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The tremendous growth of big data analysis and IoT (Internet of Things) has made cloud computing an integral part of society. The prominent problem associated with data centers is the growing energy consumption, which results in environmental pollution. Data centers can reduce their carbon emissions through efficient management of server power consumption for a given workload. Dynamic voltage frequency scaling (DVFS) can be applied to control the operating frequencies of the servers based on the workloads assigned to them, as this approach has a cubic increment relationship with power consumption. This research work proposes two DVFS-enabled host selection algorithms for virtual machine (VM) placement with a cluster selection strategy, namely the carbon and power-efficient optimal frequency (C-PEF) algorithm and the carbon-aware first-fit optimal frequency (C-FFF) algorithm.The main aims of the proposed algorithms are to balance the load among the servers and dynamically tune the cooling load based on the current workload. The cluster selection strategy is based on static and dynamic power usage effectiveness (PUE) values and the carbon footprint rate (CFR). The cluster selection is also extended to non-DVFS host selection policies, namely the carbon- and power-efficient (C-PE) algorithm, carbon-aware first-fit (C-FF) algorithm, and carbon-aware first-fit least-empty (C-FFLE) algorithm. The results show that C-FFF achieves 2% more power reduction than C-PEF and C-PE, and demonstrates itself as a power-efficient algorithm for CO2 reduction, retaining the same quality of service (QoS) as its counterparts with lower computational overheads.

The use of big data leads to higher demands for hyperscale data centers (HDCs) in terms of the scale and quantity required for data storage and processing. Before the construction of an HDC, it is necessary to comprehensively analyze the economic budget according to the energy requirements and potential energy cost. We propose a global energy consumption prediction framework based on the power usage effectiveness (PUE) calculation that considers all heat sources and power consumption. The framework integrates physical models and a statistical framework that combines IT equipment energy consumption and data center energy consuming predictions. Furthermore, the framework provides a method to calculate the carbon emissions and electricity cost of the data center. Using hourly meteorological data as climate parameters, combined with a limited range of energy parameters, the annual PUE values of 60 regions were estimated, and a further analysis of the Carbon Usage Effectiveness (CUE) and electricity costs in China was conducted as an example. Based on experimental validation and an evaluation of real-time data, our framework can predict the overall energy consumption of HDCs effectively, filling a gap in HDC research in the Asia-Pacific region and providing a basis for HDC feasibility analysis.

Air cooling is the traditional solution to chill servers in data centers. However, the continuous increase in global data center energy consumption combined with the increase of the racks’ power dissipation calls for the use of more efficient alternatives. Immersion cooling is one such alternative. In this paper, we quantitatively examine and compare air cooling and immersion cooling solutions. The examined characteristics include power usage efficiency (PUE), computing and power density, cost, and maintenance overheads. A direct comparison shows a reduction of about 50% in energy consumption and a reduction of about two-thirds of the occupied space, by using immersion cooling. In addition, the higher heat capacity of used liquids in immersion cooling compared to air allows for much higher rack power densities. Moreover, immersion cooling requires less capital and operational expenditures. However, challenging maintenance procedures together with the increased number of IT failures are the main downsides. By selecting immersion cooling, cloud providers must trade-off the decrease in energy and cost and the increase in power density with its higher maintenance and reliability concerns. Finally, we argue that retrofitting an air-cooled data center with immersion cooling will result in high costs and is generally not recommended.

With the acceleration of the digitization process, the load of the data center on the power grid continues to increase. During the operation of the data center, the load demand on the power grid is relatively large, and the load demand on the power grid fluctuates greatly. In the Power industry, the average PUE(power usage effectiveness) of data center is above 2, which is extremely unfriendly to the power grid. This paper proposes and summarizes the optimization parameters at various levels, the energy efficiency of software and hardware, and provides a method for optimization through global variables for high-efficiency and energyconsuming data centers, which can minimize cooling energy consumption and IT energy consumption. To build a electric power grid-friendly characteristic data center with high efficiency and low energy consumption.

Data center hosting is in higher demand to fulfill the computing and storage requirements of information technology (IT) and cloud services platforms which need more electricity to power on the IT devices and for data center cooling requirements. Because of the increased demand for data center facilities, optimizing power usage and ensuring that data center energy quality is not compromised has become a difficult task. As a result, various machine learning-based optimization approaches for enhancing overall power effectiveness have been outlined. This paper aims to identify and analyze the key ongoing research made between 2015 and 2021 to evaluate the types of approaches being used by researchers in data center energy consumption optimization using Machine Learning algorithms. It is discussed how machine learning can be used to optimize data center power. A potential future scope is proposed based on the findings of this review by combining a mixture of bioinspired optimization and neural network.

The purpose of this study is to design a novel custom power demand forecasting algorithm based on the LSTM Deep-Learning method regarding the recent power demand patterns. We performed tests to verify the error rates of the forecasting module, and to confirm the sudden change of power patterns in the actual power demand monitoring system. We collected the power usage data in every five-minute resolution in a day from some groups of the residential, public offices, hospitals, and industrial factories buildings in one year. In order to grasp the external factors and to predict the power demand of each facility, a comparative experiment was conducted in three ways; short-term, long-term, seasonal forecasting exp[eriments. The seasonal patterns of power demand usages were analyzed regarding the residential building. The overall error rates of power demand forecasting using the proposed LSTM module were reduced in terms of each facility. The predicted power demand data shows a certain pattern according to each facility. Especially, the forecasting difference of the residential seasonal forecasting pattern in summer and winter was very different from other seasons. It is possible to reduce unnecessary demand management costs by the designed accurate forecasting method.

Underwater data centers (UDCs) use the ocean’s cold-water resources for free cooling and have low cooling costs. However, UDC cooling is affected by marine heat waves, and underwater seismic events thereby affecting UDC functioning continuity. Though feasible, the use of reservoirs for UDC cooling is non–scalable due to the high computing overhead, and inability to support continuity for long duration marine heat waves. The presented research proposes a mobile UDC (capable of migration) to address this challenge. The proposed UDC migrates from high underwater ground displacement ocean regions to regions having no or small underwater ground displacement. It supports multiple client underwater applications without requiring clients to develop, deploy, and launch own UDCs. The manner of resource utilization is influenced by the client’s service level agreement. Hence, the proposed UDC provides resilient services to the clients and the requiring applications. Analysis shows that using the mobile UDC instead of the existing reservoir UDC approach enhances the operational duration and power usage effectiveness by 8.9–48.5% and 55.6–70.7% on average, respectively. In addition, the overhead is reduced by an average of 95.8–99.4%.

This study develops a bottom-up cost optimization model (DC-DECOM) to evaluate decarbonization pathways for Japan’s data center industry, targeting carbon neutrality of the information and communications technology (ICT) sector by 2040. The model represents Power Usage Effectiveness (PUE) as a dynamic function of ambient temperature and cooling technology, and integrates technology selection, regional energy supply, and carbon pricing within a single cost-minimization framework. Three scenarios are compared: a reference case (REF), a centralized carbon-neutral scenario (C-CN) that restricts new capacity to metropolitan areas, and a regional decentralization scenario (R-CN) that allows for nationwide siting. Input parameters are calibrated against data from the International Energy Agency (IEA), the Uptime Institute, Japan’s Ministry of Internal Affairs and Communications (MIC) White Papers, and the Japan Science and Technology Agency (JST). The R-CN scenario achieves the 2040 net-zero target at 18–23% lower total system cost than C-CN. The cost gap decomposes into four channels (cooling-energy reduction ∼35%, lower regional renewable procurement cost ∼30%, lower carbon cost ∼25%, and lower siting-related cost ∼10%). Sensitivity analysis identifies the carbon-price trajectory and the hardware-efficiency improvement rate as the most influential parameters; the R-CN advantage remains positive across all ±1σ parameter variations and across two combined-scenario stress tests.

Inference now dominates the lifecycle footprint of large language models, yet published estimates often use inconsistent boundaries and optimize carbon while ignoring water. We present a provider-agnostic framework that unifies scope-transparent measurement with time-resolved, SLO-aware orchestration and jointly optimizes carbon and consumptive water. Measurement reports daily medians at a comprehensive serving boundary that includes accelerators, host CPU/DRAM, provisioned idle, and PUE uplift, and provides accelerator-only whiskers for reconciliation. Optimization uses a mixed-integer linear program solved over five-minute windows; it selects region, batch size, and phase-aware hardware for prefill and decode while enforcing p95 TTFT and TPOT as well as capacity constraints. Applied to four representative models, a single SLO-aware policy reduces comprehensive-boundary medians by 57 to 59 percent for energy, 59 to 60 percent for water, and 78 to 80 percent for location-based CO2, with SLOs met in every window. For a day with 500 million queries on GPT-4o, totals fall from 0.344 to 0.145 GWh, 1.196 to 0.490 ML, and 121 to 25 t CO2 (location-based). The framework offers a deployable template for carbon- and water-aware LLM serving with auditable and scope-transparent reporting.

Distillery industrial wastewater (DIW) was tested for color and COD removal percentages using an electrochemical and advanced oxidation processes (AOPs). Specifically, the study compared direct/alternating–current–electrocoagulation (DC‐EC/AC‐EC), sono (US), and direct/alternating current–electrocoagulation coupled with sono (US) (DC‐EC/US and AC‐EC/US) processes. Also evaluated were the effects of these procedures on the power needed to treat DIW. Experimental results showed that compared to single processes such as DC‐EC, AC‐EC, US, hybrid DC‐EC/US, and the hybrid AC‐EC/US process achieved a total color elimination efficiency of 100 % and a COD elimination efficiency of 100 % while using a lower power consumption of 4.76 kWhrm−3. The effects of important operational factors such treatment duration, cycle of pulse duty, sonication power, current density, chemical oxygen demand, electrode spacing, electrode pairing, pH, concentration of electrolyte on the % removal of COD and power usage of DIW were investigated using hybrid AC‐EC/US process. When using a Fe/Fe electrode combination, the effectiveness of COD removal was shown to be enhanced by increasing the treatment duration, current, US power, and decreasing the COD concentration, electrode spacing. The study also provided the results of an investigation into the synergistic index between AC‐EC and US process and operational cost. Based on its ability to efficiently and effectively remove contaminants from wastewater and industrial effluent, the AC‐EC/US approach stands out among the other methods. Sono‐alternating current‐electrocoagulation: Electrochemical assisted oxidation procedure removed color, COD, and calculated power utilization. With less energy than other process, hybrid AC‐EC/US eliminated entire color and COD. Hybrid AC‐EC/US approach was used to investigate significant operating parameters. Synergistic index and operating cost of the US and AC‐EC processes were investigated.