Analysis of methods for assessing the energy efficiency of data centers using the power usage effectiveness method

The article deals with the analysis of methods for assessing the energy efficiency of data centers according to the Power Usage Effectiveness method. The demand for data centers which consumes a large amount of electricity is growing with the growth of digitalization and the accumulation of big data in the network. The energy consumption of the cooling system for the machine room accounts for a significant part of the operating costs of the building. Free cooling in a refrigeration system reduces energy consumption much more than operating systems with a vapor-compression cycle. In 2006 according to The Green Grid, the assessment method of Power Usage Effectiveness has become an international standard for measuring energy efficiency and is widely used in the design and operation of data centers. In this regard, the operation principles of free-cooling chillers are considered. The calculation example of the system payback in free-cooling is also given.

Evaluation and Optimization of a Two-Phase Liquid-Immersion Cooling System for Data Centers

An efficient cooling system for data centers can boost the working efficiency of servers and promote energy savings. In this study, a laboratory experiment and computational fluid dynamics (CFD) simulation were performed to explore the performance of a two-phase cooling system. The coefficient of performance (COP) and partial power usage effectiveness (pPUE) of the proposed system was evaluated under various IT (Information Technology) loads. The relationship between the interval of the two submerged servers and their surface temperatures was evaluated by CFD analysis, and the minimum intervals that could maintain the temperature of the server surfaces below 85 °C were obtained. Experimental results show that as server power increases, COP increases pPUE decreases. In one experiment, the COP increased from 19.0 to 26.7, whereas pPUE decreased from 1.053 to 1.037. The exergy efficiency of this system ranges from 12.65% to 18.96%, and the tank side accounts for most of the exergy destruction. The minimum intervals between servers are 15 mm under 1000 W of power, 20 mm under 1500 W, and more than 30 mm under 2000 W and above. The observations and conclusions in this study can be valuable references for the study of cooling systems in data centers.

New Data Center Performance Index: Perfect Design Data Center—PDD

Data Centers (DC) are specific buildings that require large infrastructures to store all the information needed by companies. All data transmitted over the network is stored on CDs. By the end of 2020, Data Centers will grow 53% worldwide. There are methodologies that measure the efficiency of energy consumption. The most used metric is the Power Usage Effectiveness (PUE) index, but it does not fully reflect efficiency. Three DC’s located at the cities of Curitiba, Londrina and Iguaçu Falls (Brazil) with close PUE values, are evaluated in this article using the Energy Usage Effectiveness Design (EUED) index as an alternative to the current method. EUED uses energy as a comparative element in the design phase. Infrastructure consumption is the sum of energy with Heating, Ventilating and Air conditioning (HVAC) equipment, equipment, lighting and others. The EUED values obtained were 1.245 (kWh/yr)/(kWh/yr), 1.313 (kWh/yr)/(kWh/yr) and 1.316 (kWh/yr)/(kWh/yr) to Curitiba, Londrina and Iguaçu Falls, respectively. The difference between the EUED and the PUE Constant External Air Temperature (COA) is 16.87% for Curitiba, 13.33% for Londrina and 13.30% for Iguaçu Falls. The new Perfect Design Data center (PDD) index prioritizes efficiency in increasing order is an easy index to interpret. It is a redefinition of EUED, given by a linear equation, which provides an approximate result and uses a classification table. It is a decision support index for the location of a Data Center in the project phase.

Simulation-Based Assessment of Performance Indicator for Data Center Cooling Optimization

Data center power consumption has grown substantially in the past 20 years. According to the United States Data Center Energy Usage Report, the data center consumption in 2014 was estimated at 70 billion kWh, which accounted for 1.8% of the total U.S. electricity. The present effort investigates the effects of various data center parameters on a new set of metrics called the performance indicator to help assess and optimize the cooling performance of data centers. The three metrics in the performance indicator include power usage effectiveness ratio (PUEr), thermal conformance, and thermal resilience. The data center parameters investigated include computer room air handler (CRAH) setpoints, room configuration layouts, and containment strategies. The results show that the CRAH setpoint significantly influences the PUEr with higher setpoint values resulting in lower PUEr values. Room configuration layout changes and containment strategies showed substantial effects on thermal conformance and thermal resilience. The thermal conformance was increased approximately 10% with room configuration changes without changing the PUEr. Full hot aisle containment also improved the thermal conformance by 7.4%.

Architecture and System Design for Marine Cloud Computing Assets

The Ocean provides benefits of free cooling for cloud computing platforms. However, the use of the ocean for hosting cloud platforms needs to consider three challenges. The first challenge is identifying suitable underwater locations for siting underwater data centres. The second is designing a low-cost method for acquiring underwater data centres. The third is designing a mechanism ensuring that the use of the ocean for hosting data centres is scalable. This paper proposes the intelligent marine compute locator (IMCL) to identify suitable locations for siting underwater data centres. The proposed IMCL determines the specific heat capacity of different ocean locations at multiple epochs. In addition, the conversion of end-of-life vessels into artificial reefs that host open-source disaggregated hardware computing payload is proposed to reduce acquisition costs. The use of disaggregated architecture enables multiple cloud service providers to use limited ocean locations. The formulated metrics are the power usage effectiveness (PUE) and ocean space utilization (OSU). Simulations show that the use of disaggregated design architecture instead of non-disaggregated architecture (existing mechanism) enhances the PUE and OSU by 4.4 and 16.4% on average, respectively.

Capacity Management of Hyperscale Data Centers Using Predictive Modelling

Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the data centers of the world running. It has become crucial for large IT companies to monitor the energy efficiency of their data-center facilities and to take actions on the optimization of these heavy electricity consumers. This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center. The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE), significantly improving the accuracy of PUE prediction. This model has been evaluated by using real-world data collected from a Facebook data center located in Luleå, Sweden. In addition, to prove the robustness of the predictive model, it has been compared with other machine learning techniques, such as an Artificial Neural Network (ANN) and an Adaptive Neuro Fuzzy Inference System (ANFIS), where it showed a better result. Further, due to the flexibility of the BRBES-based predictive model, it can be used to capture the nonlinear dependencies of many variables of a data center, allowing the prediction of PUE with much accuracy. Consequently, this plays an important role to make data centers more energy-efficient.

A Systems Overview of Commercial Data Centers

Data center facilities play a vital role in present and forthcoming information and communication technologies. Internet giants, such as IBM, Microsoft, Google, Yahoo, and Amazon hold large data centers to provide cloud computing services and web hosting applications. Due to rapid growth in data center size and complexity, it is essential to highlight important design aspects and challenges of data centers. This article presents market segmentation of the leading data center operators and discusses the infrastructural considerations, namely energy consumption, power usage effectiveness, cost structure, and system reliability constraints. Moreover, it presents data center network design, classification of the data center servers, recent developments, and future trends of the data center industry. Furthermore, the emerging paradigm of mobile cloud computing is debated with respect to the research issues. Preliminary results for the energy consumption of task scheduling techniques are also provided.

Power Usage Effectiveness analysis and optimization in the INFN CNAF Tier-1 data center infrastructure

The accurate calculation of the power usage effectiveness (PUE) is the most important factor when trying to analyse the overall efficiency of the power consumption in a big data center. In the INFN CNAF Tier-1 a new monitoring infrastructure, also known as Building Management System (BMS), has been recently implemented using the Schneider StruxureWare™ Building Operation (SBO) software. During the design phase of this new BMS, a great attention was given to the possibility of collecting several detailed information about the electric absorption of specific devices and parts of the facility. Considering the annual trends and the demands for reducing the operating costs it became clear that some improvements were certainly needed in the very short time. For this reason, a hardware upgrade of the cooling chillers and related chilled water pumps distribution system was seriously considered using innovative cooling technology. We focused on chillers using the Danfoss Turbocor centrifugal compressors technology that uses magnetic levitation and an oil-free approach for obtaining the best efficiency. Subsequently, we studied a solution that could easily compensate the initial investment during the first years of usage (considering the Total Cost of Ownership of the project) and that will improve the overall PUE of our data center.