Data Center Infrastructure Research Articles

Increase in Demand for Electricity Due to Rapid Increase in Data Centers to Support AI and Role of V2G in Supporting this Growth

Purpose: The rapid proliferation of artificial intelligence (AI) technologies has led to unprecedented growth in data center infrastructure, significantly increasing global electricity demand. This study examines how Vehicle-to-Grid (V2G) technology can mitigate the energy challenges associated with AI-driven data center expansion. Methodology: This paper employs a literature review of technological advancements, policy frameworks, and case studies to explore the interplay between AI-driven data center growth, electricity consumption, and the potential of V2G technology. Strategic insights are drawn to evaluate V2G’s role in energy management and grid stabilization. Findings: V2G technology provides a promising solution for peak demand management, renewable energy integration, and grid stabilization by leveraging electric vehicles as mobile energy storage units. Key findings highlight V2G's capacity to support sustainable energy practices in data centers, with examples from real-world implementations. Unique Contribution to Theory, Policy, and Practice: This paper contributes to the understanding of V2G’s transformative potential in addressing energy challenges posed by AI-driven data center expansion. It emphasizes the need for collaborative efforts in technological development, policy-making, and adoption strategies to build a resilient, sustainable infrastructure for the future.

The RedFish API and vSphere Hypervisor API: A Unified Framework for Policy-Based Server Monitoring

Integrating remote monitoring systems is crucial in the ever-changing field of data center management to enhance performance and guarantee reliability. This paper outlines a comprehensive strategy for monitoring remote servers by utilizing agents that establish connections to the RedFish API (Application Programming Interface) and vSphere hypervisor API. Our solution uses the RedFish standard to provide secure and standardized management of hardware components in diverse server environments. This improves interoperability and scalability. Simultaneously, the vSphere agent enables monitoring and hardware administration in vSphere-based virtualized environments, offering crucial insights into the state of the underlying hardware. This system, which employs two agents, simplifies the management of servers and seamlessly integrates with current data center infrastructures, enhancing efficiency. The policy-based alerting system built on top of these agents offers many capabilities based on both agents leveraging their alerting systems. This, in turn, can improve the capabilities of next-generation data centers.

Optimization and performance improvement of distributed data storage in hybrid storage systems

With the rapid development of information technology, the storage and processing of massive data has become one of the important challenges facing the current computing field. Traditional centralized storage systems have been unable to meet the needs of big data applications, while distributed storage systems have become the infrastructure of modern data centers because of their high scalability and high availability. However, in practical applications, a single distributed storage model is often difficult to balance cost-effectiveness and performance requirements. Therefore, this paper proposes a hybrid storage system model combining local storage and cloud storage resources and discusses a series of optimization strategies for this model. The focus of the research is to improve the overall performance of the system through the design of intelligent data sharding, redundancy and fault tolerance mechanisms, and the application of effective load balancing technology. The effectiveness and superiority of the proposed method are verified by testing and analyzing several typical application scenarios on the experimental platform. The experimental results show that the optimized hybrid storage system not only significantly improves the speed of data access, but also effectively reduces the storage cost, demonstrating its potential in future large-scale data management.

Bill It Right: Evaluating Public Charging Station Usage Behavior under the Presence of Different Pricing Policies

This study investigates for the first time how public charging infrastructure usage differs under the presence of diverse pricing models. About 3 million charging events from different European countries were classified according to five different pricing models (cost-free, flat-rate, time-based, energy-based, and mixed) and evaluated using various performance indicators such as connection duration; transferred energy volumes; average power; achievable revenue; and the share of charging and idle time for AC, DC, and HPC charging infrastructure. The study results show that the performance indicators differed for the classified pricing models. In addition to the quantitative comparison of the performance indicators, a Kruskal–Wallis one-way analysis of variance and a pairwise comparison using the Mann–Whitney-U test were used to show that the data distributions of the defined pricing models were statistically significantly different. The results are discussed from various perspectives on the efficient design of public charging infrastructure. The results show that time-based pricing models can improve the availability of public charging infrastructure, as the connection duration per charging event can be roughly halved compared to other pricing models. Flat-rate pricing models and AC charging infrastructure can support the temporal shift of charging events, such as shifting demand peaks, as charging events usually have several hours of idle time per charging process. By quantifying various performance indicators for different charging technologies and pricing models, the study is relevant for stakeholders involved in the development and operation of public charging infrastructure.

Smart Campus Development: Trends in Building a Digital-Based Academic Culture in Cyber Learning Environments

This research aims to formulate the design, roadmap, implementation and impact of smart campus development in higher education in building a digital-based academic culture. This research uses qualitative field research methods with a multi-site study design and flow analysis techniques. The findings of this research show that the smart campus development strategy has a special character in higher education. Smart campus development system design requires data center infrastructure, internet, and building management through Enterprise Architecture (EA) Analysis, Jason with Token (JWT), LemonLDA, and according to user interplace and user experience needs. The smart campus road map was built in a long-term plan towards smart people by relying on sustainable leadership through leadership policies. Smart campus implementation includes smart boards, multi-device gadgets, and campus domain e-mail ownership. The output of smart campus development has an impact on changing mindsets towards smart thinking and ease of serving the academic needs of both national and international students; accountability, transparency, certainty of academic processes and promotions; and compliance with the system.

Deep Learning Modified Reinforcement Learning with Virtual Machine Consolidation for Energy-Efficient Resource Allocation in Cloud Computing

Cloud computing has attracted significant attention because of the growing service demands of businesses that outsource computationally intensive tasks to the data center. Meanwhile, the infrastructure of a data center is comprised of hardware resources that consume a great deal of energy and release harmful levels of carbon dioxide. Cloud data centers demand massive amounts of electrical power as modern applications and organizations grow. To prevent resource waste and promote energy efficiency, virtual machines (VMs) must be dispersed over numerous physical machines (PMs) in a data center in the cloud. The actual allocation of VMs to PMs can involve more complex decision-making processes, such as considering the resource utilization, load balancing, performance requirements, and constraints of the system. Advanced techniques, like intelligent placement algorithms or dynamic resource allocation, may be employed to optimize resource utilization and achieve efficient VM distribution across multiple PMs. Cloud service suppliers aim to lower operational expenses by reducing energy consumption while offering clients competitive services. Minimizing large-scale data center power usage while maintaining the quality of service (QoS), especially for social media-based cloud computing systems, is crucial. Consolidating VMs has been highlighted as a promising method for improving resource efficiency and saving energy in data centers. This research provides deep learning augmented reinforcement learning (RL)-based energy efficient and QoS-aware virtual machine consolidation (VMC) approach to meet the difficulties. The proposed deep learning modified reinforcement learning-virtual machine consolidation (DLMRL-VMC) model can motivate both cloud providers and customers to distribute cloud infrastructure resources to achieve high CPU utilization and good energy efficiency as measured by power usage effectiveness (PUE) and data center infrastructure efficiency (DCiE). The suggested model, DLMRL-VMC, offers a VM placement approach based on resource usage and dynamic energy consumption to determine the best-matched host and VM selection strategy, Average Utilization Migration Time (AUMT). Based on AUMT, deep learning modified reinforcement learning (DLMRL) will choose a VM with a low average CPU utilization and a short migration time. The DLMRL-VMC Energy-efficient, Resource Allocation strategy is evaluated on the trace of the CloudSim VM to attain good PUE and CPU utilization.

Project management strategies for implementing energy-efficient cooling solutions in emerging data center markets

Project management strategies play a pivotal role in implementing energy-efficient cooling solutions in emerging data center markets. This review explores key strategies for successful implementation, focusing on the unique challenges and opportunities in these markets. Emerging data center markets are characterized by rapid growth and evolving infrastructure needs. As such, implementing energy-efficient cooling solutions requires a strategic approach that addresses local regulatory requirements, environmental conditions, and technological capabilities. Effective project management in these contexts involves several key strategies. Firstly, thorough planning is essential. This includes conducting feasibility studies, assessing site-specific requirements, and developing detailed project plans. Understanding the local context is crucial, as it can impact the selection and implementation of cooling solutions. Secondly, stakeholder engagement is critical. Engaging with local authorities, communities, and industry stakeholders can help ensure regulatory compliance and garner support for the project. Additionally, involving local experts and partners can provide valuable insights and enhance the project's success. Thirdly, implementing robust monitoring and evaluation mechanisms is essential. Monitoring energy consumption, cooling system performance, and environmental impacts allows for timely adjustments and optimizations, ensuring the long-term sustainability of the cooling solutions. Lastly, knowledge sharing and capacity building are key components of successful project management in emerging data center markets. Providing training and education on energy-efficient practices and technologies can help build local capacity and ensure the continued success of the cooling solutions. In conclusion, project management strategies for implementing energy-efficient cooling solutions in emerging data center markets require a holistic approach that considers local context, engages stakeholders, implements robust monitoring mechanisms, and promotes knowledge sharing. By adopting these strategies, data center operators can effectively address the challenges and opportunities in these markets, contributing to a more sustainable and resilient data center infrastructure.

GREEN DATA CENTERS: SUSTAINABLE PRACTICES FOR ENERGY-EFFICIENT IT INFRASTRUCTURE

The digital age has led to a surge in connectivity, innovation, and information exchange, but it has also led to escalating energy consumption by data centers. Green data centers have emerged as a transformative solution, embodying a commitment to sustainability through eco-friendly practices and cutting-edge technologies. Key principles of green data centers include energy-efficient hardware, renewable energy integration, advanced cooling systems, and resource optimization strategies. Energy-efficient hardware involves replacing outdated servers, storage systems, and network equipment with energy-efficient alternatives, such as virtualization technologies. This reduces power consumption and sets the stage for a more sustainable and technologically advanced data center infrastructure. Renewable energy integration reduces dependence on traditional power grids and fossil fuels, ensuring an eco-friendlier energy supply. Advanced cooling systems, such as liquid immersion, hot aisle containment, and free air cooling, optimize efficiency while maintaining ideal server temperatures. Resource optimization ensures that every unit of energy is utilized judiciously, contributing to the overarching goal of sustainability. The transition to green data centers presents challenges such as upfront investment costs, integration of renewable energy with fluctuating power grids, and technical complexities associated with advanced cooling systems. However, there are substantial opportunities, including reduced operational costs, improved brand image, and compliance with environmental regulations. Emerging trends in green data centers include artificial intelligence and edge computing, which enable optimization of cooling systems, prediction of peak workloads, and dynamic resource management. By prioritizing energy efficiency, embracing innovative technologies, and staying attuned to emerging trends, data centers can play a pivotal role in forging a more sustainable digital future.
 Keywords: Green Data Centers, Sustainability, Energy Efficiency, It Infrastructure, Edge Computing, Artificial Intelligence.

Resilience enhancement in open network–cloud ecosystems through disaggregation and cooperation [Invited

To accommodate the growing demand for cloud services, telecom carriers’ networks and datacenter (DC) facilities form large network–cloud ecosystems (ecosystems for short) physically supporting these services. These large-scale ecosystems are continuously evolving and must be highly resilient to support critical services. Open and disaggregated optical-networking technologies promise to enhance the interoperability across telecom carriers and DC operators, thanks to their open interfaces in both the data plane and control/management plane. In the first part of this paper, we focus on a single entity (e.g., a telecom carrier or an emerging telecom/DC partnership company) that owns both the network and DC infrastructures in the ecosystem. We introduce a solution by leveraging open and disaggregated technologies to enhance the resilience of the optical networks within a multi-vendor and multi-domain ecosystem. In the second part of this paper, we consider the case when the networks and DCs are owned by different entities. Also, in this case, cooperation among datacenter providers (DCPs) and carriers is crucial to provide failure/disaster resilience to today’s cloud services. However, such cooperation is more challenging since DCPs and carriers, being different entities, may not disclose confidential information, e.g., detailed resource availability. Hence, we introduce a solution to enhance the resilience of such multi-entity ecosystems through cooperation between DCPs and carriers without violating confidentiality.

Making In-Network Computing A Shared Infrastructure in Datacenters: Architecture, Challenges and Open Issues

Making In-Network Computing A Shared Infrastructure in Datacenters: Architecture, Challenges and Open Issues

Data-centric Infrastructure for Supporting Data Processing in the IOWN Era and Its Proof of Concept

Data-centric Infrastructure for Supporting Data Processing in the IOWN Era and Its Proof of Concept

Turning weakness into strength - A feasibility analysis and comparison of datacenter deployment in hot and cold climates

Datacenters are major components of the Information and Communication Technologies (ICT) responsible for storing, processing and transmitting enormous amounts of data every second. The significance and importance of datacenters in the world economy can be identified from studies which report that in 2023, datacenter infrastructures consumed a total of 4 % of global electricity and contributed 3–5% of global carbon emissions, whereas between the years of 2017–2021, datacenters added $2.1 trillion to the U.S. Gross Domestic Product (GDP). A big portion of energy supplied in datacenters is consumed by the required cooling systems hence companies do not favor developments in hot climate countries. However, due to environmental and climate change concerns along with the steep increase of energy production costs in recent years made the industry look for alternatives. This work includes an in-depth feasibility and comparative study of datacenter construction and operation in hot and cold European countries and addresses the environmental impact of photovoltaics integration in the electrical supply system. The study considers cost parameters (land, operating expenses, photovoltaic system, etc.), the net present cost and levelized cost of energy which are different for each European country under investigation. Furthermore, for every country under consideration, the PV generation was simulated using the PVsyst software which includes multiple meteorological databases, whereas the feasibility analysis was simulated using the HOMER Pro software which integrates components, resources and economic calculations. An elaborate analysis of the results knocks down the common belief that datacenters have lower operational and running expenses in cold climates because of lower cooling requirements. On the contrary, this study shows that hot climates with high solar radiation levels may favor the operation of datacenters by providing 45 % higher green energy and 35 % lower CO2 emissions, whereas the cooling cost is only 5 % higher. In addition, the break-even period for the photovoltaic system in Southern European countries with hot climates is 3–4 times faster.

Multiagent-Based Control for Plug-and-Play Batteries in DC Microgrids with Infrastructure Compensation

The influence of the DC infrastructure on the control of power-storage flow in micro- and smart grids has gained attention recently, particularly in dynamic vehicle-to-grid charging applications. Principal effects include the potential loss of the charge–discharge synchronization and the subsequent impact on the control stabilization, the increased degradation in batteries’ health/life, and resultant power- and energy-efficiency losses. This paper proposes and tests a candidate solution to compensate for the infrastructure effects in a DC microgrid with a varying number of heterogeneous battery storage systems in the context of a multiagent neighbor-to-neighbor control scheme. Specifically, the scheme regulates the balance of the batteries’ load-demand participation, with adaptive compensation for unknown and/or time-varying DC infrastructure influences. Simulation and hardware-in-the-loop studies in realistic conditions demonstrate the improved precision of the charge–discharge synchronization and the enhanced balance of the output voltage under 24 h excessively continuous variations in the load demand. In addition, immediate real-time compensation for the DC infrastructure influence can be attained with no need for initial estimates of key unknown parameters. The results provide both the validation and verification of the proposals under real operational conditions and expectations, including the dynamic switching of the heterogeneous batteries’ connection (plug-and-play) and the variable infrastructure influences of different dynamically switched branches. Key observed metrics include an average reduced convergence time (0.66–13.366%), enhanced output-voltage balance (2.637–3.24%), power-consumption reduction (3.569–4.93%), and power-flow-balance enhancement (2.755–6.468%), which can be achieved for the proposed scheme over a baseline for the experiments in question.

Digital Twin Technology in Data Center Simulations: Evaluating the Feasibility of a Former Mine Site

Mining activities often deem mine sites as temporary, leading to their eventual reclamation, rehabilitation, or abandonment. This study innovates by proposing the re-purposing of the disused Osarizawa mine in Akita, Japan, leveraging its consistently low tunnel temperatures to establish a data center, thereby offering a sustainable economic avenue to offset reclamation costs. We assessed the feasibility of this transformation by gathering comprehensive environmental data from the site and conducting meticulous ventilation simulations. These simulations explored various scenarios encompassing diverse ventilation configurations, data server room dimensions, thermal outputs, and the inherent cooling capabilities of the proposed humid rooms. By juxtaposing the simulation outcomes with the criteria set forth in the ASHRAE 2011 Thermal Guidelines, we pinpointed the optimal parameters that satisfy the stringent temperature and relative humidity prerequisites essential for a data center’s operation. This research underscores the potential of reimagining abandoned mine sites as strategic assets, providing economic benefits while adhering to critical data center infrastructure standards.

On the benefits of optical disaggregated datacentre infrastructures for machine learning applications

On the benefits of optical disaggregated datacentre infrastructures for machine learning applications

The uptake of digital twins in delivering infrastructure sector projects

PurposeThe anticipated strong growth of the infrastructure industry over the coming decades will require more modern, digital approaches to create data-centric infrastructure that allows infrastructure to be monitored and managed throughout its lifecycle. Digital twins (DTs) are currently at an early stage in terms of their implementation on infrastructure projects across the United Kingdom (UK). The purpose of this paper is to evaluate the current uptake of DTs in delivering infrastructure sector projects and how DTs can help contribute towards strengthening the industry.Design/methodology/approachA systematic literature review approach has been conducted with the research questions derived from the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) article screening tool. In addition to this, inclusion and exclusion criteria have been used to screen irrelevant information and help streamline research documents. Following a screening of relevant information, 36 pieces of literature were reviewed in order to identify the key drivers, barriers, enabling technologies and use cases.FindingsDTs have the potential to transform asset design, production and maintenance. However, to further advance the digital innovation in the UK infrastructure sector, further study is necessary. An emerging technology must be considered on a broader scale than just its technical aspects, particularly when it comes to DTs. With enabling technologies such as the Internet of Things (IoT), sensors and artificial intelligence (AI), the uptake of DTs appears promising. While current literature indicates that DTs offer clear benefits in the infrastructure sector, the uptake is low and hindered by both technical and non-technical challenges.Originality/valueThis paper provides a rich insight into the understanding and awareness of the DTs in delivering infrastructure sector projects and how the infrastructure sector has evolved in order to develop new ways of designing, constructing, operating and monitoring infrastructure assets. This study contributes towards informing leaders in the sector of the current uptake of DTs within the UK's infrastructure sector as well as how DTs can contribute towards strengthening the industry.

Authentication, access, and monitoring system for critical areas with the use of artificial intelligence integrated into perimeter security in a data center.

Perimeter security in data centers helps protect systems and the data they store by preventing unauthorized access and protecting critical resources from potential threats. According to the report of the information security company SonicWall, in 2021, there was a 66% increase in the number of ransomware attacks. In addition, the message from the same company indicates that the total number of cyber threats detected in 2021 increased by 24% compared to 2019. Among these attacks, the infrastructure of data centers was compromised; for this reason, organizations include elements Physical such as security cameras, movement detection systems, authentication systems, etc., as an additional measure that contributes to perimeter security. This work proposes using artificial intelligence in the perimeter security of data centers. It allows the automation and optimization of security processes, which translates into greater efficiency and reliability in the operations that prevent intrusions through authentication, permit verification, and monitoring critical areas. It is crucial to ensure that AI-based perimeter security systems are designed to protect and respect user privacy. In addition, it is essential to regularly monitor the effectiveness and integrity of these systems to ensure that they function correctly and meet security standards.

Sustainable Energy Data Centres: A Holistic Conceptual Framework for Design and Operations

Data Centres serve as the foundation for digital technologies in the energy sector, enabling advanced analytics, optimization, and automation. However, their rapid growth can exert a substantial influence on the environment due to their energy consumption, water utilization, and production of electronic waste. This research begins with an energy overview of the setup and operations of data centres, highlighting their key components and infrastructure, and emphasizing their crucial role in managing energy resources and driving the energy sector’s digital technologies. Building upon this understanding, a holistic framework is proposed to tackle energy sustainability concerns in data centres, with a focus on energy-related aspects. The framework places emphasis on three primary sustainability metrics, namely energy efficiency, water consumption, and waste management. It underscores the significance of green building design principles and energy-efficient equipment as crucial constituents of sustainable data centre infrastructure. The framework delineates optimal energy operational best practices encompassing virtualization and consolidation, effective cooling tactics, and energy management and monitoring, all aimed at reducing energy consumption and enhancing energy performance. Furthermore, the framework emphasizes the significance of incorporating energy-related sustainability metrics into decision-making procedures and adhering to regulatory standards for energy efficiency. Through adherence to this framework, data centres’ environmental impact can be mitigated and a positive contribution towards a sustainable future can be made, particularly in the realm of energy conservation and optimization.

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.

Energy and carbon-aware initial VM placement in geographically distributed cloud data centers

Energy and carbon efficiency has become one of the most important research topics in the cloud computing research area because of the growing demand for cloud data center infrastructures. In addition to the environmental impact of emitting carbon, local authorities impose a carbon tax based on the amount of emitted carbon by the data centers. Therefore, this paper introduces the energy and carbon-aware initial VM placement (ECAIVMP), one of the most efficient strategies for minimizing the total energy consumption and carbon emission of cloud data centers without sacrificing the quality of service. Our proposed algorithm considers multiple geo-distributed cloud data centers and acknowledges both IT and non-IT power usage during the virtual machine placement process. An extensive simulation in the CloudSim Plus framework is conducted to evaluate the performance of our proposed algorithm using real-world datasets, including workloads, power model benchmarks, and outside temperature. The experimental results demonstrate that our algorithm is able to reduce energy consumption by 17% and carbon emissions by 6% on average compared to other competitor algorithms.