Flexibility Management of Data Centers to Provide Energy Services in the Smart Grid

To determine whether a High-Performance Computing (HPC) data center is energy efficient, various aspects have to be taken into account: the data center's power distribution and cooling infrastructure, the HPC system itself, the influence of the system management software, and the HPC workloads; all can contribute to the overall energy efficiency of the data center. Currently, two well-established metrics are used to determine energy efficiency for HPC data centers and systems: Power Usage Effectiveness (PUE) and FLOPS per Watt (as defined by the Green500 in their ranking list). PUE evaluates the overhead for running a data center and FLOPS per Watt characterizes the energy efficiency of a system running the High-Performance Linpack (HPL) benchmark, i.e. floating point operations per second achieved with 1 watt of electrical power. Unfortunately, under closer examination even the combination of both metrics does not characterize the overall energy efficiency of a HPC data center. First, HPL does not constitute a representative workload for most of today's HPC applications and the rev 0.9 Green500 run rules for power measurements allows for excluding subsystems (e.g. networking, storage, cooling). Second, even a combination of PUE with FLOPS per Watt metric neglects that the total energy efficiency of a system can vary with the characteristics of the data center in which it is operated. This is due to different cooling technologies implemented in HPC systems and the difference in costs incurred by the data center removing the heat using these technologies. To address these issues, this paper introduces the metrics system PUE (sPUE) and Data center Workload Power Efficiency (DWPE). sPUE calculates the overhead for operating a given system in a certain data center. DWPE is then calculated by determining the energy efficiency of a specific workload and dividing it by the sPUE. DWPE can then be used to define the energy efficiency of running a given workload on a specific HPC system in a specific data center and is currently the only fully-integrated metric suitable for rating an HPC data center's energy efficiency. In addition, DWPE allows for predicting the energy efficiency of different HPC systems in existing HPC data centers, thus making it an ideal approach for guiding HPC system procurement. This paper concludes with a demonstration of the application of DWPE using a set of representative HPC workloads.

KEYWORD: Data center; DC power supply; PUE; energy efficiency ABSTRACT: Data center energy consumption is increasing with the rapid development of data cen- ter in recent years. This paper firstly elaborated data center energy saving technologies from three aspects: the power supply system, the air conditioning system and the IT epuipments, then carried on the energy efficiency comparative analysis of high voltage DC(HVDC) power supply technology and the traditional UPS system power supply technology. We come to the conclusion that the high vol- tage DC power supply technology obtained the better energy efficiency than the traditional UPS sys- tem power supply technology. A metric used to determine the energy efficiency of data center Before the discussion of the data center's energy efficiency, the metric used to measure the ener- gy efficiency of data center should be determined. Generally, EUI (Energy use intensity) was used to determine the enegy efficiency for common buildings. However, there will be some problems if EUI was used to determine the enegy efficiency of data center. Because of the wide variety and dif- ferent lay out of IT equipments, there will be a big difference in heat dissipation per unit area of IT equipment in different data centers. In this context, the EUI metric lost its significance, a new me- tric is needed to determine the energy efficiency of data center. The energy consumption of data centers is mainly distributed in IT equipment, air conditioning system, power supply and distribution system loss, etc. According to the statistical analysis of Ameri- can National Environmental Protection Bureau, the typical data center's energy consumption struc- ture is shown in Figure 1.

Energy efficiency of data centers (DCs) has become a major concern as DCs continue to grow large often hosting tens of thousands of servers or even hundreds of thousands of them. Clearly, such a volume of DCs implies scale of data center network (DCN) with a huge number of network nodes and links. The energy consumption of this communication network has skyrocketed and become the same league as computing servers’ costs. With the ever-increasing amount of data that need to be stored and processed in DCs, DCN traffic continues to soar drawing increasingly more power. In particular, more than one-third of the total energy in DCs is consumed by communication links, switching and aggregation elements. In this paper, we concern the energy efficiency of data center explicitly taking into account both servers and DCN. To this end, we present VPTCA, as a collective energy-efficiency approach to data center network planning, which deals with virtual machine (VM) placement and communication traffic configuration. VPTCA aims particularly to reduce the energy consumption of DCN by assigning interrelated VMs into the same server or pod, which effectively helps reduce the amount of transmission load. In the layer of traffic message, VPTCA optimally uses switch ports and link bandwidth to balance the load and avoid congestions, enabling DCN to increase its transmission capacity, and saving a significant amount of network energy. In our evaluation via NS-2 simulations, the performance of VPTCA is measured and compared with two well-known DCN management algorithms, Global First Fit and ElasticTree. Based on our experimental results, VPTCA outperforms existing algorithms in providing DCN more transmission capacity with less energy consumption.

An Increase in Data Center power requirements has placed significant pressure on traditional Data Center cooling management Systems. The temperature in the Data Center is controlled using Air handling units (AHUs) and plays a critical role in a Data Center to maintain the required temperature to ensure the best possible performance. As the targeted Data Center is quite Old and using backdated technologies and does not have sensor-based technologies implemented. One of the issues faced by the target Data Center was that AHU fan speed was set to the static setting which impacts the Supplied Temperature in Data Center and results in excessive hot & cold temperature inside a Data Center. The proposed model resolves the problem faced by the targeted Data Center to operate the AHU fan speed to maintain the required DC Temperature on the predicted range by using machine learning techniques. This model not only solves the problem of maintaining the necessary temperature in the Data Center, but it can also regulate the fan speed within the expected range, contributing to the Data Center's energy efficiency. Supervised machine learning with linear regression and logistical regression approaches are utilized to investigate which methodology produces the best prediction results for adjusting the AHU unit fan speed for better control of the supplied Data Center temperature. In the targeted Data Center, it has no scope to expand more rack space or host IT load. It is desired that the predicted or recommended range for controlling AHU fan speed be determined so that the needed temperature can be sustained with the suggested setting without requiring extensive manual task. Henceforth as the data generated by the Data Center is historical, supervised regression machine learning models using Linear and Logistic Regression techniques are used. Both regression models are compared to see which regression methodology predicts the best variable fan speed range for maintaining the data center's required temperature.

Global digitalization has given birth to the explosion of digital services in approximately every sector of contemporary life. Applications of artificial intelligence, blockchain technologies, and internet of things are promising to accelerate digitalization further. As a consequence, the number of data centers, which provide the services of data processing, storage, and communication services, is also increasing rapidly. Because data centers are energy-intensive with significant and growing electricity demand, an energy model of data centers with temporal, spatial, and predictive analysis capability is critical for guiding industry and governmental authorities for making technology investment decisions. However, current models fail to provide consistent and high dimensional energy analysis for data centers due to severe data gaps. This can be further attributed to the lack of the modeling capabilities for energy analysis of data center components including IT equipment and data center cooling and power provisioning infrastructure in current energy models. In this research, a technology-based modeling framework, in hybrid with a data-driven approach, is proposed to address the knowledge gaps in current data center energy models. The research aims to provide policy makers and data center energy analysts with comprehensive understanding of data center energy use and efficiency opportunities and a better understanding of macro-level data center energy demand and energy saving potentials, in addition to the technological barriers for adopting energy efficiency measures.

Virtual machine (VM) consolidation and switch/path consolidation are two typical techniques for improving energy efficiency in data centers (DCs). Most of existing work separately optimize VM consolidation and switch consolidation which results in inferiority of the optimization performance. Moreover, these work usually handle a user application as a VM flow (i.e., a source VM is connected to a destination VM). In practice, however, relation of VMs could be much more complex than the single flow and multiple VMs are connected via a network. In this work, we address a general DC energy optimization problem that enables tenants to express their applications by a general resource request graph (i.e., computation requests of VMs, bandwidth requests of VM communications, and time requests of VM execution). We propose a joint VM-switch consolidation (JVSC for short) algorithm to this problem. JVSC jointly optimizes the energy consumption of DCs in three steps: (i) it decreases the number of active PMs by VM consolidation; (ii) it decreases the number of active switches by switch consolidation at the tor tier, the aggregation tier and the core tier of the network, respectively; and (iii) it minimizes energy consumption of VM migration via an energy-aware migration strategy. Extensive experiments are conducted on both simulated applications and real Google cluster usage traces. Experimental results demonstrate that JVSC can save 60% around energy of DCs, compared to the state-of-the-art.

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.

Issues in Energy Efficiency of Data Centers (DC) are important, due to the cumulative effects of the increase in the DCs number and in the energy consumption per center. Developing new design recommendations to improve a cooling system efficiency, commonly quantified by the PUE metric (Power Usage Effectiveness) is one objective of the Green IT organizations. For existing DCs, without considering the optimization of the IT workload, a possible way to improve the DC's energy efficiency is to adjust the cooling setpoints. In this paper, a methodology based on predictive models is used to optimize the PUE by improving the cooling setting. The modeling approach consists in exploiting the temperatures and energy measurements at various operating conditions to predict the PUE behavior using data-driven models commonly called black box models. The optimization procedures are based on the simulation of these models in order to estimate the best working conditions.

A data centre is an important component in any organisation as it plays a key role in its growth and success; it has become the most popular cost-effective platform for hosting large scale applications. While more data centres are being implemented and existing facilities are continuously expanding in order to meet the so ever increasing demand, the global network of data centres has become similar to the electricity grid, yet the comparison fails dramatically when it comes to the matter of energy efficiency and cost. Data centres incur frightening costs and in some case spiral out control as regards to power consumption and cooling. An efficient method for saving energy in data centres is to dynamically adjust the data centre compute capacity resources. Nonetheless, this is a challenging solution as it will require thorough understanding of the hosted applications, the resource demand characteristics and the impact on the service level agreement (SLA). In this paper, we investigate the possibility of providing an intelligent control mechanism that manages compute resource capacity dynamically which reduces data centre energy while meeting the performance requirements by means of simulation and extensive analysis using Google's trace workload real data, we will demonstrate how our proposed approach can achieve significant energy savings while meeting the performance requirements.

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.

The NLR portion of the "Cloud & Infrastructure CoP - Data Center Energy and Efficiency with AI Adoption" web meeting will cover data center locations, energy use and load growth, best practices, performance metrics, transition to direct liquid cooled data center equipment, and NLR's approach to optimizing data center.

To environmental friendly and energy-efficient data centers, it is prudent to leverage on-site renewable sources like solar and wind. Data centers deploy distributed UPS systems to handle the intermittent nature of renewable energy. We propose a renewable-energy manager called REDUX, which offers a smart way of managing server energy consumption powered by a distributed UPS system and renewable energy. REDUX maintains a desirable balance between renewable-energy utilization and data center performance. REDUX makes judicious use of UPS devices to allocate energy resources when renewable energy generation is low or fluctuate condition. REDUX not only guarantees the stable operation of daily workload, but also reduces the energy cost of data centers by improving power resource utilization. Compared with existing strategies, REDUX demonstrates a prominent capability of mitigating average peak workload and boosting renewable-energy utilization.

Data centers occupy less than 2% of the federally owned portfolio under the jurisdiction, custody or control of the U.S. General Services Administration (GSA), but represent nearly 5% of the agency’s overall energy budget. Assuming that energy use in GSA’s data centers tracks with industry averages, GSA can anticipate that data center energy use will grow at an annual rate of 15%, a doubling of energy use every five years.1 In fact, energy is the single largest operating expense for most data centers. Improving the energy performance of data center systems supports progress toward meeting federally mandated greenhouse gas emission-­reduction goals, while reducing operating and energy costs and allowing for greater flexibility in future expansion by eliminating the need to provide additional power and cooling. Studies sponsored by the U.S. Department of Energy (DOE) and the U.S. Environmental Protection Agency (EPA) have shown that energy use can be reduced by 25% through implementation of best practices and commercially available technologies. The present study evaluated the effectiveness of a strategy to cost- effectively improve the efficiency of data center cooling, which is the single largest non-­IT load. The technology that was evaluated consists of a network of wireless sensors—including branch circuit power monitors, temperature sensors, humidity sensors, and pressure sensors, along with an integrated software product to help analyze the collected data. The technology itself does not save energy; however, its information collection and analysis features enable users to understand operating conditions and identify problem areas. In addition, data obtained by this technology can be input into assessment tools that can identify additional best practice measures. Energy savings result from the implementation of the best practices. The study was conducted to validate the premise that providing data center operators with detailed, real- time measurement of environmental parameters and power consumption enables them to establish baseline performance, discover areas of sub-optimal performance, and identify concrete opportunities for improvement.

Data centers have a huge impact on the world we live in. Today data centers account for 3% of the global electricity supply and consume more power than the total of some country consumption. Data centers also contribute 2% of the total global greenhouse gas emissions. Data center's energy efficiency is the eternal hot research and engineering hot topic. In this paper, an advanced distributed power backup battery power system (BBS) design is introduced, also engineering comparison between lead-acid battery (VRLA), lithium iron phosphate battery (LFP) and ternary lithium-ion battery (NCM) is detailed listed with key parameters. Moreover, this paper has proved the best engineering practice of using the cylindrical lithium iron phosphate (LiFePO4) battery for BBS design optimization considering the reliability, cost saving, and energy efficiency of data center deployment in a large scale. Engineering work summarized in this paper includes a serial of related tests of the electrical performance, life cycle and reliability items, and the corresponding test results reaching design targets of power supply capacity, energy efficiency improvement for data center which eventually contributed a total cost ownership (TCO) saving. The theoretical basis, proto product, engineering test result and deployment analysis of LiFePO4 battery backup power system is data-proved reference for ecosystem in their new data center construction.

The server is recognized as one of the main energy consumption equipment in data center, of which accurate energy consumption modeling is the key of improving the data center's energy efficiency. This paper proposes a data-driven based energy consumption modeling for the heterogeneous servers popularized in the modern data center, i.e. it constructs a polynomial based energy consumption model by using the real-time server performance data published in the SPECpower_ssj 2008. The results illustrate that the server can have different energy consumption characteristics even with the same CPU and it also shows the superiority of the proposed model. Lastly, this paper takes the data center with 1000 servers as an example, a comprehensive comparison analysis between the proposed model and the existing server energy consumption models is conduced, and the results show the accuracy of the proposed model and the effectiveness to track the actual energy consumption profile.