Optimization of underfloor air distribution systems for data centers based on orthogonal test method: A case study

In the presence of latency sensitive geo-distributed applications, users require fast service for their queries. Cloud computing provides physical servers from its data center in order to process user requests. The cloud data center consumes a huge amount of energy due to lack of management of the data center servers as the container-based service consolidation is a nontrivial task. Since the containers require less resource footprint, consolidating it in servers might make resource availability sparse. In order to reduce the energy consumption of the cloud data center, we have proposed a green container-based consolidation of the services so that the maximum number of servers can be put into idle mode without affecting the application quality of experience. The service consolidation problem has been formulated as an optimization problem considering minimization of total energy consumption of the data center as the objective, and an algorithm named Energy Aware Service consolidation using baYesian optimization (EASY) has been proposed to solve the optimization. We have evaluated the EASY algorithm in simulation using python. The experimental results have shown that EASY improves the total energy consumption of the data centers. This improvement comes at the cost of a small increase of service response time as there exists a trade-off between energy consumption and service response time.

Cooling is a major component in the enormous energy consumption in data centers. Accurate evaluation of cooling inside a data center forms the backbone of all the attempts for improving cooling efficiency. Models based on computational fluid dynamics (CFD) are typically used for accurate evaluation, but have a drawback of high computation time. This paper presents a novel thermal predictor to evaluate data center cooling in seconds. The key idea is to extract information from a single instance of CFD simulation using metrics called as influence indices to build the fast thermal predictor. Then, this predictor can evaluate the cooling for altered operation of data center with comparable accuracy in seconds without the need for repetitive CFD simulations. This paper demonstrates the accuracy of the thermal predictor by comparing with CFD simulations for a sample, but realistic data center. The fast thermal predictor then successfully passed more challenging tests in a real production data center and proved its practical utility. The results of the thermal predictor compared with measurements carried out in the production data center are also presented. This fast thermal predictor is an important milestone in the development of a method for model-based real time control of data center cooling.

High energy consumption in data center has become a key problem, and the Carbon (CO $$_2$$ ) emissions from data center have serious impacts on environment. To improve traditional performance-energy model and minimize energy consumption in cloud data center, an energy-efficient and QoS-aware (EEQoS) model is developed for virtual resources consolidation. The EEQoS model is designed to investigate the trade-off between energy and QoS. The user’s satisfaction degree and Euclidean distance of resources are also respectively applied to the QoS model and power model. In addition, this EEQoS model is combined with the particle swarm optimization algorithm by setting the power consumption per QoS value as the objective function to consolidate virtual resources in data center. Compared with traditional power model, the proposed model reduces response time by 27.2%, cost by 31.4%, SLA violations by 40.5% and improves throughput by 13.5% on average while increasing less than 3.8% energy consumption during the whole test period.

The growth of energy consumption in data centers is becoming one of the significant problems all over the world. The power usage of IT devices and air-conditioning (AC) units accounts for up to three-quarters of total energy consumption in typical data centers. This study discusses the effectiveness of using shutter-controlled air flow management to control cold air flow into the servers. It can impact the power consumption of both, the servers and the AC units. A raised floor data center has two types of aisles - cold and hot. In most cases, excessive volumes of cold air are pumped into the servers using cold aisles. We are proposing the utilization of a shutter to control the air flow and reduce wastage while preventing damage due to increased CPU temperature. The shutter is intended to control the air flow and maintain the temperature appropriately according to the workload of the servers. We evaluated the proposed system for controlling the flow of air into the servers. The results show that cold air volumes can be reduced by up to 92% while maintaining appropriate control of CPU temperature.

Using Model Predictive Control in Data Centers for Dynamic Server Provisioning

Complete immersion of servers in dielectric mineral oil has recently become a promising technique for minimizing cooling energy consumption in data centers. However, a lack of sufficient published data and long-term documentation of oil immersion cooling performance make most data center operators hesitant to apply these approaches to their mission critical facilities. In this study, a single server was fully submerged horizontally in mineral oil. Experiments were conducted to observe the effects of varying the volumetric flow rate and oil inlet temperature on thermal performance and power consumption of the server. Specifically, temperature measurements of the central processing units (CPUs), motherboard (MB) components, and bulk fluid were recorded at steady-state conditions. These results provide an initial bounding envelope of environmental conditions suitable for an oil immersion data center. Comparing with results from baseline tests performed with traditional air cooling, the technology shows a 34.4% reduction in the thermal resistance of the system. Overall, the cooling loop was able to achieve partial power usage effectiveness (pPUECooling) values as low as 1.03. This server level study provides a preview of possible facility energy savings by utilizing high temperature, low flow rate oil for cooling. A discussion on additional opportunities for optimization of information technology (IT) hardware and implementation of oil cooling is also included.

Cloud computing is the combination of grid computing, distributed computing and utility computing. Cloud computing provides various types of services (servers and storage facility) in the on demand basis. The main goal of the cloud computing is to preserve and organize the very huge data center or data forms. The data forms are composed of thousands of servers that absorb the giant (ample) amount of electricity in the word of energy. The decreasing the energy consumption in datacenter is the major challenge in cloud computing now a day. This research article is going to address the problem of high energy consumption at datacenter. Concentrate on virtual machine scheduling in cloud datacenter with Dynamic Voltage Frequency Scaling (DVFS) approach. We have combined shortest job first and Round Robin algorithms with Vibrant Quantum. This combination of algorithm is considered as shortest round vibrant queue (SRVQ) algorithm. SRVQ reduces the waiting time of the scheduling process and minimize the starvation. The DVFS and SRVQ worked together and produced fruitful results in the final experiments. This work reduced the server’s energy consumption in the cloud data center. In the final results, our proposed framework exhibits 45% of energy efficiency compare to other previously proposed algorithms. 33% of QoS performance were enhanced by our framework.

Comparative study on different energy-saving plans using water-side economizer to retrofit the computer room air conditioning system

The 2007 US EPA report to Congress (US EPA, 2007) on the state of energy consumption in data centers brought to light the true energy inefficiencies built into today’s data centers. Marquez et al. (2008) conducted an initial analysis on the productivity of a Pacific Northwest National Lab computer using The Green Grid’s Data Center Energy Productivity metric (The Green Grid, 2008). Their study highlights how the Top500 ranking of computers disguises the serious energy inefficiency of today’s High Performance Computing data centers. In the rapidly expanding Cloud Computing space, the race will be won by the providers that deliver the lowest cost of computing — such cost is heavily influenced by the operational costs incurred by data centers. As a means to address the urgent need to lower the cost of computing, solution providers have been intensely focusing on real-time monitoring, visualization, and control/management of data centers. The monitoring aspect involves the widespread use of networks of sensors that are used to monitor key data center environmental variables such as temperature, relative humidity, air flow rate, pressure, and energy consumption. Such data is then used to visualize and analyze data center problem areas (e.g., hotspots), which is then followed by control/management actions designed to alleviate such problem areas. The authors have been researching the operational benefits of a network of sensors tied in to a software package that uses the data to visualize, analyze, and control/manage the data center cooling system and IT Equipment for maximum operational efficiency. The research is being conducted in a corporate production data center that is networked in to the authors’ company’s global network of data centers. Results will be presented that highlight the operational benefits that are realizable through real-time monitoring and visualization.

Energy consumption of data center has increased dramatically due to the massive computing demands driven from every sector of the economy. Hence, data center energy management has become very important for operating data centers within environmental standards while achieving low energy cost. In order to advance the understanding of thermal management in data centers, relevant environmental information such as temperature, humidity and air quality are gathered through a network of real-time sensors or simulated via sophisticated physical models (e.g. computational fluid dynamics models). However, sensor readings of environmental parameters are collected only at sparse locations and thus cannot provide a detailed map of temperature distribution for the entire data center. While the physics models yield high resolution temperature maps, it is often not feasible, due to computational complexity of these models, to run them in real-time, which is ideally required for optimum data center operation and management. In this work, we propose a novel statistical modeling approach to updating physical model outputs in real-time and providing automatic scheduling for re-computing physical model outputs. The proposed method dynamically corrects the discrepancy between a steady-state output of the physical model and real-time thermal sensor data. We show that the proposed method can provide valuable information for data center energy management such as real-time high-resolution thermal maps. Moreover, it can efficiently detect systematic changes in a data center thermal environment, and automatically schedule physical models to be re-executed whenever significant changes are detected.

Environmental impacts of ever-growing computational requirements have raised worldwide concerns and significant efforts have been dedicated to reducing power consumption, water usage, and eventually the carbon footprint. Cloud architecture and services have undergone substantial development to become more sustainable and reliable with implementing advanced cooling solutions. In this research, a bottom-up approach has been presented to investigate the energy optimization opportunities of an air-liquid hybrid cooling method compared to the pure air cooling for a 1.7 MW data center. A gradual transition from 100% air cooling to 25%–75% air and liquid cooling has been studied to capture the changes in IT, fan, facility, and the total data center power consumption. Various system design optimizations such as supply air temperature (SAT), facility chiller water temperature, economization and secondary fluid temperature are embedded in this work to highlight the importance of proper setpoint conditions on both primary and secondary sides. Computational fluid dynamics (CFD) and flow network modeling (FNM) are utilized to precisely assess the performance of air and liquid cooling by evaluating the required flow rate, pressure drop, and critical case temperature of computing components as well as temperature change of cooling medium. Energy consumption of the selected cooling equipment is measured based on the BIN data for CRAH and CDU’s performance models. Power usage effectiveness (PUE) measured and compared with Total Usage Effectiveness (TUE) which appears to be a more suitable metric to weigh a data center’s design efficiency by not limiting the fan power to the IT boundary. For the most optimized case, we obtained up to 27% lower consumption in the facility power and 15.5% lower usage in the whole data center site. Increasing the percentage of liquid cooling contribution significantly diminishes the power intake which addresses concerns about natural resources limit as one of the most critical requirements of a sustainable design.

Data centers are serious, energy-hungry infrastructures that can run large scale Internet based services. Energy ingesting representations are essential in designing and improving energy-efficient operations to reduce excessive energy consumption in data centers. This paper presents a survey on Energy efficiency in data centers, importance of energy efficiency. It also describes the increasing demands for data center in worldwide and the reasons for data centers energy inefficient? In this paper we define the challenges for implementing changes in data centers and explain why and how the energy requirements of data centers are growing. After that we compare the German data center market at international level and we see the energy consumption of data centers and servers in Germany from 2010 -2016.

Cooling solutions in an operational data centre: A case study

The main purpose of this paper is to improve the resilience of data center cooling system and reduce the energy consumption in data center by using the Tilted Active Tile (TATs). We deploy multiple TATs in cold aisle environment then analyze the thermal performance of racks. TATs also help to improve the reliability in case of cooling failure by increased ride through time. We observed an extended ride‐through time for data centers deployed with Tilted Active Tiles (TATs) when the CRAC blower failed. Proposed approach can maintain cooling system temperature for up to 10 minutes. We also introduced ANN based thermal models to predict the thermal performance of TATs, To train the ANN models, we collect the rack inlet and CRAC outlet temperature from Data Center. We also fixed blank panel in empty U part of racks and then analyzed that the thermal performance of racks are better and temperature distribution at bottom, middle and top of the rack were same under 30°angle. The airflow of TATs and passive tile influence on surrounding racks. That’s why we analyzed the disturbance of TATs on surrounding rack and observed that the TATs at 30°angle has positiveimpact on the surrounding racks.

Increasing demand for Cloud infrastructure and services leads to the challenges for management and maintenance of large data Center. Data center is fully equipped with huge number of resources. Those resources consumes energy in spite of their partial or full utilization. As a result data center consumes lots of energy, which in turn increases the total cost of operation and carbon footprint in environment. These concern leads to “Green Computing”, i.e. to reduce total operational cost, Carbon Footprint in environment and efficient usage of the computing resources. In data center main processing element is virtual machine (VM), which is an instance of computing and storage resources, handles computational processes. Hence, it is important to reduce energy consumed by VM. As the workload distribution is varying in data Center as per the need, the number of VMs configured in the host are uneven, but host consumes maximum energy every time, irrespective of the workload. This leads to wastage of computational resources. This paper is intended to analyze such issues and specifically prove an algorithm which, significantly reduces energy consumption in data center, while ensuring SLA, when VM is in migration from one host to another in the data center.