature reduction of 16°C compared to recirculating the coolant to just one HEX. Efficiency (η) of HEXs are characterized and a new efficiency parameter (T eff ) is proposed for characterizing heat exchanger efficiencies as an alternate to conventional HEX efficiency methods. Experimental data for a 1kW scenario on one HEX yields a system efficiency (η) of 67% and for 4 HEXs at identical power, it results in a system efficiency of 92.7%, which is a remarkable 25.7% improvement in the overall thermal efficiency of the system. A new efficiency parameter (T eff ) for HEX is introduced in this research not just to characterize the efficiency of HEX but is also proposed as an efficient compact modeling tool that reduces mesh count in the model as well. This has been adequately explained in this paper. A plug and play, adaptive, coolant flow branching system that removes human intervention and dynamically optimizes thermal efficiency based on real-time changing power conditions and achieving a junction temperature target is implemented and presented in this research. When implemented in data centers, this is projected to optimize TCO (Total Cost of Ownership) at the rack level and for large scale deployment & adoption. This method consequentially improves the overall Power Usage Effectiveness (PUE) of a data center. There have been numerous ways proposed to characterize heat exchanger efficiency specific to the design and analysis of how it is built. The proposed method is novel to the design of cascading network of crossflow heat exchangers where efficiency is shown to be closer to 100% compared to other methods.

The air free-cooling has been long thought infeasible in tropics due to the unique challenges of year-round high ambient temperature and relative humidity. In recent years, the increasing availability of servers that can tolerate higher temperatures and relative humidity levels sheds light upon the feasibility of the air free-cooling to enhance the data center energy efficiency. However, building an air free-cooled data center in the tropics requires extensive experiments to understand the details of how the tropical environment conditions will affect data center power consumption, computing throughput, and server hardware reliability. Thus, together with multiple partners in data center industry and research, we conducted a project that designs, builds, and experiments with an air free-cooled data center testbed consisting of three server rooms hosting 12 server racks with 60 kW total power rating. This paper presents the key observations, experiences and learned lessons obtained from our project. The experiments show that (1) the air free-cooling design that uses fans only can reduce the power usage effectiveness (PUE) by 38%, compared to the global average PUE, (2) the tropics' year-round high temperatures up to 37°C do not impede the air free-cooling, and (3) the implementation of the air free-cooled data centers in tropics requires special cares to deal with airborne contaminants to avoid fast corrosion rate and dust-induced server faults.

Data centers are the information factories of the digital evolution. Creating, storing, processing, distributing, and analyzing data all need energy. Therefore, data center industry consumes energy more than 2 percent of the global electricity consumption. Energy efficiency need to discuss at the outset of data center design. The root cause of oversizing data center design is the research question because this will affect investment or CAPEX and long-term operating costs or OPEX of data center as long as data center life cycle (DCLC). Data center measurement in power usage effectiveness (PUE) unit helps data center owners and consultants realized on relationship between oversizing data center design and total cost of ownership (TCO). The research results propose modular data center as a solution to handle uncertainty demand of IT equipment, scalability for growth as your need, flexibility in any size of infrastructure, fast deployment because of prefabricated design, and more efficiency by applying energy management platform called data center infrastructure management (DCIM).

The official service period of the K computer ended in 2019. Most of the equipment has been enhanced and are being used as part of the infrastructure of the Fugaku successor system. Understanding the historical facility behavior of the K computer is valuable to ensure stable and energy-efficient operations over the next decade. The K computer was powered by electricity purchased from a utility company and energy generated by gas turbine power generators on the premises. To evaluate the energy efficiency of the entire center, we use a modified power usage effectiveness (PUE) metric that considers different forms of energy source purchased from utility companies and provides the value of the metric in the service period. To analyze the effect of operational impact on PUE, we use both the facility metrics and the server operation metrics extracted from the K computer logs. Applying the metric data to three case studies reveals that some maintenance operations degrade PUE. In particular, compared to emergency operations, annual maintenance operations tend to affect the PUE metric. Finally, we provide preliminary results that indicate an operational issue regarding the gas co-generation system.

Efficiency improvements over the past decade have meant that data center energy usage has decoupled from the growth in IT workloads. Much of this efficiency improvement has been attributed to innovations made by “hyperscale” public cloud vendors, where a large proportion of new IT workloads are now being deployed. However, the move to the cloud is making it more difficult to assess the environmental impact of workloads deployed there. Although the large cloud vendors are amongst the largest purchasers of renewable electricity, customers do not have access to the data they need to complete emissions assessments under the Greenhouse Gas Protocol. Data such as Power Usage Effectiveness, emissions factors and equipment embodied energy are not available from public cloud vendors. This paper demonstrates how the Greenhouse Gas Protocol method of assessment of IT emissions does not work for public cloud environments and suggests how this can be tackled by the cloud vendors themselves.