Data Center Environment Research Articles

A Platform of Federated Learning Management for Enhanced Mobile Collaboration

Federated learning (FL) has emerged as a crucial technology in today’s data-centric environment, enabling decentralized machine learning while safeguarding user privacy. This study introduces “Federated Learning ML Operations (FedOps) Mobile”, a novel FL framework optimized for the dynamic and heterogeneous ecosystem of mobile devices. FedOps Mobile addresses the inherent challenges of FL—such as system scalability, device heterogeneity, and operational efficiency—through advanced on-device training using TensorFlow Lite and CoreML. The framework’s innovative approach includes sophisticated client selection mechanisms that assess device readiness and capabilities, ensuring equitable and efficient participation across the network. Additionally, FedOps Mobile leverages remote device control for seamless task management and continuous learning, all without compromising the user experience. The main contribution of this study is the demonstration that federated learning across heterogeneous devices, especially those using different operating systems, can be both practical and efficient using the FedOps Mobile framework. This was validated through experiments that evaluated three key areas: operational efficiency, model personalization, and resource optimization in multi-device settings. The results showed that the proposed method excels in client selection, energy consumption, and model optimization, establishing a new benchmark for federated learning in diverse and complex environments.

An energy-environment coupled simulation framework for multi-scale and multi-facet evaluation of data center

This paper focuses on analyzing the thermal environment and optimizing energy consumption in data centers, which has largely omitted from previous studies. The thermal environmental of data centers is simulated and analyzed by utilizing the established “server-rack-room” multi-scale heat transfer numerical model. Based on this, the coupling simulation model of thermal environment and energy consumption in data centers is proposed to explore the relationship between them, and the corresponding optimization strategy is put forward. The energy consumption simulated by energy-environment coupled model and non-coupled model can reach a discrepancy of over 30 %, which indicates that the thermal environment impacts the power consumption of the data center significantly. Besides, the effect of several operational parameters of air conditioning system on the thermal environment and energy consumption of data center is analyzed. Through the particle swarm optimization algorithm, the optimal system parameters, which meet the requirements of thermal environment and lead to the lowest energy consumption, are found for typical cities in different climatic regions. The recommended settings include the supply air temperature of about 24 ∼ 26 ℃, the air supply volume of 8 m3/s, and the temperature difference of about 3.5 ℃ between the supply air temperature and the supply chilled water temperature. Under the optimized parameter setting, the highest temperature of server decreases to below 71 ℃, and the energy saving rate is more than 6 %.

NeSt: A QoS differentiating end-to-end networked storage simulator

The emerging high-speed storage technologies increasingly use Nonvolatile Memory Express (NVMe) protocol to meet their high throughput and low latency needs. In a datacenter environment, applications accessing multiple such devices over the fabric (i.e. the network) tend to have Quality of Service (QoS) requirements pertaining to offered throughput and experienced latency. In this paper we describe a networked storage system simulator called NeSt that supports end-to-end (E2E) QoS differentiation across multiple classes of service. This is done by conveying the class designation end to end and using it to consistently but independently apply the differentiation in each segment of the path. We demonstrate the ability of NeSt to provide end-to-end QoS differentiation under a variety of situations. To the best of our knowledge, NeSt is the first simulator of networked storage (consisting of multiple NVMe SSDs) that supports E2E QoS differentiation.

Quantum-Secured Data Centre Interconnect in a field environment

In the evolving landscape of quantum technology, the increasing prominence of quantum computing poses a significant threat to the security of conventional public key infrastructure. Quantum key distribution (QKD), an established quantum technology at a high readiness level, emerges as a viable solution with commercial adoption potential. QKD facilitates the establishment of secure symmetric random bit strings between two geographically separated, trustworthy entities, safeguarding communications from potential eavesdropping. In particular, data centre interconnects can leverage the potential of QKD devices to ensure the secure transmission of critical and sensitive information in preserving the confidentiality, security, and integrity of their stored data. In this article, we present the successful implementation of a QKD field trial within a commercial data centre environment that utilises the existing fibre network infrastructure. The achieved average secret key rate of 2.392 kbps and an average quantum bit error rate of less than 2% demonstrate the commercial feasibility of QKD in real-world scenarios. As a use case study, we demonstrate the secure transfer of files between two data centres through the Quantum-Secured Virtual Private Network, utilising secret keys generated by the QKD devices. %This demonstration marks a significant milestone in the deployment of QKD across Singapore, establishing a foundation for widespread adoption and enhancing the infrastructure for practical, quantum-safe communication in commercial environments.

In-line rate encrypted links using pre-shared post-quantum keys and DPUs

The development of quantum computers poses a risk to the cryptographic algorithms utilized by current public key infrastructure (PKI). High performance computing (HPC), inter- and intra-datacenter communications, and governmental communications are particularly vulnerable to security attacks by quantum computers. In response, there are on-going efforts on implementing post-quantum cryptography (PQC) despite the considerable overhead involved in processing PQC encrypted data packets in comparison with their classical counterparts. This work aims to help with the transition to quantum resistant cryptographic schemes by means of providing a thorough experimental performance comparison of the four algorithms selected by the National Institute of Standards and Technology (NIST) for standardization when implemented in a data processing unit (DPU). The experiments are conducted at line-rate, demonstrating for the first time operation at almost 100 Gbit/s and hence suitability for high-capacity data center environments.

An Alternative Reinforcement Learning (ARL) control strategy for data center air-cooled HVAC systems

Energy efficiency of data center is of great concern globally due to their large amount of energy consumption and the foreseeable growth in the demand of digital services in the future. Advanced control strategies are needed to reduce energy consumption. However, the optimization of data center HVAC control is a challenge task due to the complexity of the thermal dynamic models of buildings and uncertainties associated with both server loads and outdoor temperature. In this paper, we propose a new control strategy called Alternately-Reinforcement Learning-control(ARL), which realizes the alternating control of RL and proportional, integral and derivative (PID) for optimizing the control of air-cooled HVAC systems in data centers. The control object of the ARL is the speed set of the compressor and the condensing fan, and the control goal is to minimize energy consumption while maintaining temperature stability. The applied RL algorithm is Deep deterministic policy gradient (DDPG) and Proximal policy optimization (PPO). We pre-train the ARL strategy in offline environment firstly and deploy it in real data center environment for online testing. The test results show that the ARL has significant advantages in maintaining temperature stability while reducing energy consumption, and the PPO algorithm performs better than the DDPG algorithm. Compared with the PID algorithm, the PPO algorithm can save energy by 5.27%, and the temperature control effect can be improved by 3.27%,which indicates the feasibility of the ARL for implementation in real data centers.

Role of Data Analytics in Business Decision Making

Data analytics plays a pivotal role in modern business decision-making by enabling organizations to convert raw data into valuable insights. Through techniques such as data mining, predictive analytics, and machine learning, businesses can identify patterns, forecast trends, and make evidence-based decisions. This research explores the impact of data analytics on enhancing operational efficiency, improving customer engagement, and gaining competitive advantages. The study also highlights the importance of data-driven strategies in reducing risks and optimizing resources. Ultimately, it underscores how data analytics empowers businesses to make informed decisions in an increasingly data-centric environment.

An experimental study on improving the thermal environment of data centers by optimizing fan wall arrangement

For a fan-wall data center, a large number of fans can be installed on one side of the server room to facilitate the introduction of outdoor cold air into the server room for effective heat dissipation, thereby accomplishing the objective of energy conservation. However, fewer researches are currently available on optimizing the fan array layout of fan-wall data centers to improve the thermal environment. This study aims to explore the impact of fan array layout parameters, including the area, distance, and height of the fan array, on the cooling efficiency of server rooms through both single-factor experiments and orthogonal experiments. The results indicate that: (1) In a fan-wall cooling system, racks closest to the fan wall have high temperature fluctuations and less orderly airflow organization. (2) The optimization of the fan array layout in a data center is primarily influenced by the height, area and distance of the fan array. (3) For a DC, the fan array layout, with an area of 4320 cm2, a distance of 81 cm, and a height of 18 cm, can achieve optimal conditions and effectively enhances the overall thermal performance of the rack. By optimizing the layout of the fan array, a more efficient combination of parameters for air distribution can be achieved, providing a reference for the arrangement and layout of fan arrays in actual engineering applications.

Strengthening Security: Implementation of Multi-Factor Authentication (MFA) in Data Center Environment

Strengthening Security: Implementation of Multi-Factor Authentication (MFA) in Data Center Environment

Model-based control strategy with linear parameter-varying state-space model for rack-based cooling data centers

Cooling system energy consumption occupies approximately 40% of the total energy consumption in data centers, and efficient control is crucial for improving cooling efficiency and reducing the operational costs of data centers. This paper introduced a novel economic model predictive control (EMPC) strategy based on a linear parameter-varying state-space model. The primary objective was to maintain the thermal environment of data centers while minimizing the energy consumption of the cooling system. Therein, a linear parameter-varying state-space model was developed based on the parameter identification method to precisely predict the temperature field of the rack-based cooling data center in real-time. The energy management and thermal regulation performance of the EMPC and the proportion integration differentiation (PID) controller were comparatively analyzed through simulation experiments. Moreover, the impacts of the server workload level and optimization time domain on the performance of EMPC were comprehensively investigated. The results show that, the proposed EMPC was able to minimize the energy consumption by optimizing the supplied cold air temperature and airflow rate simultaneously. In comparison with the PID controller, EMPC not only achieves a 9.7% energy saving by preventing server over-cooling but also diminishes server temperature fluctuations, promoting the safe and stable operation of the server. The research shows that the EMPC reveals excellent performance along with all server workload levels. In addition, the length of the prediction horizon has a notable impact and three minutes is suggested for the rack-based cooling system.

Technical Challenges and Solutions to TCP in Data Center

Transmission Control Protocol plays a crucial role in data center networking, but it also presents several technical challenges. This article examines the various technical challenges faced in implementing TCP in data center environments and presents a comprehensive analysis of the solutions available to address these challenges. The paper discusses issues such as performance bottlenecks, congestion control, and scalability, and provides insights into the advancements in TCP technologies that have been developed to overcome these hurdles. Additionally, the article explores the impact of emerging trends such as cloud computing, big data, and virtualization on TCP in data centers, shedding light on the evolving landscape of networking protocols in modern data center environments.

Investigation on performance enhancement of micro-channel separated heat pipe in data center: A coupled heat-mass-flow characterization approach

As data center continues to expand in scale and complexity, the demand for efficient heat dissipation solutions becomes increasingly critical. The utilization of micro-channel separated heat pipe is prevalent in data center cooling due to their low energy consumption and efficient cooling. This study aims to investigate the effects of different refrigerant flow rates, different inclination angles and different inlet vapor phase fractions on the heat transfer and flow characteristics of micro-channel separated heat pipe evaporator. Firstly, a simulation model of the heat pipe evaporator was established considering the heat-mass-flow coupling characteristics, and the fins were modeled and simplified without neglecting the enhanced heat transfer. Then, the experimental data are used to verify the established simulation model, and the error is within the acceptable range. Finally, scheme design and performance optimization research are conducted. Research on the inlet flow rate of R134a shows that when the inlet flow rate is 0.005 m/s, the outlet is superheated vapor. The presence of an inclination angle (0°-20°) in the evaporator not only significantly reduces the pressure difference between the outlet and the inlet (more than 30 %), but also improves the heat transfer coefficient and the refrigerant velocity magnitude. In addition, increasing the inlet vapor volume fraction promotes vapor bubble nucleation and reduces the pressure drop, but leads to the presence of outlet overheating. The findings of this study contribute valuable insights for advancing thermal management strategies in data center environments.

Optimizing Data Centre Energy Efficiency with Dynamic Resource Allocation and Intelligent Cooling Management through Machine Learning

Our research aims to improve energy efficiency in data centres by combining cloud computing infrastructure with machine learning techniques. We propose that dynamic resource assignment, combined with intelligent optimisation of cooling systems, can reduce power waste and operational costs. Real-time sensor data from various data centre components such as servers, cooling systems, and power distribution units is collected and fed into machine learning models for analysis. In this way, we can create power arrangements that are tailored to the resources and needs of various applications. The experimental results show that energy consumption has been reduced by an average of 30% compared to traditional methods. Furthermore, our machine learning models are quite accurate in predicting cooling system performance. For example, an Artificial Neural Network (ANN) has an accuracy rate of 98.78%. This result demonstrates the efficacy of our approach in promoting energy efficiency and operational performance in data centres: it not only provides a scalable, cost-effective solution to industry energy efficiency challenges, but it also improves day-to-day data centre operations by reducing electrical consumption. Our approach, which is based on dynamic allocation of computational resources and real-time data analysis for optimising cooling systems, not only saves energy but also improves operational efficiency. With energy in mind, we must all work towards a more sustainable and green approach to data centre management. Future research can look into other potential optimisations, as well as issues with scalability and application in real-world data centre environments.

Data analytics in energy corporations: Conceptual framework for strategic business outcomes

Data analytics has emerged as a pivotal tool for energy corporations to navigate the complexities of the modern market landscape, optimize operations, and drive strategic decision-making. This abstract presents a conceptual framework delineating the role of data analytics in fostering strategic business outcomes within energy corporations. At its core, the framework emphasizes the integration of advanced data analytics methodologies with the unique operational dynamics of the energy sector. It begins by delineating the data sources available to energy corporations, ranging from sensor data in oil and gas exploration to customer consumption patterns in utilities. These diverse data streams form the foundation upon which analytics-driven insights are built. Central to the framework is the notion of actionable intelligence derived from data analytics. By harnessing advanced analytics techniques such as machine learning, predictive modeling, and optimization algorithms, energy corporations can extract meaningful insights from vast and disparate datasets. These insights facilitate informed decision-making across various business functions, including asset management, supply chain optimization, demand forecasting, and risk mitigation. Furthermore, the framework underscores the importance of leveraging real-time data analytics capabilities to enhance operational agility and responsiveness. In a rapidly evolving energy landscape characterized by fluctuating market dynamics and regulatory changes, the ability to extract actionable insights in real-time confers a competitive advantage. Moreover, the framework advocates for a holistic approach to data analytics integration, encompassing not only technological infrastructure but also organizational culture and capabilities. Effective implementation of data analytics initiatives necessitates alignment with strategic objectives, executive sponsorship, and the cultivation of a data-driven mindset throughout the organization. Ultimately, the conceptual framework presented herein serves as a roadmap for energy corporations seeking to harness the transformative potential of data analytics to drive strategic business outcomes. By embracing data-driven decision-making, these organizations can enhance operational efficiency, optimize resource allocation, mitigate risks, and capitalize on emerging market opportunities in an increasingly data-centric environment.

Cost-aware Service Placement and Scheduling in the Edge-Cloud Continuum

The edge to data center computing continuum is the aggregation of computing resources located anywhere between the network edge (e.g., close to 5G antennas), and servers in traditional data centers. Kubernetes is the de facto standard for the orchestration of services in data center environments, where it is very efficient. It, however, fails to give the same performance when including edge resources. At the edge, resources are more limited, and networking conditions are changing over time. In this article, we present a methodology that lowers the costs of running applications in the edge-to-cloud computing continuum. This methodology can adapt to changing environments, e.g., moving end-users. We are also monitoring some Key Performance Indicators of the applications to ensure that cost optimizations do not negatively impact their Quality of Service. In addition, to ensure that performances are optimal even when users are moving, we introduce a background process that periodically checks if a better location is available for the service and, if so, moves the service. To demonstrate the performance of our scheduling approach, we evaluate it using a vehicle cooperative perception use case, a representative 5G application. With this use case, we can demonstrate that our scheduling approach can robustly lower the cost in different scenarios, while other approaches that are already available fail in either being adaptive to changing environments or will have poor cost-effectiveness in some scenarios.

Impact of a novel emergency cooling system on data center environment under long-term power failure

Nuclear power plant data centers (NPPDCs) are in charge of regulating nuclear reactors. For nuclear safety, the cooling system should provide a stable operating environment for the NPPDC. A novel emergency cooling system with ultralow-temperature heat exchangers could enhance interior radiant cooling. Liquid nitrogen (LN2) flows into the indoor finned tube heat exchangers (FTHEs) under the pressure of the storage tank. In this paper, the impacts of the FTHE layout and connection and the LN2 flow rate on server cooling and interior temperature distribution were explored through experiments. The results indicated that the position and quantity of the LN2 inflow section had a major impact on the cooling effect of the cabinet. Changing the LN2 flow rate can adjust the cooling. A massive flow of LN2 was suitable for rapid emergency cooling while degrading the temperature stratification. In parallel, the FHTEs provided the best cooling effect and temperature uniformity. The Parallel 1 scheme cooled the interior of the cabinet 20% faster than the Parallel 2 scheme, and it had 11.3% less temperature stratification near the cabinet (Line 2).

Application of SDN-IP hybrid network multicast architecture in Commercial Aerospace Data Center

Abstract The increasing amount of massive data generated by commercial spacecraft in orbit puts forward higher and higher requirements for the stability, reliability, and computing power of computer systems for commercial aerospace data center. Data center computer systems are gradually transforming from X86 architecture and IP network model to a platform model with cloud computing and software-defined network (SDN) technology. This article proposes a new network architecture based on a unicast/multicast protocol for data interaction between the SDN and IP network. There are three main contributions of this article. The first is that the architecture proposed in this article aims to reduce end-to-end transmission latency and packet loss. The second is to improve the flexibility of system configuration and precise control when the SND controller state changes. The third is to verify the feasibility of deploying network architecture in a real data center environment.

Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm

Virtual network functions (VNFs) have gradually replaced the implementation of traditional network functions. Through efficient placement, the VNF placement technology strives to operate VNFs consistently to the greatest extent possible within restricted resources. Thus, VNF mapping and scheduling tasks can be framed as an optimization problem. Existing research efforts focus only on optimizing the VNFs scheduling or mapping. Besides, the existing methods focus only on one or two objectives. In this work, we proposed addressing the problem of VNFs scheduling and mapping. This work proposed framing the problem of VNFs scheduling and mapping as a multi-objective optimization problem on three objectives, namely (1) minimizing line latency of network link, (2) reducing the processing capacity of each virtual machine, and (3) reducing the processing latency of virtual machines. Then, the proposed VNF-NSGA-III algorithm, an adapted variation of the NSGA-III algorithm, was used to solve this multi-objective problem. Our proposed algorithm has been thoroughly evaluated through a series of experiments on homogeneous and heterogeneous data center environments. The proposed method was compared to several heuristic and recent meta-heuristic methods. The results reveal that the VNF-NSGA-III outperformed the comparison methods.

Unleashing the power of decentralized serverless IoT dataflow architecture for the Cloud-to-Edge Continuum: a performance comparison

The advent of new computing and communication trends that link pervasive data sources and consumers, such as Edge Computing, 5G and IIoT, has led to the development of the Cloud-to-Edge Continuum in order to take advantage of the resources available in massive IoT scenarios and to conduct data analysis to leverage intelligence at all levels. This paper outlines the challenging requirements of this novel IoT context and presents an innovative IoT framework to develop dataflow applications for data-centric environments. The proposed design takes advantage of decentralized Pub/Sub communication and serverless nanoservice architecture, using novel technologies such as Zenoh and WebAssembly, respectively, to implement lightweight services along the Cloud-to-Edge infrastructure. We also describe some use cases to illustrate the benefits and concerns of the coming IoT generation, giving a communication performance comparison of Zenoh over brokered MQTT strategies.Graphical abstract

Information Security Evaluation of Data Centre Architecture Using COBIT 5

Pusat Teknologi Informasi dan Pangkalan Data (Pustipanda) UIN Jakarta is an institution in charge of managing all information systems and data management for UIN Jakarta. However, security issues are still one of the problems faced by Pustipanda today, such as data leaks, and websites that are often problematic. This research aims to assess the level of information security at the UIN Jakarta Pustipanda data centre using the COBIT 5 framework. Information security is very important in supporting organizational operations, especially facing cyber threats in the data centre environment. The research approach included document analysis, observation, and interviews with stakeholders at Pustipanda UIN Jakarta. Identification of information security weaknesses, assessment of compliance with security standards, and design of appropriate solutions are the subject of the research. It is hoped that the results will provide a comprehensive picture of information security in the data centre as well as concrete recommendations for improvement. The results of the research include an understanding of the status of information security at Pustipanda UIN Jakarta, as well as guidelines for improving information security in accordance with COBIT 5 principles. These efforts aim to reduce risk and protect the integrity, confidentiality, and availability of data in the data centre environment