XPUE: Extending Power Usage Effectiveness Metrics for Cloud Infrastructures

The current research investigates the effects of numbers and arrangements of the cooling units on the thermal and energy management of in-row cooling architectures of data centers. Three different configurations with different numbers of cooling units; namely one rack between each two cooling units (Configuration A), two racks between each two cooling units (Configuration B), and three racks between each two cooling units (Configuration C), are numerically investigated. A scaled physical module of data center rack is experimentally constructed and tested for model validation. Two thermal evaluation indices (Supply Heat Index SHI and Index of Mixing IOM) and two energy efficiency metrics (Beta Index β and Energy Utilization Index ηr) are used to investigate, evaluate, and compare the thermal management and energy efficiency of the three configurations. The results show that the three configurations have good thermal homogeneity and the same good values of the thermal and energy efficiency metrics at the intermediate racks. However, problems of hot air circulations with hot spots and bad performances indices were noticed at terminal racks of the racks array. The energy utilization index is in the optimal range (about 2) for the intermediate rack and drop to 1.5 for the terminal racks. Economic study shows that configuration B has the minimum capital cost and configuration A has the minimum needed white area with a capital cost very close to that of B. Accordingly, configuration B is economically recommended, and configuration A is recommended for limited white area.

Data centers are energy-intensive facilities, with over 95% of their total cooling load attributed to the heat generated by information technology equipment (ITE). Various energy-saving techniques have been employed to enhance data center efficiency and to reduce power usage effectiveness (PUE). Among these, economizers using outdoor air for cooling are the most effective for addressing year-round cooling demands. Despite the simplicity of the load composition, analyzing data center cooling systems involves dynamic considerations, such as weather conditions, system conditions, and economizer control. A PUE interpretation tool was specifically developed for use in data centers, aimed at addressing the simplicity of data center loads and the complexity of system analysis. The tool was verified through a comparison with results from DesignBuilder implementing the EnergyPlus algorithm. Using the developed tool, a comparative analysis of economizer strategies based on the PUE distribution was conducted, with the aim of reducing the PUE of data centers across various climatic zones. The inclusion of evaporative cooling (EC) further improved cooling efficiency, leading to reductions in PUE by approximately 0.02 to 0.05 in dry zones. Additionally, wet zones exhibited PUE reductions, ranging from approximately 0.03 to 0.07, with the implementation of indirect air-side economizer (IASE). Sensitivity and uncertainty analysis were further conducted. The computer room air handler (CRAH) supply temperature and CRAH temperature difference were the most influential factors affecting the annual PUE. For the direct air-side economizer (DASE) and DASE + EC systems, higher PUE uncertainty was observed in zones 1B, 3B, 4B, and 5B, showing ranges of 1.17–1.39 and 1.15–1.17, respectively. In the case of the IASE and IASE + EC systems, higher PUE uncertainty was noted in zones 0A, 0B, 1A, 1B, and 2A, with ranges of 1.22–1.43 and 1.17–1.43, respectively. The distinctive innovation of the tool developed in this study is characterized by its integration of specific features unique to data centers. It streamlines the computation of cooling loads, thus minimizing the burden of input, and delivers energy consumption data for data center cooling systems with a level of precision comparable to that of commercial dynamic energy analysis tools. It provides data center engineers with a valuable resource to identify optimal alternatives and system design conditions for data centers. This empowers them to make informed decisions based on energy efficiency enhancements, thereby strengthening their ability to improve energy efficiency.

Water-energy tradeoffs in data centers: A case study in hot-arid climates

Energy and spectral efficiencies are key metrics to assess the performance of networks and compare different configurations or techniques. There are many ways to define those metrics, and the performance indicators used in their calculation can also be measured in different ways. Using an LTE-A network, we measure different performance indicators and the metrics' outputs are compared. Modifying the transmitted output power, the bandwidth, and the number of base stations, different network configurations are also compared. As expected, the measurements show that increasing the bandwidth increases the throughput more than it increases the energy consumption. Results clearly show that using inappropriate indicators can be misleading. The power indicator should include all energy consumed and the throughput should be dependent on the traffic, taking into account the idle time of the network, if any. There is a need to include more performance indicators into the metrics, especially those related to quality of service.

Unit process energy consumption analysis and models for electron beam powder bed fusion (EB-PBF): Effects of chamber saturation

Historically compute server performance has been the most important pillar in the evaluation of datacenter efficiency, which can be measured using a variety of industry standard benchmarks. With the introduction of industry standard servers, price-performance became the second pillar in the 'efficiencyequation'. Today with an increased awareness in the industry for power optimized designs and corporate initiatives to reduce carbon emissions, data center efficiency needs to incorporate yet another key element in this equation: energy efficiency. Initial models based on 'name-plate' power consumption have been used to estimate energy efficiency while recently industry standard consortia like SPEC, TPC and SPC have started amalgating new energy metrics with their traditional performance metrics. TPC-Energy, enables the measuring and reporting of energy efficiency for transaction processing systems and decision support systems. In this paper we analyze TPC C benchmark configurations that may achieve leadership results in TPC-Energy using existing, more energy efficient technologies, such as solid states drives for storage subsystems, low power processors and high density DRAM in back end server and middle tier systems. Even though the study is based on TPC-C configurations these configuration optimizations are applicable to other benchmarks and production systems alike. We envision that the energy efficiency metrics and related optimizations to claim benchmark leadership will accelerate development and qualifications of energy efficient component and solutions.

It is essential to maximise the lifetime of a given multicast tree, in order to keep the nodes active as long as possible in Wireless Ad hoc Networks (WANETs). Due to the impact of hop count on energy efficiency, any efficient routing protocol for WANETs should have low hop count. However, the problem of generating the optimised energy efficient routing protocol for WANETs is NP-hard. Any workable heuristic solution is, therefore, highly desirable in this case. To take into account the trade-off between the lifetime and the hop count in routing of multicast tree in WANETs, this paper defines a new metric termed Energy Efficiency Metric (EEM). Theoretical analyses show that it is efficient to fully characterise the energy efficiency of WANETs. A distributed routing algorithm called Maximum Lifetime and Minimum Hop-count (MLMH) is then proposed with the aim that extends the lifetime while minimising the maximal hop count of a source-based multicast tree in WANETs. Simulation results provide evidence that MLMH achieves satisfactory performance.

Overhead and underground MIMO low voltage broadband over power lines networks and EMI regulations: Towards greener capacity performances

The biggest challenge for heterogeneous network is co-channel interference problem, and due to the presence of interference, the system energy efficiency reduced greatly. In previous studies that use of space resources to eliminate interference between heterogeneous networks , most are based on the receiving end with a single antenna case to use zero forcing (ZF) beam-forming algorithm , however in practice the receiving end often configure multiple antennas, so this article use Block Diagonalization (BD) beam-forming algorithm to eliminate inter-cell interference after considering the coordinated multi-point transmission processing technology. This paper defines a energy efficiency metrics and convert it into a convex optimization problem by mathematical methods, and the improved energy efficiency programs is obtained ultimately.

Energy efficiency (EE), energy consumption cost and environmental impact are vibrant challenges to cloud computing and data centers. Reducing energy consumption and emissions of carbon dioxide (CO2) in data centers represent open areas and driving force for future research work on green data centers. Our Literature review reveals that there are currently several energy efficiency frameworks for data centers which combine a green IT architecture with specific activities and procedures that led to decrease the impact on environment and less CO2 emissions. The current available frameworks have some pros and cons that is the reason why there is an urgent need for an integrated criterion for selecting and adopting energy efficiency framework for data centers. The required energy efficiency framework criteria should also consider the social network applications as a vital related factor in elevating energy consumption, as well as high potential for better energy efficiency in data centers. Additionally, in this paper, we highlighted the importance of the identification of efficient and effective energy efficiency metric that can be used for the measurement and determination of the value of data centers efficiency and their performance combined with sound and empirically validated integrated EE framework.

Optimal energy efficiency capacity under joint design of sensing and transmission duration for cognitive radio system

The article focuses on developing the key tasks and vectors of the Sustainable Development Strategy for Ukraine’s telecommunications enterprises until 2030 in alignment with the UN Sustainable Development Goals (SDGs). It examines the current state of the telecommunications sector, addressing its economic, social, and environmental challenges. Using statistical indicators such as investment volumes, broadband penetration levels, and energy efficiency metrics, five priority vectors are identified: innovative infrastructure, inclusivity, environmental sustainability, cybersecurity, and partnership. Unique opportunities for this industry are highlighted, namely: implementation of 5G and IoT: 5G technologies and the Internet of Things open up prospects for smart cities and industry; green technologies: the use of solar panels and wind turbines for base stations can reduce energy consumption; integration with the EU: harmonization of standards with ISO and ITU will help attract foreign investment. A strategic development model is proposed, emphasizing the implementation of 5G and Internet of Things (IoT) technologies, improvements in energy efficiency, and harmonization of national standards with ISO and ITU frameworks. The study employs SWOT analysis, comparative analysis, and forecasting methods to outline future trajectories. The results include practical recommendations for shaping telecommunications policy to ensure sustainable growth by 2030, with particular attention to rural connectivity expansion, green technology integration, and enhanced cybersecurity measures. By linking these initiatives to SDG 9 (industry, innovation, infrastructure) and SDG 11 (sustainable cities), the research demonstrates how coordinated efforts can accelerate digital transformation while meeting global sustainability benchmarks. The findings are intended for use by policymakers, telecommunications operators, and international partners seeking to align development strategies with global best practices and promote an inclusive, resilient, and secure telecommunications ecosystem in Ukraine.

This paper considers the problem of maximizing energy efficiency on the downlink of two-tier wireless backhaul small cell heterogeneous networks, where an interference mitigation strategy that combines reverse time division duplexing and equally orthogonal spectrum splitting is proposed. By enabling the small cell access points with the capability of switching ON/OFF, we develop a joint design of transmit beamforming, power and small cell selection that maximizes the proposed weighted access energy efficiency metric. To better convey the total power consumption model, we assume the adaptive decoding power model at each small cell access point. The formulated problem is combinatorial and non-convex, which is NP-hard in general. Hence, to find a more realistic close-to-optimal feasible solution, we iteratively approximate the non-convex constraints in the formulated problem as second order cone ones based on the first order Taylor convex approximation and error-controlled second order cone approximation. The problem arrived at each iteration is a mixed integer second order cone programming, which can be solved optimally and efficiently by available dedicated solver to achieve the final result at convergence. Numerical results are studied to show the improvement of our proposed model compared to previous works.

This study focuses on enhancing indoor air quality and thermal comfort in indoor swimming pool facilities through the investigation of ventilation system configurations. Creating a comfortable and healthy environment in these facilities is crucial for the well-being of occupants and overall operational efficiency. The performance of the ventilation system significantly influences user comfort, energy consumption, and air quality. This research aims to analyze the impact of different ventilation system configurations on indoor air quality and thermal comfort parameters using computational fluid dynamics (CFD) simulations.To achieve the research objectives, CFD simulations were conducted using ANSYS Fluent ®, a widely used commercial CFD package. The simulations involved solving the governing equations for continuity, momentum, energy, and species transport, along with employing the k-epsilon turbulence closure model. A high-resolution mesh with over 5.6 million elements accurately captured the flow regimes and related phenomena.The study investigated various aspects of ventilation system configurations, including the placement and design of inlets and outlets, airflow rates, and distribution patterns. Evaluations were made based on key performance indicators such as indoor air quality parameters, thermal comfort indices, and energy efficiency metrics. Comparisons were made between different configurations to identify the most effective strategies for enhancing indoor air quality and thermal comfort.The findings of the study demonstrate the importance of ventilation system design in achieving optimal indoor air quality and thermal comfort in indoor swimming pool facilities. The results indicate that specific configuration choices, such as the use of circular inlets in the ceiling for improved spectator comfort and rectangular inlets in the side walls for better performance in the swimming pool area, can significantly impact thermal conditions and air distribution. Additionally, the study emphasizes the need for appropriate inlet grille height to ensure adequate air mixing and thermal comfort.The outcomes of this research provide valuable insights for architects, engineers, and facility managers involved in the design, construction, and operation of indoor swimming pool facilities. By understanding the impact of different ventilation system configurations, stakeholders can make informed decisions to optimize indoor air quality, thermal comfort, and energy efficiency. Ultimately, this research contributes to the development of sustainable and comfortable indoor swimming pool environments that cater to the needs of occupants and enhance their overall experience.

Future wireless communication systems require efficient and flexible baseband receivers. Meaningful efficiency metrics are key for design space exploration to quantify the algorithmic and the implementation complexity of a receiver. Most of the current established efficiency metrics are based on counting operations, thus neglecting important issues like data and storage complexity. In this paper we introduce suitable energy and area efficiency metrics which resolve the afore-mentioned disadvantages. These are decoded information bit per energy and throughput per area unit. Efficiency metrics are assessed by various implementations of turbo decoders, LDPC decoders and convolutional decoders. An exploration approach is presented, which permit an appropriate benchmarking of implementation efficiency, communications performance, and flexibility trade-offs. Two case studies demonstrate this approach and show that design space exploration should result in various efficiency evaluations rather than a single snapshot metric as done often in state-of-the-art approaches.