TAPO: Thermal-aware power optimization techniques for servers and data centers

Modern data centers must provide performance assurance for complex system software such as multi-tier web applications. In addition, the power consumption of data centers needs to be minimized to reduce operating costs and avoid system overheating. Various power-efficient performance management strategies have been proposed based on dynamic voltage and frequency scaling (DVFS). Virtualization technologies have also made it possible to consolidate multiple virtual machines (VMs) onto a smaller number of active physical servers for even greater power savings, but at the cost of a higher overhead. This paper proposes a performance-controlled power optimization solution for virtualized data centers with multi-tier applications. While existing work relies on either DVFS or server consolidation in a separate manner, our solution utilizes both strategies for maximized power savings by integrating feedback control with optimization strategies. At the application level, a multi-input-multi-output controller is designed to achieve the desired performance for applications spanning multiple VMs, on a short time scale, by reallocating the CPU resources and DVFS. At the data center level, a power optimizer is proposed to incrementally consolidate VMs onto the most power-efficient servers on a longer time scale. Empirical results on a hardware testbed demonstrate that our solution can effectively achieve performance-assured power savings. Extensive simulation results, based on a trace file of 5,415 real servers, demonstrate the efficacy of our solution in large-scale data centers.

This paper introduces an improved real-time Model Predictive Climate Control (MPCC) technique to reduce power consumption in Heating, Ventilation and Air Conditioning (HVAC) system of the Battery Electric Vehicles (BEVs). In the presented control technique, the fan, heating and cooling power usage is optimized considering the effect of the ambient temperature. Moreover, the need of a physical temperature measurement sensor is eliminated using an ambient temperature observer. In a typical BEV, the HVAC load is significant and it influences the overall vehicle performance and driving range. Therefore, a real-time control system capable of maintaining desired cabin temperature while achieving maximum HVAC efficiency is highly desirable. In the proposed MPCC, the optimum split of the battery power between the heating\cooling and the fan power is identified as a function of ambient temperature through an offline optimization process and used in the form of a lookup-table for real-time implementation. It is demonstrated that the proposed MPCC improves energy consumption efficiency of the entire HVAC system up to 5% by dynamically adapting to the variations in the ambient temperature. Furthermore, owing to the online temperature observation process, the proposed HVAC control system allows elimination of ambient temperature sensor and corresponding maintenance efforts. In this paper, the performance of the proposed MPCC is evaluated over a single-zone HVAC model of a BEV in MATLAB\Simulink <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> environment.

Cooling power constitutes a large portion of the total electrical power consumption in data centers. Approximately 25%∼40% of the electricity used within a production data center is consumed by the cooling system. Improving the cooling energy efficiency has attracted a great deal of research attention. Many strategies have been proposed for cutting the data center energy costs. One of the effective strategies for increasing the cooling efficiency is using dynamic thermal management. Another effective strategy is placing cooling devices (heat exchangers) closer to the source of heat. This is the basic design principle of many hybrid cooling systems and liquid cooling systems for data centers. Dynamic thermal management of data centers is a huge challenge, due to the fact that data centers are operated under complex dynamic conditions, even during normal operating conditions. In addition, hybrid cooling systems for data centers introduce additional localized cooling devices, such as in row cooling units and overhead coolers, which significantly increase the complexity of dynamic thermal management. Therefore, it is of paramount importance to characterize the dynamic responses of data centers under variations from different cooling units, such as cooling air flow rate variations. In this study, a detailed computational analysis of an in row cooler based hybrid cooled data center is conducted using a commercially available computational fluid dynamics (CFD) code. A representative CFD model for a raised floor data center with cold aisle-hot aisle arrangement fashion is developed. The hybrid cooling system is designed using perimeter CRAH units and localized in row cooling units. The CRAH unit supplies centralized cooling air to the under floor plenum, and the cooling air enters the cold aisle through perforated tiles. The in row cooling unit is located on the raised floor between the server racks. It supplies the cooling air directly to the cold aisle, and intakes hot air from the back of the racks (hot aisle). Therefore, two different cooling air sources are supplied to the cold aisle, but the ways they are delivered to the cold aisle are different. Several modeling cases are designed to study the transient effects of variations in the flow rates of the two cooling air sources. The server power and the cooling air flow variation combination scenarios are also modeled and studied. The detailed impacts of each modeling case on the rack inlet air temperature and cold aisle air flow distribution are studied. The results presented in this work provide an understanding of the effects of air flow variations on the thermal performance of data centers. The results and corresponding analysis is used for improving the running efficiency of this type of raised floor hybrid data centers using CRAH and IRC units.

We proposed a workload assignment policy for reducing power consumption by air conditioners in data centers. Power consumption was estimated by computational fluid dynamics for both the conventional equipment arrangement and the tandem arrangement newly developed to reuse the exhaust heat from servers. To reduce the air conditioner power consumption by raising the temperature set points of the air conditioners, the temperatures of all server back-planes were equalized by moving workload from the servers with the highest temperatures to the servers with the lowest temperatures. Consequently, the air conditioners' power consumption was reduced by 10.4% in the conventional arrangement. In the tandem arrangement, the air conditioners' power consumption was reduced by 53%, and also the total power consumption of the whole data center was exhibited to be reduced by 23% by reusing the exhaust heat from the servers.

All-inverter coupled air-side free cooling technology is an energy-saving technology that applies inverter technology to a coupled air conditioning system with liquid pump driven heat pipes and vapor compression refrigeration, allowing it to fully utilize outdoor air cooling resources. In order to comprehensively analyze the energy saving effect of this technology when applied to a data center, we designed and implemented an energy consumption monitoring and testing platform using a data center air-cooled refrigeration air conditioning system as a retrofit and research object. Then, we monitored and analyzed the operating parameters of the heating, ventilation, and air conditioning (HVAC) system before and after the retrofit on this platform, and combined the actual application of this technology in different temperature and altitude regions in China to analyze the energy efficiency of the all-inverter coupled air-side free cooling technology. The results show that: (1) The all-inverter coupled air-side free cooling technology can achieve smooth control of the air conditioning system, reduce power system shock caused by frequent system start-up, and improve system stability. (2) The technology has improved outdoor temperature sensitivity and can make better use of natural outdoor air cooling sources, with energy savings of up to 46.1% when comparing the hottest and coldest seasons. (3) Application results in various regions of China show that the all-inverter coupled air-side free cooling technology is more versatile and achieves good results in both old data center retrofit and new data center construction, with the lowest power usage effectiveness (PUE) value reaching 1.228.

Green technology is the emerging new sustainable technology which paves the way for lowering the raise in the earth’s temperature. The increase in toxic waste from the electronic devices in every level of usage increases the global warming and thus the need of green technology is inevitable. The contributions towards green computing in global community will save the environment by minimizing the energy consumption. Emerging technologies in Internet of things, pervasive applications, cloud computing provides effective consumer, scientific and business domains. Due to the massive data storage and computation of cloud computing, there is an exponential increment in the demand of cloud computing. The growth of enormous amount of data brings a huge challenge to the cloud computing environment. Data centers are the main module of the cloud computing environment, but the major issue is the management of power computing in those data centers. Green data centers are established with limited power consumption and carbon-di-oxide emission. The research work mainly focusses on the delay time of the data centers in Internet cloud which in turn increases the usage of power consumption in data centers. The analysis is simulated with Internet cloud and data centers.

Due to the growing demand on high efficient heat ventilation and air conditioning (HVAC) systems, how to improve the efficiency of HVAC system regarding reduces energy consumption of system has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependent upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the fault detection and isolation (FDI) system plays a crucial role for identifying failures. Finding healthy HVAC source as the reference for health monitoring is the main aim in this area. To dispel this concern a comprehensive transient model of heat ventilation and air conditioning (HVAC) systems is developed in this study. The transient model equations can be solved efficiently using MATLAB coding and simulation technique. Our proposed model is validated against real HVAC system regarding different parts of HVAC. The developed model in this study can be used for a pre tuning of control system and put to good use for fault detection and isolation in order to accomplish high-quality health monitoring and result in energy saving. Fan supply consider as faulty device of HVAC system with six fault type. A sensitivity analysis based on evaluated model shows us three features are sensitive to all faults type and three auxiliary features are sensitive to some faults. The magnitude and trait of features are a good potential for automatic fault tolerant system based on machine learning systems

A cloud computing paradigm enables users to access services, applications, and infrastructure resources by using thin clients anywhere and at any time. In this paradigm, multiple users can share cloud infrastructure resources. The application or service requests from a user can be abstracted as a virtual network (VN) request and can be submitted to the cloud-based data centers. How to map a VN onto the cloud infrastructure network is a challenging issue in cloud resource provisioning. Thus, efficient mapping techniques that intelligently use the resources of cloud infrastructure are important and necessary. Current research on VN mapping and design focuses on resource-efficient VN mapping or cost-efficient VN mapping. However, there is another important issue in cloud-based data centers that we must pay attention to, i.e., the amount of power or energy that is consumed by a data center. The power consumption in data centers can be a significant percentage of the total power consumption, and it not only leads to a higher data center operating cost but also contributes to carbon emissions and the greenhouse effect. In this paper, we propose a power-efficient resource provisioning technique in cloud-based data centers while complying with service level agreements. We first model a power-efficient VN provisioning problem as a mathematical optimization problem, with the objective of minimizing the power consumption by employing mixed-integer programming. We then propose a heuristic algorithm to efficiently solve this model since this optimization problem is NP-hard. We validate and evaluate our framework and algorithm by conducting extensive simulations on different cloud infrastructure networks under various scenarios. The simulation results show that our approach performs well.

Heating, Ventilation, and Air Conditioning (HVAC) systems are major energy consumers in buildings, challenging the balance between efficiency and occupant comfort. While prior research explored generative AI for HVAC control in simulations, real-world validation remained scarce. This study addresses this gap by designing, deploying, and evaluating “Office-in-the-Loop,” a novel cyber-physical system leveraging generative AI within an operational office setting. Capitalizing on multimodal foundation models and Agentic AI, our system integrates real-time environmental sensor data (temperature, occupancy, etc.), occupants’ subjective thermal comfort feedback, and historical context as input prompts for the generative AI to dynamically predict optimal HVAC temperature setpoints. Extensive real-world experiments demonstrate significant energy savings (up to 47.92%) while simultaneously improving comfort (up to 26.36%) compared to baseline operation. Regression analysis confirmed the robustness of our approach against confounding variables like outdoor conditions and occupancy levels. Furthermore, we introduce Data-Driven Reasoning using Agentic AI, finding that prompting the AI for data-grounded rationales significantly enhances prediction stability and enables the inference of system dynamics and cost functions, bypassing the need for traditional reinforcement learning paradigms. This work bridges simulation and reality, showcasing generative AI’s potential for efficient, comfortable building environments and indicating future scalability to large systems like data centers.

Energy savings and occupant thermal comfort are the two most important factors in controlling heating ventilation and air conditioning (HVAC) operation in buildings. Typically, it is found that thermal comfort is not always met in buildings. Hence, there is still an opportunity to improve indoor thermal comfort, and at the same time save energy by controlling HVAC set points. The objective of this paper is to propose a method to obtain energy savings by adjusting HVAC set points based on occupant comfort measured using Predicted Mean Vote (PMV) and occupancy information. The idea is to calculate hourly PMV values based on real-time occupancy information, indoor temperature set points and humidity in a building. Then, a new set of temperature set points that can maintain occupant comfort, i.e., PMV = 0, is derived. To evaluate the effectiveness of the proposed method, a building model is developed in eQUEST using the information from a real-world building located in Alexandria, VA. Research findings indicate that HVAC electrical consumption savings of 14.58% is achieved when the proposed set point adjustment method is implemented as compared to that of the base case. To study the impact of adding occupancy information on HVAC energy savings, another scenario is simulated where HVAC set point is increased when the building is unoccupied, e.g., during lunchtime or holidays. Research findings indicate that additional HVAC electrical consumption savings of 8.79% is achieved when taking into account occupancy information in HVAC control.

Data center optimization using PID regulation in CFD simulations

Recent work has presented hierarchical deployment of energy storage devices (ESDs) at the data center, rack, and server levels within a data center, along with a corresponding control framework for peak power shaving and energy cost reduction under (time-of-use) dynamic energy pricing policies. However, the prior work does not use a realistic power delivery architecture of the data center with hierarchical ESD structure, and fails to account for some key characteristics such as rate capacity effect of batteries and power losses in various AC/DC and DC/DC converters in the power delivery architecture. This paper aims to overcome these shortcomings by (i) adopting a realistic power delivery architecture (from Intel) for centralized ESD structure as the starting point, (ii) presenting a novel power delivery architecture for data centers with hierarchical ESD structure, borrowing the best features of the centralized ESD structure from Intel and the distributed single-level ESD structures from Google and Microsoft, (iii) providing a mathematical framework for the optimal design (i.e., ESD provisioning) and control (i.e., Scheduling the charging and discharging of various ESDs) of the hierarchical ESD structure to minimize overall energy cost under dynamic energy pricing functions. This framework accounts for constraints on ESD volume (for each level) and the overall (annually amortized) capital cost, and power losses due to the rate capacity effect and conversion circuitry. The ESD design problem is solved by using a search-based algorithm, whereas the ESD control problem is formulated and solved as a hierarchical convex optimization algorithm. Experiments have been conducted using real Google cluster workload based on realistic data center specifications, demonstrating the effectiveness of the proposed optimal design and control framework.

The heating, ventilation, and air conditioning (HVAC) system plays a crucial role in ensuring the safety of workers and preventing the release of gaseous radioactive materials into the environment during the decommissioning of a nuclear power plant (NPP). To establish an HVAC operation strategy, decommissioning phases were divided into four stages, and the HVAC systems were reclassified. In addition, assumptions have been made regarding design modifications and maintenance for the reactor containment building (RCB) HVAC, fuel handling building (FHB) HVAC, and main control room (MCR) HVAC. Based on these, for RCB HVAC operation, natural ventilation and RCB purge operation during the transition period are proposed. In the decommissioning stage, recirculation operation, entire ventilation operation consisting of continuous operation and purge operation, and finally partial ventilation operation to purify local space were proposed. Moreover, during the transition period, the FHB HVAC was proposed to operate as normal NPP, and the MCR HVAC was suggested to operate with safety-related equipment removed.

Performance-controlled server consolidation for virtualized data centers with multi-tier applications

Higher power consumption in data centers results in more heat dissipation, cooling costs and degrades the system reliability. Conventional power reduction techniques such as dynamic voltage/frequency scaling (DVS/DFS) have disadvantages when they are ported to current data centers with virtualization deployments. In this paper, we give a short survey and discussion on some issues and aspects of DVS/DFS in data centers. This paper also presents a simple comparison of four power management schemes in virtualization environments.