Data Center Operators Research Articles

Deploying Nagios for Service Monitoring and Reporting

NAGIOS is Server Monitoring tool as an important criteria coming to a data center operation and service .Detects all type of network errors or server issues and allowed to monitor the server performance issues by using Nagios. This can be manually and physically monitoring the location of the server and mapping the network interface and network bridge server will be monitor. Count every 10 minutes once the application will be running properly or else store application related bugs in data center. Large organizations always require fast and efficient network monitoring system which reports to the network administrator as soon as a network problem arises. This paper presents an effective and automatic network monitoring system that continuously monitor all the network switches and inform the administrator by E-MAIL or SMS when any of the network switch goes down. An efficient and automatic network monitoring is always required for large organizations like universities, companies and other business sectors where the manual network monitoring is very difficult. Since large organizations have a big network topology, the manual network monitoring causes waste of time to point out problem location.

Geospatial assessment of water footprints for hyperscale data centers in the United States

The sustainability of water usage in data centers is of growing importance considering rapid digitalization and environmental constraints on freshwater resources. However, the water footprint of data centers remains understudied, posing challenges to achieving sustainable data center operations. In this study, we assessed the water footprint of hyperscale data centers in the U.S. and identified major drivers of water use in data centers. Our findings offer valuable insights for designing water-efficient data centers, implementing sustainable practices, and formulating effective policies and technology incentives.

A liquid desiccant-assisted free-cooling system for energy-efficient data centers in hot and humid climates

Air conditioning and cooling are essential to ensure the reliable operation of data centers. Several studies have been conducted regarding the use of free cooling for energy-efficient cooling. However, free cooling faces challenges when used in hot and humid environments, which can lead to a decline in cooling tower performance. To address this issue, we propose a cooling system that utilizes a liquid desiccant to lower the humidity and improve the performance of the cooling tower, thereby reducing the energy consumption of the refrigeration system. Simulations were conducted in areas where the data centers were located, considering hot and humid climates. According to the simulation results, it was possible to achieve an average reduction of 35.12 % in cooling energy consumption by improving the performance of the cooling tower.

Toward Data Center Digital Twins via Knowledge-based Model Calibration and Reduction

Computational fluid dynamics (CFD) models have been widely used for prototyping data centers. Evolving them into high-fidelity and real-time digital twins is desirable for the online operations of data centers. However, CFD models often have unsatisfactory accuracy and high computation overhead. Manually calibrating the CFD model parameters is tedious and labor-intensive. Existing automatic calibration approaches apply heuristics to search the model configurations. However, each search step requires a long-lasting process of repeatedly solving the CFD model, rendering them impractical, especially for complex CFD models. This article presents Kalibre , a knowledge-based neural surrogate approach that calibrates a CFD model by iterating four steps of (i) training a neural surrogate model, (ii) finding the optimal parameters through neural surrogate retraining, (iii) configuring the found parameters back to the CFD model, and (iv) validating the CFD model using sensor-measured data. Thus, the parameter search is offloaded to the lightweight neural surrogate. To speed up Kalibre’s convergence, we incorporate prior knowledge in training data initialization and surrogate architecture design. With about ten hours of computation on a 64-core processor, Kalibre achieves mean absolute errors (MAEs) of 0.57°C and 0.88°C in calibrating the CFD models of two production data halls hosting thousands of servers. To accelerate CFD-based simulation, we further propose Kalibreduce that incorporates the energy balance principle to reduce the order of the calibrated CFD model. Evaluation shows the model reduction only introduces 0.1°C to 0.27°C extra errors while accelerating the CFD-based simulations by thousand times.

GEECO: Green Data Centers for Energy Optimization and Carbon Footprint Reduction

Cloud computing has revolutionized data storage, processing, and access in modern data center operations. Conventional data centers use enormous amounts of energy for server operation, power supply, and cooling. The processors produce heat while processing the data and therefore increase the center’s carbon footprint, and the rising energy usage and carbon emissions caused by data centers pose serious environmental challenges. Under these circumstances, energy-efficient green data centers are being used as a phenomenal source of sustainable modernization. This study proposes the implementation of the Green Energy Efficiency and Carbon Optimization (GEECO) model for enhancing energy usage. Within the data center, the GEECO model dynamically adjusts workload distribution and task assignment to balance performance and manage service-level reconciliation. The ability to identify possibilities for energy optimization and carbon emission reduction is possible through real-time monitoring of energy usage and workload demand. The results revealed a considerable increase in energy efficiency, with significant decreases in energy usage and related costs. The GEECO model provides a significant improvement in energy consumption and carbon emission reduction for the different introduced scenarios. This model’s introduction to practical application would be made possible by these improvements in the quantitative results. The approach of this study also has a positive impact on the environment by reducing carbon emissions. The resilience and practicality of the solution are also analyzed, highlighting the probability of widespread adoption and its associated improvements in the advancement of sustainable cloud computing.

Reconstruction and fast prediction of 3D heat and mass transfer based on a variational autoencoder

Reconstruction and fast prediction of heat and mass transfer are important for the improvement of data center operations and energy savings. In this study, an artificial neural network (ANN) and variational autoencoder (VAE) composite model is proposed for the reconstruction and prediction of 3D flowfields with high accuracy and efficiency. The VAE model is trained to extract features of the problem and to realize 3D physical field reconstruction. The ANN is employed to achieve the constructability of the extracted features. A dataset of steady temperature/velocity fields is acquired by computational fluid dynamics and heat transfer (CFD/HT) and fed to train the deep learning model. The proposed ANN-VAE model is experimentally proven to achieve promising field prediction accuracy with a significantly reduced computational cost. CFD/HT method is often incapable of occasions that require instant knowledge of heat and mass transfer. The proposed ANN-VAE method convert the time-consuming simulations into the training stage. Driven by large amount of simulation data, the trained ANN-VAE model is capable of field prediction. Compared to the CFD/HT method, the ANN-VAE method speeds up the physical field prediction by approximately 380,000 times, with mean accuracies of 97.3% for temperature field prediction and 97.9% for velocity field prediction, making it feasible for real-time prediction of heat and mass transfer.

BIM and Computational Fluid Dynamics Analysis for Thermal Management Improvement in Data Centres

One of the most energy-intensive facilities requiring a comprehensive and well-optimised cooling system is the data centre. Air containment across the data centre is a key thermal management and energy-saving strategy that enhances the performance of data centres. The majority of modern energy-efficient data centres use some type of air containment. The primary advantage of aisle separation and containment is the decrease in the air temperature at the server inlet by reducing the mixing of hot air with cold air. In order to ascertain the volume of literature relating to corridor insulation, we conducted a literature review. Currently, there have been numerous articles regarding the application of computational fluid dynamics (CFD) analysis, however, publications delineating the integration of building information modelling (BIM) principles for corridor separation are still limited. Research specifically targeting data centre corridor insulation is somewhat limited. As a result of this analysis, the most common methods used to isolate hot or cold aisles within a data centre were identified. To determine the most effective type of corridor insulation, the BIM family was created in Autodesk Revit. The model includes 15 telecom cabinets containing information technology (IT) equipment, eight inter-row air conditioners, and one UPS. The model was used for the CFD analysis of the air temperature in different zones of the room. Visualisation of the results using gradient temperature distributions at different levels provides a complete picture of the microclimate formation in the room and allowed the advantage of the hot aisle isolation scheme to be demonstrated.

Balanced comparative assessment of thermal performance and energy efficiency for three cooling solutions in data centers

As the demand for data centers rises, efficient cooling systems become increasingly vital to ensure uninterrupted operations and adaptability to IT service changes. The research focuses on quantitatively comparing the thermal performance and energy efficiency of cooling systems capable of handling high-density IT power. It aims to provide objective criteria for selecting suitable cooling solutions, including an assessment of three 150 kW-class IT and facility modules, a novel contribution not extensively explored in previous studies. The study evaluates room-based, row-based, and rack-based cooling options based on the configured ITE power density, ensuring that all three solutions meet ASHRAE's recommended IT operating environment through CFD analysis. The research reveals energy efficiency improvements, with PUEcooling decreasing from 1.33 for room-based cooling to 1.28 for rack-based cooling. These improvements are attributed to reduced fan power by CRAC/H type and decreased primary plant energy consumption by increasing COP through the supply chilled water temperature. The findings offer valuable insights for designing cooling solutions tailored to ITE power density and considering factors such as space requirements, thermal performance, energy efficiency, and cost for new hyperscale data centers. Overall, this research constitutes a significant academic contribution in the field of data center cooling solutions.

Computational Engineering based approach on Artificial Intelligence and Machine learning-Driven Robust Data Centre for Safe Management

This research explores the integration of Artificial Intelligence (AI), specifically the Recurrent Neural Network (RNN) model, into the optimization of data center cooling systems through Computational Engineering. Utilizing Computational Fluid Dynamics (CFD) simulations as a foundational data source, the study aimed to enhance operational efficiency and sustainability in data centers through predictive modeling. The findings revealed that the RNN model, trained on CFD datasets, proficiently forecasted key data center conditions, including temperature variations and airflow dynamics. This AI-driven approach demonstrated marked advantages over traditional methods, significantly minimizing energy wastage commonly incurred through overcooling. Additionally, the proactive nature of the model allowed for the timely identification and mitigation of potential equipment challenges or heat hotspots, ensuring uninterrupted operations and equipment longevity. While the research showcased the transformative potential of merging AI with data center operations, it also indicated areas for further refinement, including the model's adaptability to diverse real-world scenarios and its management of long-term dependencies. In conclusion, the study illuminates a promising avenue for enhancing data center operations, highlighting the significant benefits of an AI-driven approach in achieving efficiency, cost reduction, and environmental sustainability.

Something Old, Something New: Extending the Life of CPUs in Datacenters

Datacenter operators are increasingly powering their operations with low-carbon energy sources such as wind and solar. As a result, the overall carbon footprint of datacenters increasingly comes from the manufacturing phase of server lifecycles. The most direct way to reduce the environmental impact of datacenters is to amortize the manufacturing emissions of hardware over longer timespans by keeping hardware in production beyond today's relatively short refresh cycles. In this paper we analyze the feasibility of server lifespan extension in datacenters using a series of microbenchmarks. We find that while newer processors outperform older ones, the difference is workload dependent, with some workloads showing promise for older CPUs. We also analyze the potential of incorporating overclocking to further extend server lifespans.

Dynamic thermal environment management technologies for data center: A review

The energy demand of the data center (DC) industry has accounted for 2% of the total global energy consumption, and its operating power consumption has reached 50 times that of the commercial buildings. Therefore, improving the thermal environment performance to reduce the energy cost is major trend to realize the energy savings of DC. It has become a positive measure to integrate smart technologies to improve the operating efficiency of HVAC systems, especially due to the development of informatization and intelligence. This study provides detailed overview of the dynamic thermal environment management technologies of the DCs. The requirements of thermal environment in DCs were reviewed by different levels, which are the basis of the design and operation. Accordingly, various dynamic prediction technologies were presented concerning the theoretical mechanics, the computation efficiency and the applicable circumstances. The performance of the PID, the model predictive control and the reinforcement learning in the dynamic thermal environment control of DC were analyzed. Moreover, the evaluation systems based on cooling capacity, exergy analysis and entransy analysis were introduced respectively to measure the performance of the thermal environment. By investigating the knowledge related to the “requirement, prediction, control, and evaluation” of the DC dynamic thermal environment, this study aims to provide reference and guidance for energy conservation, airflow organization and smart operation of DCs.

Congestion control analysis of optical packet switch for optical data center applications

Abstract Optical data centers serve as the backbone of modern networking, facilitating seamless connectivity for users across the globe. The connection between users and the optical data centers is established through various network topologies, which play a critical role in determining the traffic characteristics. The design and implementation of these network topologies, along with the assortment of applications hosted on optical data centers, significantly influence the flow of data within the network. The advent of cloud computing has further revolutionized optical data center operations, leading to the coexistence of a wide range of applications on different optical data center switches. As a result, the traffic characteristics observed on each optical data center switch vary significantly. This diversity in traffic patterns necessitates a comprehensive understanding of how data arrival is managed and handled by the networking infrastructure. In this paper, we explore the concept of a random traffic model for data arrival on Top of Rack (ToR) switches, which represent a crucial component of optical data center networking. In the modeling, small world model is considered. The effect of buffering and packet priorities is observed on traffic shaping. Finally, to evaluate the effectiveness of the traffic shaping techniques, we measure the packet loss performance of ToR switches and found to be as low as 10−4 even at the higher loads. Blocking performance provides valuable insights into how effectively the optical data center network manages incoming data and avoids congestion or bottlenecks.

Power data integrity verification method based on chameleon authentication tree algorithm and missing tendency value

Abstract The power system operation and control data are from a wide range of sources. The relevant data acquisition equipment is disturbed by the complex electromagnetic environment on the power system operation and control lines, resulting in data errors and affecting the application and analysis of data. Therefore, a power data integrity verification method based on chameleon authentication tree algorithm and missing trend value is proposed. Get 2D data from different sensors and place it in the space environment. After data conversion, convert heterogeneous data into the same structure, expand the scope of power data acquisition, and conduct power system operation and control node layout and integrity data acquisition; The chameleon authentication tree algorithm is used to deal with the heterogeneous information of the power data, and the true value of the data is determined in the heterogeneous conflict of the power data at the same site; Query the integrity data based on the power system operation and control positioning node, creatively calculate the missing trend value of power data, evaluate the importance of data integrity, obtain the priority of power data integrity verification, and complete the integrity verification of power data. The experimental results show that the optimal clustering number is 9.05, the distribution coefficient is 16.30, the absolute error of validity analysis is 2.80, all test indicators are close to the preset standard, and the trend of the validation curve is close to the trend of the set demand covariance curve. Ensuring the integrity of power data and determining the important indicators of power lines are more conducive to the safe and stable operation of the power data center.

Energy performance evaluation in a data center with water-side free cooling

In recent years, water-side free cooling has been broadly applied in data centers to lessen the high cost of energy consumption. The energy-saving opportunities of free cooling have been recognized by numerous studies across the world. In this paper, the field test and energy performance evaluation were performed in an operational data center with water-side free cooling, located in Chongqing, China. Existing problems were found during tests, including the insufficient cooling capacity of towers in wintertime and the inability to activate partial-free cooling. The results of the tests show that the reduction in Power Usage Effectiveness (PUE) is much lower than the expected. To understand the marked discrepancies, the performance of the cooling tower was discussed in terms of the approach temperature and actual cooling capacity. The maximum cooling capacity in winter is only about 50% of the rated. With the consideration of the cooling tower configuration performance in different climate conditions, the conservative energy effects were calculated in 34 provincial cities of China. To prolong the hours available, optimized operation schemes of cooling towers are presented. The available hours for free cooling could be increased from 730h to 1300h when operating at 67% of rated flow rate, resulting in a decrease in the energy saving of chillers between 8.44% and 21.05%.

ABLA: Application-Based Load-Balanced Approach for Adaptive Mapping of Datacenter Networks

Cloud-based services are growing more rapidly than ever, and so does the management challenge on their providers’ side. Cloud-based datacenter networks are built with nodes of huge processing power, connected by high bandwidth capacities to carry their interior traffic requirements. However, such cloud networks still have limits that are imposed not necessarily by their physical components, but by the schemes of resource management being deployed. Traditionally, for an institute to provide services, it needs to have its own datacenter facility that interconnects its servers through a topology that matches its desired administrative policies and scaling objectives. With the theme of cloud-based IaaS, such datacenter topologies can be created virtually over the cloud. Nowadays, a significant part of those institutes who provide us with our daily services have their infrastructures hosted over cloud ones. Therefore, resources of such cloud networks need to be efficiently utilized, in order to keep their performance and hosting prices competitive. A typical datacenter network mainly consists of server nodes and network links. Besides the resources of the server nodes, the network bandwidth resources are considered a crucial key determinant for the whole datacenter performance. Indeed, a server without sufficient bandwidth capacities is almost useless. Proposals in the literature present schemes for resource utilization on either side of the problem at a time: the nodes or the links. Working in isolation can never deliver efficient mapping solutions. ABLA is an Application-Based, and Load balancing Approach for adaptive mapping proposal. ABLA’s methodology tackles both sides of the datacenter, the nodes and links. It starts by (1) breaking down the node’s resource requirement for the requested applications to be hosted over the virtual server machines besides (2) reading the topological connectivity and bandwidth requirements for each virtual node to all other nodes in the virtual datacenter topology. Compared to other models in the literature, the simulation results show that our proposed ABLA model provides for complete mapping services via load-balanced hosting networks. This allows for competitive hosting prices, with higher performance and service satisfaction rates.

Operations of data centers with onsite renewables considering greenhouse gas emissions

In a highly technology-dependent society, processing large amounts of data has become essential, resulting in a growing number of data centers. The global data center industry accounts for a significant proportion of the world’s energy consumption. A partial or total transition of the energy sources that power the operation of data centers to cleaner energy, particularly onsite renewable technologies, will help to achieve the greenhouse gas emission reduction goals established in international agreements. However, integrating such technologies is difficult due to the intermittency and availability of renewable sources. Nevertheless, temporal and geographical flexibility for processing part of the data centers’ workload provides an opportunity to efficiently integrate these renewable energy sources. In this paper, we develop an operational scheme for a network of interconnected data centers with a significant electricity supply from onsite technologies consisting of (i) an optimal job migration and scheduling policy for data centers to match their operation and the availability of renewable energy under an emissions cap-and-trade program; (ii) an energy management system to control the data centers’ onsite renewable and grid energy resources; and (iii) a robust optimization approach to account for the intermittency of the renewables and to identify the optimal migration and scheduling policies under the worst-case scenario. A case study is carried out to assess the economic and environmental impacts. The results indicate that the installation of onsite technologies and an optimal migration policy reduce the operational costs and the greenhouse gas emissions of the networks of data centers.

FG-PFC: A Fine-Grained PFC Mechanism for Lossless RDMA

Remote Direct Memory Access (RDMA) is widely deployed in data centers to improve the performance, efficiency, and reliability of data centers. Priority-based Flow Control (PFC) can ensure no packet loss as a necessary approach to achieve lossless RDMA. However, since PFC is a coarse-grained flow control mechanism based on port and priority, it can cause the head-of-line blocking problem and affect other flows, resulting in high latency. In this paper, we propose a fine-grained PFC mechanism (FG-PFC), which refines port and priority into flow granularity and further determines and pauses the flow most likely to cause congestion. The benefit is reducing the persistent high occupancy of the buffer and protecting the innocent flow. Simulations and experiments demonstrate that FG-PFC outperforms PFC in terms of flow completion time (FCT).

Progressive-fidelity computation of the genetic algorithm for energy-efficient virtual machine placement in cloud data centers

Energy efficiency is a critical issue in the management and operation of data centers, which form the backbone of cloud computing. Virtual machine placement has a significant impact on the energy efficiency of virtualized data centers. Among various methods to solve the virtual-machine placement problem, the genetic algorithm has been well accepted for its quality of solution. However, it is computationally demanding largely due to the complex form of fitness function, limiting its applications in data centers. To enhance the computational efficiency of the genetic algorithm while maintaining its quality of solution, a progressive-fidelity approach is developed in this paper for genetic-algorithm computation. It starts with a low-fidelity genetic algorithm with a simple fitness function. Then, for solution refinement, it switches to a medium-fidelity genetic algorithm with a more complicated fitness function. Finally, it progresses to the fine tuning of solution through a high-fidelity genetic algorithm with the energy consumption of data centers as fitness function. Heuristics are presented for the adaptive switching of genetic-algorithm computation from low fidelity to medium fidelity and finally to high fidelity. Experiments show that compared with the standard genetic algorithm of high fidelity, our progressive-fidelity approach of genetic algorithm computation is 50% faster for large-scale data centers while maintaining similar quality of solution in terms of the energy consumption of data centers.

Data center server workload and infrastructure control based on a joint RSM and CFD approach

The rapid expansion of data centers and cloud computing has exacerbated the issue of high energy consumption and localized overheating in servers. Consequently, the identification of effective cooling methods and resource allocation strategies for server rooms has become pivotal in enhancing overall airflow distribution and optimizing thermal performance within data centers. Time-efficient Computational Fluid Dynamics (CFD) tools offer an alternative to resource-intensive experimental measurements. However, it is difficult to monitor the information in real time and optimize the design of DC. In this study, an innovative application of Response Surface Methodology (RSM) is introduced to prediction thermal environment information and optimization performance of data center. In contrast to traditional single-factor optimization methods, this approach incorporates multiple factors, such as server workloads and air supply conditions, as optimization parameters. Furthermore, maximum server temperatures, temperature differences, and other metrics are defined as key performance indicators (KPIs). A predictive model was developed with the aim of offering comprehensive information about the thermal environment of the data center. Moreover, the study qualitatively analyzes the influence of each parameter on various indicators. Fitting equations are solved based on actual conditions to determine optimized configuration schemes in real time for the entire data center or specific scenarios. This approach effectively reduces cooling energy consumption and optimizes data center thermal management. The optimized configuration resulted in a significant reduction of approximately 30% in the maximum server temperature and approximately 20% in the temperature difference. Additionally, the optimization methodology established in this study facilitates the implementation of a real-time control mechanism for data center cooling systems, enabling energy demand and management optimization. The findings of this study offer valuable insights and generalized methods for enhancing the performance of air-cooled servers.

Entrainment of Mutually Synchronized Spatially Distributed 24 GHz Oscillators

Synchronization is one of the most challenging aspects of distributed systems in terms of their scalability. Minimal uncertainties can lead to problems or failures regarding data consistency in globally operating data centers or in distributed sensor arrays. Existing approaches to address these challenges are based on hierarchical synchronization concepts which are well understood and have reached technical maturity, but have the disadvantage of having a single point of failure. However, especially for critical infrastructure or backup more resilient solutions are required. Mutual synchronization where oscillators in a network are coupled bidirectionally without a reference have been considered. Due to the flat hierarchy such systems do not have a single point of failure. This work studies how hierarchical synchronization can be combined with architectures implementing mutual synchronization. A network of three mutually coupled 24 GHz oscillators is used to study how injecting a reference signal into one oscillator affects the dynamics. This can be quantified by analyzing in which range of frequencies the network of mutually coupled oscillators can follow the reference frequency. Measurements on a ring and chain network topology forced by an external reference oscillator shown here are in good agreement with the predictions of a nonlinear dynamical model.