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With the development of the power distribution Internet of Things (IoT), the escalating power demand of data centers (DCs) poses a formidable challenge to the operation of distribution networks (DNs). To address this, the present study considers the operational flexibility of DCs and its impact on DNs and constructs a collaborative planning framework of DCs, renewable energy sources (RESs), and DNs. This framework employs the interval optimization method to mitigate uncertainties associated with RES output, wholesale market prices, carbon emission factors, power demand, and workloads, and the collaborative planning model is transformed into an interval optimization problem (IOP). On this basis, a novel hybrid solution method is developed to solve the IOP, where an interval order relation and interval possibility method are employed to transform the IOP into a deterministic optimization problem, and an improved integrated particle swarm optimization algorithm and gravitational search algorithm (IIPSOA-GSA) is presented to solve it. Finally, the proposed planning framework and solution algorithm are directly integrated into an actual integrated system with a distribution network and DC to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The cooling of server components has been developed over the past few years in order to meet increasing cooling requirements. The growth in performance and power density increases the cooling demand. To gain a better understanding of the evolution and possible future technology developments in the field of data center cooling, a patent analysis method was used with a focus on the server and computer room levels. After the patent extraction from the European patent database for the period 2000–2023, the search results were classified and analyzed. Most of the patents deal with air or liquid cooling. Since 2015, a technological shift from air to liquid cooling can be identified on the level of patent activities. In conclusion, from the patent analysis, it can be derived that liquid cooling will continue to gain in importance in the future and could also be combined with other approaches in the form of hybrid cooling. However, air cooling may still be relevant, even if the main cooling load is handled by liquid-based approaches. At the same time, the optimization potential for air cooling seems to have been largely exploited in comparison to liquid cooling, as can be seen from the falling number of the patent applications. [ABSTRACT FROM AUTHOR]

Data centers consume significant amounts of energy, contributing indirectly to environmental pollution through greenhouse gas emissions during electricity generation. According to the Natural Resources Defense Council, information and communication technologies and networks account for roughly 10% of global energy consumption. Reducing power consumption in Data Center Networks (DCNs) is crucial, especially given that many data center components operate at full capacity even under low traffic conditions, resulting in high costs for both service providers and consumers. Current solutions often prioritize power optimization without considering Quality of Service (QoS). Services such as video streaming and Voice over IP (VoIP) are particularly sensitive to loss or delay and require QoS to be maintained below certain thresholds. This paper introduces a novel framework called QoS-Aware Power-Optimized Path Selection (Q-PoPS) for software-defined DCNs. The objective of Q-PoPS is to minimize DCN power consumption while ensuring that an acceptable QoS is provided, meeting the requirements of DCN services. This paper describes the implementation of a prototype for the Q-PoPS framework that leverages the POX Software-Defined Networking (SDN) controller. The performance of the prototype is evaluated using the Mininet emulator. Our findings demonstrate the performance of the proposed Q-PoPS algorithm in three scenarios. Best-case: Enhancing real-time traffic protocol quality without increasing power consumption. midrange-case: Replacing bottleneck links while preserving real-time traffic quality. Worst-case: Identifying new paths that may increase power consumption but maintain real-time traffic quality. This paper underscores the need for a holistic approach to DCN management, optimizing both power consumption and QoS for critical real-time applications. We present the Q-PoPS framework as evidence that such an approach is achievable. [ABSTRACT FROM AUTHOR]

The distributed energy power system needs to provide sufficient and flexible computing power on demand to meet the increasing digitization and intelligence requirements of the smart grid. However, the current distribution of the computing power and loads in the energy system is unbalanced, with data center loads continuously increasing, while there is a large amount of idle computing power at the edge. Meanwhile, there are a large number of real-time computing tasks in the distributed energy power system, which have strict requirements on execution deadlines and require reasonable scheduling of multi-level heterogeneous computing power to meet real-time computing demands. Based on the aforementioned background and issues, this paper studies the real-time service scheduling problem in a multi-level heterogeneous computing network of distributed energy power systems. Specifically, we consider the divisibility of tasks in the model. This paper presents a hierarchical real-time task-scheduling framework specifically designed for distributed energy power systems. The framework utilizes an orchestrating agent (OA) as the execution environment for the scheduling module. Building on this, we propose a hierarchical selection algorithm for choosing the appropriate network layer for real-time tasks. Further, we develop two scheduling algorithms based on greedy strategy and genetic algorithm, respectively, to effectively schedule tasks. Experiments show that the proposed algorithms have a superior success rate in scheduling compared to other current algorithms. [ABSTRACT FROM AUTHOR]

Cloud service providers deliver computing services on demand using the Infrastructure as a Service (IaaS) model. In a cloud data center, several virtual machines (VMs) can be hosted on a single physical machine (PM) with the help of virtualization. The virtual machine placement (VMP) involves assigning VMs across various physical machines, which is a crucial process impacting energy draw and resource usage in the cloud data center. Nonetheless, finding an effective settlement is challenging owing to factors like hardware heterogeneity and the scalability of cloud data centers. This paper proposes an efficient algorithm named VMP-ER aimed at optimizing power consumption and reducing resource wastage. Our algorithm achieves this by decreasing the number of running physical machines, and it gives priority to energy-efficient servers. Additionally, it improves resource utilization across physical machines, thus minimizing wastage and ensuring balanced resource allocation. [ABSTRACT FROM AUTHOR]

The ever-increasing demand for high-speed data transmission in telecommunications and data centers has driven the development of advanced on-chip integrated electro-optic modulators. Silicon modulators, constrained by the relatively weak carrier dispersion effect, face challenges in meeting the stringent requirements of next-generation photonic integrated circuits. Consequently, there has been a growing interest in Pockels effect-based electro-optic modulators, leveraging ferroelectric materials like LiNbO3, BaTiO3, PZT, and LaTiO3. Attributed to the large first-order electro-optic coefficient, researchers have delved into developing modulators with expansive bandwidth, low power consumption, compact size, and linear response. This paper reviews the working principles, fabrication techniques, integration schemes, and recent highlights in Pockels effect-based modulators. [ABSTRACT FROM AUTHOR]

In the past twenty years, the IT industry has moved away from using physical servers for workload management to workloads consolidated via virtualization and, in the next iteration, further consolidated into containers. Later, container workloads based on Docker and Podman were orchestrated via Kubernetes or OpenShift. On the other hand, high-performance computing (HPC) environments have been lagging in this process, as much work is still needed to figure out how to apply containerization platforms for HPC. Containers have many advantages, as they tend to have less overhead while providing flexibility, modularity, and maintenance benefits. This makes them well-suited for tasks requiring a lot of computing power that are latency- or bandwidth-sensitive. But they are complex to manage, and many daily operations are based on command-line procedures that take years to master. This paper proposes a different architecture based on seamless hardware integration and a user-friendly UI (User Interface). It also offers dynamic workload placement based on real-time performance analysis and prediction and Machine Learning-based scheduling. This solves a prevalent issue in Kubernetes: the suboptimal placement of workloads without needing individual workload schedulers, as they are challenging to write and require much time to debug and test properly. It also enables us to focus on one of the key HPC issues—energy efficiency. Furthermore, the application we developed that implements this architecture helps with the Kubernetes installation process, which is fully automated, no matter which hardware platform we use—x86, ARM, and soon, RISC-V. The results we achieved using this architecture and application are very promising in two areas—the speed of workload scheduling and workload placement on a correct node. This also enables us to focus on one of the key HPC issues—energy efficiency. [ABSTRACT FROM AUTHOR]

The Industrial Metaverse paradigm can be broadly described as a virtual environment that integrates various technologies such as augmented reality and mixed reality to enhance business operations and processes. It aims to streamline workflows, reduce error rates, improve efficiency, and provide a more engaging experience for employees. The promise of the Industrial Metaverse to drive sustainability and resource efficiency is compelling. Using advanced technologies such as the Industrial Metaverse is vital in an endeavor to have a competitive edge in a rapidly evolving business environment. However, the environmental impact of the technologies underpinning the Industrial Metaverse, like data centers and network infrastructure, should not be overlooked. The ecological footprint of these technologies must be considered in the sustainability equation. Researchers have warned that, by 2025, without sustainable artificial intelligence (AI) practices, AI will consume more energy than the human workforce, significantly offsetting zero carbon gains. As the Metaverse persists in evolving and gaining momentum, it will be necessary for companies to prioritize sustainability and explore new ways to balance technological advancements with environmental stewardship. However, recent studies have conjectured that the Metaverse holds the potential to reduce carbon emissions, as digital replacements for physical goods become more prevalent and physical activities like mobility and construction are reduced. Moreover, the specific extent to which this substitution can alleviate environmental concerns remains an open issue, presenting a knowledge gap in understanding the real-world impact of digital replacements. Thus, the objective of this paper is to provide a comprehensive review of the Industrial Metaverse, as well as explore the environmental impact of the Industrial Metaverse. The integrative literature review design and methodological approach involved multiple sources from the Web of Science and databases such as the ACM library, IEEE Library, and Google Scholar, which were analyzed to provide a comprehensive understanding of the developments in the Industrial Metaverse. Firstly, by considering the Industrial Metaverse's architecture, we elucidate the Industrial Metaverse concept and the associated enabling technologies. Secondly, we performed an exploration through a discussion of the prevalent use cases and the deployment of the emerging Industrial Metaverse. Thirdly, we explored the impact of the Industrial Metaverse on the environment. Lastly, we address novel security and privacy risks, as well as upcoming research challenges, keeping in mind that the Industrial Metaverse is based on a strong data fabric. The results point to the Industrial Metaverse as having both positive and negative environmental effects in terms of energy consumption, e-waste, and pollution. Research, however, indicates that most Industrial Metaverse applications have a positive environmental impact and subsequently trend toward sustainability. Finally, for sustainability in the Industrial Metaverse, enterprises may consider utilizing renewable energy sources and cloud services. Furthermore, we examined the effects of products on the environment, as well as in the creation of a circular economy. [ABSTRACT FROM AUTHOR]

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we introduce the Modified Feeding Birds Algorithm (ModAFBA) as an innovative solution for virtual machine (VM) consolidation in cloud environments. The primary objective is to enhance resource management and operational efficiency in cloud data centers. ModAFBA incorporates adaptive position update rules and strategies specifically designed to minimize VM migrations, addressing the unique challenges of VM consolidation. The experimental findings demonstrated substantial improvements in key performance metrics. Specifically, the ModAFBA method exhibited significant enhancements in energy usage, SLA compliance, and the number of VM migrations compared to benchmark algorithms such as TOPSIS, SVMP, and PVMP methods. Notably, the ModAFBA method achieved reductions in energy usage of 49.16%, 55.76%, and 65.13% compared to the TOPSIS, SVMP, and PVMP methods, respectively. Moreover, the ModAFBA method resulted in decreases of around 83.80%, 22.65%, and 89.82% in the quantity of VM migrations in contrast to the aforementioned benchmark techniques. The results demonstrate that ModAFBA outperforms these benchmarks by significantly reducing energy consumption, operational costs, and SLA violations. These findings highlight the effectiveness of ModAFBA in optimizing VM placement and consolidation, offering a robust and scalable approach to improving the performance and sustainability of cloud data centers. [ABSTRACT FROM AUTHOR]

The feedback of vegetation on cloud cover is an important link in the global water cycle. However, the relative importance of vegetation and related factors (surface properties, heat fluxes, and environmental conditions) on cloud cover in the context of greening remains unclear. Combining the Global Land Surface Satellite (GLASS) leaf area index (LAI) product and the fifth-generation reanalysis data of the European Centre for Medium-Range Weather Forecasts (ERA5), we quantified the relative contribution of vegetation and related factors to total cloud cover (TCC) in typical regions (Eastern European Plain, Western Siberian Plain, Mongolian Plateau, and Northeastern China Plain) of Eurasia over 21 years, and investigated how vegetation moderated the contribution of the other factors. Here, we show that the relative contribution of different factors to TCC was closely related to the climate and vegetation characteristics. In energy-limited (moisture-limited) areas, temperature (relative humidity) was more likely to be the factor that strongly contributed to TCC variation. Except for sparsely vegetated ecosystems, the relative contribution of LAI to TCC was stable within a range of 8–13%. The case study also shows that vegetation significantly modulated the contribution of other factors on TCC, but the degree of the regulation varied among different ecosystems. Our results highlight the important influence of vegetation on cloud cover during greening, especially the moderating role of vegetation on the contribution of other factors. [ABSTRACT FROM AUTHOR]

Software-defined networks (SDNs) are expanding their presence beyond laboratories, campus networks, ISPs, and data centre networks, moving into various domains. Although originally designed for campus networks, SDNs face scalability challenges, especially with the use of OpenFlow. Addressing these challenges requires innovative traffic management mechanisms to efficiently handle the growing number of connected devices and the increasing volume of traffic from various types of applications. This article proposes an innovative method for link weight selection that incorporates multipath transmission and flow aggregation in the SDNs. This novel approach improves resource utilization in two key ways. First, it involves the preservation of bandwidth during congestion. Second, it minimizes internal resource usage, as illustrated by a reduction in the number of table entries in switches. Resources undergo optimization through the introduction of a novel mechanism for flow aggregation. This novel mechanism, coupled with multipath transmission, enables adaptive responses to dynamic changes in network conditions. The aggregation process leads to a reduced number of flow entries in the core switches compared to the conventional operation of OpenFlow. The proposed scenarios for link weight allocation allow for a reduction in the number of entries in the core switches by up to 99%. The application of the proposed method also results in an increase of 58% in traffic transmission. [ABSTRACT FROM AUTHOR]

The booming growth of the internet of things has brought about widespread deployment of devices and massive amounts of sensing data to be processed. Federated learning (FL)-empowered mobile edge computing, known for pushing artificial intelligence to the network edge while preserving data privacy in learning cooperation, is a promising way to unleash the potential information of the data. However, FL's multi-server collaborative operating architecture inevitably results in communication energy consumption between edge servers, which poses great challenges to servers with constrained energy budgets, especially wireless communication servers that rely on battery power. The device-to-device (D2D) communication mode developed for FL turns high-cost and long-distance server interactions into energy-efficient proximity delivery and multi-level aggregations, effectively alleviating the server energy constraints. A few studies have been devoted to D2D-enabled FL management, but most of them have neglected to investigate server computing power for FL operation, and they have all ignored the impact of dataset characteristics on model training, thus failing to fully exploit the data processing capabilities of energy-constrained edge servers. To fill this gap, in this paper we propose a D2D-assisted FL mechanism for energy-constrained edge computing, which jointly incorporates computing power allocation and dataset correlation into FL scheduling. In view of the variable impacts of computational power on model accuracy at different training stages, we design a partite graph-based FL scheme with adaptive D2D pairing and aperiodic variable local iterations of heterogeneous edge servers. Moreover, we leverage graph learning to exploit the performance gain of the dataset correlation between the edge servers in the model aggregation process, and we propose a graph-and-deep reinforcement learning-based D2D server pairing algorithm, which effectively reduces FL model error. The numerical results demonstrate that our designed FL schemes have great advantages in improving FL training accuracy under edge servers' energy constraints. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, artificial intelligence, etc., data centers have become a flexible load due to their adjustable capabilities and are able to take on power service. However, the existing communication architecture is monolithic and poorly adapted between services and communications, which poses a challenge for data centers to participate in power service. Firstly, this article constructs a multi-domain communication architecture for data center management systems that includes local and remote communication, and analyzes the key technologies supporting this architecture. Secondly, based on differentiated service types and communication requirements, an evaluation system for the adaptability between service and communication technology was constructed, and a communication mode adaptation method based on fuzzy analytic hierarchy process (FAHP)-improved CRITIC combined weighting model and grey Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was proposed to achieve the analysis of the adaptability between differentiated service and multi-domain communication technology. According to the analysis of examples, the architecture and adaptation algorithm proposed in this article provide an effective theoretical basis and solution for the selection of multi-domain communication technologies for data center management system service. [ABSTRACT FROM AUTHOR]

Geographically distributed cloud data centers (DCs) consume enormous amounts of energy to meet the ever-increasing processing and storage demands of users. The brown energy generated using fossil fuels is expensive and significantly contributes to global warming. Considering the environmental impact caused by the high carbon emissions and relatively high energy cost of brown energy, we propose the integration of renewable energy sources (RES), especially solar and wind energy, with brown energy to power cloud data centers. In our earlier study, we addressed the intermittency of renewable energy sources, where we replaced the random initialization of artificial neural network (ANN) edge weights with the harmony search algorithm (HSA)-optimized assignment of weights. This study incorporated reliably forecast solar and wind energy into the input parameters of our proposed green energy manager (GEM), for cost minimization, carbon emission minimization, and better energy management of cloud DCs, to make our current study more reliable and trustworthy. Four power sources, on-site solar energy and wind energy, off-site solar energy and wind energy, energy stored in energy storage devices, and brown energy, were considered in this study and simulations were carried out for three different cases. The simulation results showed that case 1 (all brown) was 58% more expensive and caused 71% higher carbon emissions than case 2.1 (cost minimization). Case 1 (all brown) was 39% more expensive and had 80% higher carbon emissions than case 2.2 (carbon emission minimization). The simulation results justify the necessity and importance of the GEM, and finally the results proved that our proposed GEM is less expensive and more environmentally friendly. [ABSTRACT FROM AUTHOR]

With new high-performance server technology in data centers and bunkers, optimizing search engines to process time and resource consumption efficiently is necessary. The database query system, upheld by the standard SQL language, has maintained the same functional design since the advent of PL/SQL. This situation is caused by recent research focused on computer resource management, encryption, and security rather than improving data mining based on AI tools, machine learning (ML), and artificial neural networks (ANNs). This work presents a projected methodology integrating a multilayer perceptron (MLP) with Kmeans. This methodology is compared with traditional PL/SQL tools and aims to improve the database response time while outlining future advantages for ML and Kmeans in data processing. We propose a new corollary: h k → H = S S E (C) , w h e r e k > 0 a n d ∃ X , executed on application software querying data collections with more than 306 thousand records. This study produced a comparative table between PL/SQL and MLP-Kmeans based on three hypotheses: line query, group query, and total query. The results show that line query increased to 9 ms, group query increased from 88 to 2460 ms, and total query from 13 to 279 ms. Testing one methodology against the other not only shows the incremental fatigue and time consumption that training brings to database query but also that the complexity of the use of a neural network is capable of producing more precision results than the simple use of PL/SQL instructions, and this will be more important in the future for domain-specific problems. [ABSTRACT FROM AUTHOR]

The antimicrobial resistance of Neisseria gonorrhoeae (NG) is an increasing public health concern, highlighted by the fact that gonococcus is considered as a 'high'-priority pathogen by the WHO for research and development of new therapeutic options. According to the data of the European Centre for Disease Prevention and Control (ECDC) in 2022, the rate of NG infections is the highest recorded since European surveillance of sexually transmitted infections began in 2009. We report a brief description of a patient infected with two different isolates of drug-resistant N. gonorrhoeae. N. gonorrhoeae cultures were positive from oropharyngeal and urethral swabs and isolates had different antimicrobial susceptibility. We investigated the antimicrobial susceptibility of these isolates to six antimicrobials (ceftriaxone, cefixime, azithromycin, ciprofloxacin, tetracycline, and benzylpenicillin), and minimum inhibitory concentrations (MICs; mg/L) were determined using Etest on gonococcal isolates. Oropharyngeal isolate was resistant to azithromycin while urethral was resistant to penicillin, ciprofloxacin, and tetracycline. Two different and phylogenetically distinct sequence types of NG isolates were identified. Understanding the dynamics and drivers of resistance spread can provide an improved rationale for antibiotic management, and the level of NG resistance should be monitored closely. [ABSTRACT FROM AUTHOR]

As research into data centers progresses, the importance of resonant switched-capacitor converters in power supply design becomes more evident. Practical applications reveal that the values of resonant capacitors and inductors may deviate from their nominal values due to various factors, leading to resonant frequency instability. This instability poses a challenge to power electronics technology, affecting system reliability and performance. This paper analyzes the effect of frequency deviation on system functionality, identifies the relationship between output voltage and switching frequency, and proposes a self-tracking frequency strategy to address this issue. Through experimental validation, this approach shows that it maintains synchronization between switching and resonant frequencies, reducing losses associated with frequency misalignment. Simulation and experimental results validate the converter's stable operation and its ability to achieve zero-voltage and zero-current switching. [ABSTRACT FROM AUTHOR]

With the development of port automation and artificial intelligence, coordination with multi-geographic data centers (Geo-DCs) has become a viable solution to address the issue of limited port computing resources. This study proposes a distributed energy dispatch method for the port microgrid coordinated with Geo-DCs (Geo-DCPM), aimed at reducing port carbon emissions and operational costs. Consider the single point of failure problem and high construction costs of centralized data centers. Geo-DCs are first introduced to solve the problem of insufficient computing resources in ports. An energy consumption calculation model for Geo-DCs is established, considering the data load delay constraint and the data space transfer constraint caused by specific delay-sensitive loads in the port microgrid. Then, an energy dispatch model (EDM) is constructed for the Geo-DCPM, taking into account carbon capture costs. Moreover, based on mixed-integer linear programming, a distributed algorithm is proposed to solve the EDM problem. Finally, the simulation results verify the effectiveness of the proposed method. Compared with the centralized algorithm, the packet loss rate of the distributed algorithm combined with Geo-DCs is significantly lower, reduced by about 70%. [ABSTRACT FROM AUTHOR]

Continuous discovery and update of applications or their boundaries running in cloud environments in an automatic way is a highly required function of modern data center operation solutions. Prior attempts to address this problem within various products or projects were/are applying rule-driven approaches or machine learning techniques on specific types of data–network traffic as well as property/configuration data of infrastructure objects, which all have their drawbacks in effectively identifying roles of those resources. The current proposal (ADLog) leverages log data of sources, which contain incomparably richer contextual information, and demonstrates a reliable way of discriminating application objects. Specifically, using native constructs of VMware Aria Operations for Logs in terms of event types and their distributions, we group those entities, which then can be potentially enriched with indicative tags automatically and recommended for further management tasks and policies. Our methods differentiate not only diverse kinds of applications, but also their specific deployments, thus providing hierarchical representation of the applications in time and topology. For several applications under Aria Ops management in our experimental test bed, we discover those in terms of similarity behavior of their components with a high accuracy. The validation of the proposal paves the path for an AI-driven solution in cloud management scenarios. [ABSTRACT FROM AUTHOR]

The "Yangtze River Economic Belt Development Strategy" is one of China's three major national development strategies. Enhancing the protection and quality of arable land in the Yangtze River Economic Belt (YEB) is pivotal for fostering regional growth. In this study, land use data spanning the years 1980 to 2020 in the YEB were extracted from the national land use database maintained by the Resource and Environment Data Center of the Chinese Academy of Sciences. Employing Geographic Information System (GIS) spatial analysis techniques and arable land change metrics, the study delineated the spatiotemporal characteristics of arable land alterations across the YEB for the period. Additionally, using grain output data at the prefecture level from 2011 to 2020, the paper calculated provincial grain output to analyze the impact of arable land changes over the last four decades on grain output. The findings revealed that: (1) From 1980 to 2020, the total arable land area in the YEB decreased by approximately 41,775 square kilometers, with the most significant decrease occurring in the downstream region. (2) From 1980 to 1990, the primary factor contributing to the decrease in arable land area was the expansion of water bodies, while from 1990 to 2020, the principal reason for the reduction in arable land area was the expansion of construction land. (3) From 1980 to 2020, the decrease in arable land area resulted in a net reduction of approximately 25.12 million tons in total grain output, with the largest decline observed in the downstream regions and the smallest decline in the upstream regions. (4) Consistent with the trends in arable land area reduction, the main reason for the decline in grain output from 1980 to 1990 was the expansion of water bodies encroaching upon arable land, whereas from 2000 to 2010, the primary cause of arable land reduction was the expansion of construction land areas. In conclusion, the research suggested that over the past four decades, the primary driver behind the reduction in arable land within the YEB has been the expansion of construction land areas. Particularly noteworthy was the period from 2000 to 2010, during which the impact of arable land reduction on grain output was most pronounced. This period coincided with the rapid economic development and accelerated urbanization process within the YEB. [ABSTRACT FROM AUTHOR]

In response to the global megatrends of digitalization and transportation automation, Turku University of Applied Sciences has developed a test platform to advance autonomous maritime operations. This platform includes the unmanned surface vessel eM/S Salama and a remote operations center, both of which are detailed in this article. The article highlights the importance of collecting and annotating multi-modal sensor data from the vessel. These data are vital for developing deep learning algorithms that enhance situational awareness and guide autonomous navigation. By securing relevant data from maritime environments, we aim to enhance the autonomous features of unmanned surface vessels using deep learning techniques. The annotated sensor data will be made available for further research through open access. An image dataset, which includes synthetically generated weather conditions, is published alongside this article. While existing maritime datasets predominantly rely on RGB cameras, our work underscores the need for multi-modal data to advance autonomous capabilities in maritime applications. [ABSTRACT FROM AUTHOR]

Recently, lumped Mach-Zehnder Modulators (MZMs) have received renewed attention due to their potential for low power consumption and compact size. However, the practicality of lumped MZMs with conventional lumped electrodes (C−LEs) is limited by their lower electro−optical (EO) bandwidth. The reduction in EO bandwidth results from the inherent trade−off between EO bandwidth and half−wave voltage length product ( V π L ) within the C−LE architecture. This paper proposes a thin−film lithium niobate (TFLN)−based lumped MZM with capacitively−loaded lumped electrodes (CL−LEs). The purely linear EO effect of the LN eliminates the parasitic capacitance in the doped PN junction and enhances the EO bandwidth. Furthermore, the CL−LE structure can break the limitation between EO bandwidth and V π L inherent in the C−LE design. Simulations show the proposed device achieves a high EO bandwidth of 32.4 GHz and a low V π L of 1.15 V·cm. Due to the reduced capacitance and lower V π L , the power consumption of the device is as low as 0.1 pJ/bit. Simulation results indicate that the open−eye diagrams are achieved at 64 Gb/s for 1.5 mm TFLN lumped MZM, with an ER of 2.97 dB. Consequently, the proposed device architecture substantially enhances the performance of lumped MZMs, showing promise for application in short−reach optical interconnects within data centers. [ABSTRACT FROM AUTHOR]

The application of Artificial Intelligence (AI) and Machine Learning (ML) models is increasingly leveraged to automate and optimize Data Centre (DC) operations. However, the interpretability and transparency of these complex models pose critical challenges. Hence, this paper explores the Shapley Additive exPlanations (SHAP) values model explainability method for addressing and enhancing the critical interpretability and transparency challenges of predictive maintenance models. This method computes and assigns Shapley values for each feature, then quantifies and assesses their impact on the model's output. By quantifying the contribution of each feature, SHAP values can assist DC operators in understanding the underlying reasoning behind the model's output in order to make proactive decisions. As DC operations are dynamically changing, we additionally investigate how SHAP can capture the temporal behaviors of feature importance in the dynamic DC environment over time. We validate our approach with selected predictive models using an actual dataset from a High-Performance Computing (HPC) DC sourced from the Enea CRESCO6 cluster in Italy. The experimental analyses are formalized using summary, waterfall, force, and dependency explanations. We delve into temporal feature importance analysis to capture the features' impact on model output over time. The results demonstrate that model explainability can improve model transparency and facilitate collaboration between DC operators and AI systems, which can enhance the operational efficiency and reliability of DCs by providing a quantitative assessment of each feature's impact on the model's output. [ABSTRACT FROM AUTHOR]

(1) Background: With the rapid development of cloud computing, large AI models, and other emerging technologies, the issue of heat dissipation in data centers has become increasingly prominent. This issue is often caused by inappropriate temperature distribution when using cold air to cool servers. Improving temperature distribution is key to optimizing the thermal performance of data centers. Previous solutions do not include installing adjustable underfloor deflectors under a raised floor while adjusting the aperture ratio of the floor grille and replacing the side of the floor grille located near the air-conditioning unit with a fan floor. (2) Methods: A 3D model of a data center was established, and its meshing and boundary conditions were set. The airflow inside the data center was analyzed using a CFD simulation to assess the temperature distribution resulting from two proposed solutions. (3) Results: Simulations and analyses showed that both options balanced the airflow close to and away from the conditioned side cabinets. This maximized the cooling capacity and improved temperature uniformity. The maximum temperature drop registered for the average cabinet's out temperature was 2.81 °C. And by installing an adjustable underfloor deflector under the floor grille in rows O and N and adjusting the grille opening, the airflow to the cabinet near the air-conditioned side increased by 18.1%, and the airflow away from the air-conditioned side decreased by 5.1%. Similarly, replacing the Q-row floor grille with a fan floor resulted in a 4.9% increase in airflow to the cabinet near the air-conditioning side and a 3.8% decrease in airflow to the cabinet away from the air-conditioning side. (4) Conclusions: Airflow is a crucial factor that affects cabinet temperature. And balancing airflow between the front-end and rear-end cabinets is essential to make the best use of the cooling capacity and improve temperature distribution within data-center cabinets. This can be achieved by installing a fan floor and an underfloor deflector device in front of high-temperature cabinets located near air-conditioning units. [ABSTRACT FROM AUTHOR]

The rapid growth in computing and data transmission has significant energy and environmental implications. While there is considerable interest in waste heat emission and reuse in commercial data centers, opportunities in academic data centers remain largely unexplored. In this study, real-time onsite waste heat data were collected from a typical academic data center and an analysis framework was developed to determine the quality and quantity of waste heat that can be contained for reuse. In the absence of a comprehensive computer room monitoring system, real-time thermal data were collected from the data center using two arrays of thermometers and thermo-anemometers in the server room. Additionally, a computational fluid dynamics model was used to simulate temperature distribution and identify "hot spots" in the server room. By simulating modification of the server room with a hot air containment system, the return air temperature increased from 23 to 46 °C and the annual waste heat energy increased from 377 to 2004 MWh. Our study emphasizes the importance of containing waste heat so that it can be available for reuse, and also, that reusing the waste heat has value in not releasing it to the environment. [ABSTRACT FROM AUTHOR]

DNA data storage based on synthetic oligonucleotides is a major attraction due to the possibility of storage over long periods. Nowadays, the quantity of data generated has been growing exponentially, and the storage capacity needs to keep pace with the growth caused by new technologies and globalization. Since DNA can hold a large amount of information with a high density and remains stable for hundreds of years, this technology offers a solution for current long-term data centers by reducing energy consumption and physical storage space. Currently, research institutes, technology companies, and universities are making significant efforts to meet the growing need for data storage. DNA data storage is a promising field, especially with the advancement of sequencing techniques and equipment, which now make it possible to read genomes (i.e., to retrieve the information) and process this data easily. To overcome the challenges associated with developing new technologies for DNA data storage, a message encoding and decoding exercise was conducted at a Brazilian research center. The exercise performed consisted of synthesizing oligonucleotides by the phosphoramidite route. An encoded message, using a coding scheme that adheres to DNA sequence constraints, was synthesized. After synthesis, the oligonucleotide was sequenced and decoded, and the information was fully recovered. [ABSTRACT FROM AUTHOR]

Traditional hypervisor-assisted virtualization is a leading virtualization technology in data centers, providing cost savings (CapEx and OpEx), high availability, and disaster recovery. However, its inherent overhead may hinder performance and seems not scale or be flexible enough for certain applications, such as microservices, where deploying an application using a virtual machine is a longer and resource-intensive process. Container-based virtualization has received attention, especially with Docker, as an alternative, which also facilitates continuous integration/continuous deployment (CI/CD). Meanwhile, LXD has reactivated the interest in Linux LXC containers, which provides unique operations, including live migration and full OS emulation. A careful analysis of both options is crucial for organizations to decide which best suits their needs. This study revisits key concepts about containers, exposes the advantages and limitations of each container technology, and provides an up-to-date performance comparison between both types of containers (applicational vs. system). Using extensive benchmarks and well-known workload metrics such as CPU scores, disk speed, and network throughput, we assess their performance and quantify their virtualization overhead. Our results show a clear overall trend toward meritorious performance and the maturity of both technologies (Docker and LXD), with low overhead and scalable performance. Notably, LXD shows greater stability with consistent performance variability. [ABSTRACT FROM AUTHOR]

Historical sites are incomplete relics left by human activities and are also valuable resources for human society, with most of them buried deep underground. Because the protection of original historical sites is difficult, very few sites choose this method. Many funerary objects that have been in historical sites are scattered in museums, losing their original context and complicating their utilization. Thus, using digital technology to protect and utilize historical sites and to explore their outstanding value from historical, aesthetic, and anthropological perspectives is a foremost concern. Therefore, this study aims to develop a comprehensive method for the protection and utilization of historical sites, that is, digital protection and utilization based on a digital twin. We constructed a historical site digital twin model using qualitative and vertical methods, including a physical entity, virtual entity, twin data center, digital twin service, and connection. We also established a technical framework of data acquisition and processing, digital protection, and digital utilization, forming a layered management and application of digital resources. In digital protection, information in the real world and the virtual world are connected to monitor risks, collect data, create simulations, and propose protection strategies, quickly and accurately. In digital utilization, the knowledge graph is constructed to associate seemingly unrelated information, explore potential knowledge, and improve information sharing. In addition, the method is validated by means of case studies of historical sites in China. In this paper, the historical sites of the Northern Qi Dynasty in Taiyuan, Shanxi, especially the Xuxianxiu Tomb and Lourui Tomb, are discussed in detail. The results indicate that this method is effective for the protection and utilization of historical sites. [ABSTRACT FROM AUTHOR]

Low-rate Denial of Service (LDoS) attacks are today considered one of the biggest threats against modern data centers and industrial infrastructures. Unlike traditional Distributed Denial of Service (DDoS) attacks that are mainly volumetric, LDoS attacks exhibit a very small network footprint, and therefore can easily elude standard detection and defense mechanisms. This work introduces a defense strategy that may prove particularly effective against attacks that are based on long-lived connections, an inherent trait of LDoS attacks. Our approach is based on iteratively partitioning the active connections of a victim server across a number of replica servers, and then re-evaluating the health status of each replica instance. At its core, this approach relies on live migration and containerization technologies. The main advantage of the proposed approach is that it can discover and isolate malicious connections with virtually no information about the type and characteristics of the performed attack. Additionally, while the defense takes place, there is little to no indication of the fact to the attacker. We assess various rudimentary schemes to quantify the scalability of our approach. The results from the simulations indicate that it is possible to save the vast majority of the benign connections (80%) in less than 5 min. [ABSTRACT FROM AUTHOR]

In order to solve the congestion problem caused by the dramatic growth of traffic in data centers, many end-to-end congestion controls have been proposed to respond to congestion in one round-trip time (RTT). In this paper, we propose a new congestion control mechanism based on backpressure feedback (BFCC), which is designed with the primary goal of switch-to-switch congestion control to resolve congestion in a one-hop RTT. This approach utilizes a programmable data plane to continuously monitor network congestion in real time and identify real-congested flows. In addition, it employs targeted flow control through backpressure feedback. We validate the feasibility of this mechanism on BMV2, a programmable virtual switch based on programming protocol-independent packet processors (P4). Simulation results demonstrate that BFCC greatly enhances flow completion times (FCTs) compared to other end-to-end congestion control mechanisms. It achieves 1.2–2× faster average completion times than other mechanisms. [ABSTRACT FROM AUTHOR]

In data center networks, when facing challenges such as traffic volatility, low resource utilization, and the difficulty of a single traffic scheduling strategy to meet demands, it is necessary to introduce intelligent traffic scheduling mechanisms to improve network resource utilization, optimize network performance, and adapt to the traffic scheduling requirements in a dynamic environment. This paper proposes a fine-grained traffic scheduling scheme based on multi-agent deep reinforcement learning (MAFS). This approach utilizes In-Band Network Telemetry to collect real-time network states on the programmable data plane, establishes the mapping relationship between real-time network state information and the forwarding efficiency on the control plane, and designs a multi-agent deep reinforcement learning algorithm to calculate the optimal routing strategy under the current network state. The experimental results demonstrate that compared to other traffic scheduling methods, MAFS can effectively enhance network throughput. It achieves a 1.2× better average throughput and achieves a 1.4–1.7× lower packet loss rate. [ABSTRACT FROM AUTHOR]

The operation of a four-channel multiplexer, utilizing multimode interference (MMI) wavelength division multiplexing (WDM) technology, can be designed through the cascading of MMI couplers or by employing angled MMI couplers. However, conventional designs often occupy a larger footprint, spanning a few millimeters, thereby escalating the energy power requirements for the photonic chip. In response to this challenge, we propose an innovative design for a four-channel silicon nitride (Si3N4) MMI coupler with a compact footprint. This design utilizes only a single MMI coupler unit, operating within the O-band spectrum. The resulting multiplexer device can efficiently transmit four channels with a wavelength spacing of 20 nm, covering the O-band spectrum from 1270 to 1330 nm, after a short light propagation of 22.8 µm. Notably, the multiplexer achieves a power efficiency of 70% from the total input energy derived from the four O-band signals. Power losses range from 1.24 to 1.67 dB, and the MMI coupler length and width exhibit a favorable tolerance range. Leveraging Si3N4 material and waveguide inputs and output tapers minimizes light reflection from the MMI coupler at the input channels. Consequently, this Si3N4-based MMI multiplexer proves suitable for deployment in O-band transceiver data centers employing WDM methodology. Its implementation offers the potential for higher data bitrates while maintaining an exemplary energy consumption profile for the chip footprint. [ABSTRACT FROM AUTHOR]

To solve the problem that noise seriously affects the online monitoring of parts signals of outdoor machinery, this paper proposes a signal reconstruction method integrating deep neural network and compression sensing, called ADMM-1DNet, and gives a detailed online vibration signal monitoring scheme. The basic approach of the ADMM-1DNet network is to map the update steps of the classical Alternating Direction Method of Multipliers (ADMM) into the deep network architecture with a fixed number of layers, and each phase corresponds to an iteration in the traditional ADMM. At the same time, what differs from other unfolded networks is that ADMM-1DNet learns a redundant analysis operator, which can reduce the impact of outdoor high noise on reconstruction error by improving the signal sparse level. The implementation scheme includes the field operation of mechanical equipment and the operation of the data center. The empirical network trained by the local data center conducts an online reconstruction of the received outdoor vibration signal data. Experiments are conducted on two open-source bearing datasets, which verify that the proposed method outperforms the baseline method in terms of reconstruction accuracy and feature preservation, and the proposed implementation scheme can be adapted to the needs of different types of vibration signal reconstruction tasks. [ABSTRACT FROM AUTHOR]

Cloud computing has become one of the key technologies used for big data processing and analytics. User management on cloud platforms is a growing challenge as the number of users and the complexity of systems increase. In light of the user-management system provided by major cloud service providers, which cannot manage multiple types of user systems, this article proposed scale-out automated expansion user management for authorization synchronization to improve the efficiency and scalability of user management on cloud platforms. Three modules for user-automated expansion were designed and implemented to synchronize the authentication information from the cloud platform resource user to the data-processing user. Additionally, an optimized dynamically weighted load-balancing algorithm in Nginx is presented in this article that adjusts the weight according to load information such as CPU and memory usage, and a better load balance can be achieved. The effectiveness of the proposed user-management system is substantiated by comparing it with two existing infrastructures, including multiple data centers and the Huawei cloud platform. The experimental results validate the finding that scale-out automated expansion user management across the Huawei cloud platform can effectively synchronize data accessing authority with cloud resource utilizing authority. Furthermore, the optimized weighted load-balancing algorithm is also valuable for massive concurrent user registration based on limited cloud resources. In the future, this scale-out user-management system could be applied to other cloud platforms and extended by database synchronization to satisfy the needs of data sharing among multiple types of users belonging to different cloud platforms. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) technology, as a subset of the Internet of Things (IoT) in the concept of Industry 4.0 and, in the future, 5.0, will face the challenge of streamlining the way huge amounts of data are processed by the modules that collect the data and those that analyse the data. Given the key features of these analytics, such as reducing the cost of building massive data centres and finding the most efficient way to process data flowing from hundreds of nodes simultaneously, intermediary devices are increasingly being used in this process. Fog and edge devices are hardware devices designed to pre-analyse terabytes of data in a stream and decide in realtime which data to send for final analysis, without having to send the data to a central processing unit in huge local data centres or to an expensive cloud. As the number of nodes sending data for analysis via collection and processing devices increases, so does the risk of data streams being intercepted. There is also an increased risk of attacks on this sensitive infrastructure. Maintaining the integrity of this infrastructure is important, and the ability to analyse all data is a resource that must be protected. The aim of this paper is to address the problem of autonomous threat detection and response at the interface of sensors, edge devices, cloud devices with historical data, and finally during the data collection process in data centres. Ultimately, we would like to present a machine learning algorithm with reinforcements adapted to detect threats and immediately isolate infected nests. [ABSTRACT FROM AUTHOR]

The quantitative characterization of the thermal conditions in the Tibetan Plateau has long been a focal point of global research. Downward shortwave radiation, as a crucial component, plays an important role in numerous land surface processes while also serving as a significant indicator of the plateau's thermal state. In order to gain a more comprehensive understanding of the Earth's radiation budget in the Tibetan Plateau region, this study undertook an evaluation of six radiation products (ISCCP-FH, CERES-SYN, GLASS DSR, Himawari-8, MCD18A1, and ERA5). Two sets of ground measurements (downward shortwave radiation values from 10 CMA sites and 6 sites provided by the National Tibetan Plateau Data Center) in 2015 and 2016 were used as validation data to verify the accuracy of the remote sensing products. The results show that in the Tibetan Plateau region, CERESC products show the highest accuracy among the six data products with a bias (relative bias) of −7.57 W/m2 (3.46%), RMSE (relative RMSE) of 32.77 W/m2 (14.99%), and coefficient of determination of 0.80. Among all products, only the ERA5 products overestimated the value of downward shortwave radiation in the Tibetan Plateau region with a bias (relative bias) of 15.62 W/m2 (7.14%). By employing a spatial resolution upscaling approach, we assessed the influence of varying spatial resolutions on the validation accuracy, with the results indicating minimal impact. Through an analysis of the impact of cloud factors and aerosol factors on the validation accuracy, it is deduced that ERA5, Himawari-8, and MCD18A1 products are significantly influenced by cloud factors, whereas the CERES-SYN product is notably affected by aerosol factors. [ABSTRACT FROM AUTHOR]

The identification of new psychoactive substances (compounds that mimic the effects of outlawed substances) poses a significant challenge due to their rapid emergence and continuous modifications. This phenomenon results in these molecules escaping legal regulation, allowing them to circumvent legislation. The phenethylamine class has garnered attention because its molecules replicate the effects of LSD and are associated with numerous cases of intoxication. In this study, we focused on three phenethylamines—2C-H, 25H-NBOH, and 25I-NBOMe—with crystallographic structures available in the Cambridge Crystallographic Data Center (CCDC) database. We conducted a systematic conformational analysis and compared the structural information obtained. Subsequently, we compared the spectra derived from this analysis with experimental details from the ENFSI database. Structural comparisons were made based on the RMSDs between the lower energy conformations and experimental crystallographic structures. Additionally, structures obtained from direct optimization were compared. We then simulated the spectra based on the X-ray structures and compared them with those in the experimental database. Interpretation was carried out using heat maps and PCA in Pirouette software. Combining in silico methods with experimental approaches provides a more comprehensive understanding of the characterization process of new psychoactive substances (NPSs). [ABSTRACT FROM AUTHOR]

This paper is an extended version of a previously reported conference paper regarding a low-power design for NAND Flash. As the number of bits per NAND Flash die increases with cost scaling, the IO data path speed increases to minimize the page access time with a scaled CMOS in IOs. The power supply for IO buffers, namely, VDDQ, decreases from 3 V to 1.2 V, accordingly. In this paper, the way in which a reduction in VDDQ can contribute to power reduction in the BL path is discussed and validated. Conventionally, a BL voltage of about 0.5 V has been supplied from a supply voltage source (VDD) of 3 V. The BL path power can be reduced by a factor of VDDQ to VDD when the BL voltage is supplied by VDDQ. To maintain a sense margin at the sense amplifiers, the supply source for BLs is switched from VDDQ to VDD before sensing. As a result, power reduction and an equivalent sense margin can be realized at the same time. The overhead of implementing this operation is an increase in the BL access time of about 2% for switching the power supply from VDDQ to VDD and an increase in the die size of about 0.01% for adding the switching circuit, both of which are not significant in comparison to the significant power reduction in the BL path power of the NAND die of about 60%. The BL path is then designed in 180 nm CMOS to validate the design. When the cost for powering the SSD becomes quite significant, especially for data centers, an additional lower voltage supply, such as 0.8 V, dedicated to BL charging for read and program verifying operations may be the best option for future applications. [ABSTRACT FROM AUTHOR]

Optical interconnects are being discussed as a replacement for conventional electrical interconnects and are expected to be applied for future generations of high-performance supercomputers and data centers. Benes networks have attracted much attention because they require only 2 × 2 optical switches, which reduce the cost of rearrangeable nonblocking. However, optical power imbalances can significantly challenge receiver sensitivity. In this work, insertion loss (IL) fairness has been proposed and applied to the field of switches to achieve a relative balance of optical path data transmission in Benes networks. Fairness can be achieved when the port count is small (4 × 4) if the IL between ports is balanced. When the number of ports is moderate (8 × 8), we must use a suitable algorithm or determine the appropriate operating wavelength to minimize the power imbalance. An efficient two-step algorithm (ETS) has particular advantages in solving the path fairness problem and mitigating the power imbalance. As the number of ports increases, the switch states and topology jointly deteriorate the power imbalance. Finally, the ETS algorithm narrows the dynamic range requirement to 13.66 dB, with a 2 dB improvement. It achieves an extinction ratio of 24 dB and a bandwidth of 375 GHz, which outperforms the conventional 32 × 32 Benes network, respectively. [ABSTRACT FROM AUTHOR]

Fault detection and localization are vital for ensuring the stability of data center networks (DCNs). Specifically, adaptive fault diagnosis is deemed a fundamental technology in achieving the fault tolerance of systems. The highly scalable data center network (HSDC) is a promising structure of server-centric DCNs, as it exhibits the capacity for incremental scalability, coupled with the assurance of low cost and energy consumption, low diameter, and high bisection width. In this paper, we first determine that both the connectivity and diagnosability of the m-dimensional complete HSDC, denoted by H S D C m (m) , are m. Further, we propose an efficient adaptive fault diagnosis algorithm to diagnose an H S D C m (m) within three test rounds, and at most N + 4 m (m − 2) tests with m ≥ 3 (resp. at most nine tests with m = 2 ), where N = m · 2 m is the total number of nodes in H S D C m (m) . Our experimental outcomes demonstrate that this diagnosis scheme of HSDC can achieve complete diagnosis and significantly reduce the number of required tests. [ABSTRACT FROM AUTHOR]

A seafloor observation network (SON) consists of a large number of heterogeneous devices that monitor the deep sea and communicate with onshore data centers. Due to the long-distance information transmission and the risk of malicious attacks, ensuring the integrity of data in transit is essential. A cryptographically secure frame check sequence (FCS) has shown great advantages in protecting data integrity. However, the commonly used FCS has a collision possibility, which poses a security risk; furthermore, reducing the encryption calculation cost is a challenge. In this paper, we propose a secure, lightweight encryption scheme for transmitted data inspired by mimic defense from dynamic heterogeneous redundancy theory. Specifically, we use dynamic keys to encrypt a data block and generate multiple encrypted heterogeneous blocks for transmission. These continuously changing encrypted data blocks increase the confusion regarding the original encoded data, making it challenging for attackers to interpret and modify the data blocks. Additionally, the redundant information from the multiple blocks can identify and recover tampered data. Our proposed scheme is suitable for resource-constrained environments where lightweight encryption is crucial. Through experimental demonstrations and analysis methods, we determine the effectiveness of our encryption scheme in reducing computational costs and improving security performance to protect data integrity. [ABSTRACT FROM AUTHOR]

This paper presents a study of global Rayleigh wave attenuation and group velocity at a period of around 20 s using data from the International Seismological Centre (ISC) bulletin. Rayleigh waves at this period are sensitive to the crustal structure beneath continents and the uppermost mantle beneath oceans. Tomographic imaging reveals strong continental-ocean contrasts due to this. Oceanic group velocities are high but vary with seafloor depth, while oceanic attenuation shows mid-ocean ridges. Subduction zone regions display high attenuation but little velocity reduction, indicating scattering attenuation. Low attenuation regions are associated with the Earth's major cratonic regions, but there are no associated velocity changes. This implies that intrinsic attenuation is low and scattering dominates. Cratonic crustal scatterers have been annealed. A new surface wave magnitude scale is constructed that is valid from near-source to near-antipode distances. [ABSTRACT FROM AUTHOR]

In the era of cloud computing, the reliability and efficiency of distributed systems, particularly in cloud-based databases and applications, are important. State Machine Replication (SMR), underpinning these distributed architectures, commonly utilizes consensus protocols to ensure linearizable operations. These protocols are critical in cloud environments as they maintain data consistency across geographically dispersed data centers. However, the inherent latency in cloud infrastructures poses a challenge to the performance of consensus-based systems, especially for read operations that do not alter the system state and are frequently executed. This paper addresses this challenge by proposing "Asynchronous Consensus Quorum Read" (ACQR), a novel read optimization method specifically designed for asynchronous consensus protocols in cloud computing scenarios. We have incorporated ACQR into Rabia, an advanced asynchronous consensus protocol, to show its effectiveness. The experimental results are encouraging, they demonstrate that ACQR improves Rabia's performance, achieving up to a 1.7× increase in throughput and a 40% reduction in optimal latency. This advancement represents a critical step in enhancing the efficiency of read operations in asynchronous consensus protocols within cloud computing environments. [ABSTRACT FROM AUTHOR]

As demand for data centers (DC) has increased rapidly, so has their electricity consumption. Cooling DCs is essential to maintain optimal temperatures for the operation of servers and equipment. The consequence of the cooling process is that most of the electricity consumed in DCs, including cooling, is eventually dissipated as heat that is released into the atmosphere without any useful application. Recovering and reusing waste heat offers a sustainable solution to reduce primary energy consumption and minimize the environmental impact. Using waste heat from DCs to heat buildings can significantly improve the energy efficiency and environmental sustainability of DCs. Therefore, this research analyzes the existing potential of waste heat recovery from data centers in Latvia and proposes a system dynamic modelling approach for evaluation of the future impact of waste heat on the national heat supply. The overall waste heat generated by DCs in 2022 was 51.37 GWh at a temperature of 65 °C. By 2050, the total heat energy production potential from DCs will increase to 257 GWh, with 201 GWh being utilized. [ABSTRACT FROM AUTHOR]

Cloud computing has gained widespread recognition for facilitating myriad online services and applications. However, the current stages of commercial cloud computing employ a moderate design, wherein computational resources like storage and servers are housed in a few sizable worldwide data centers. System reliability, efficiency, and low latency are all goals of virtual machine (VM) placement. Load balancing has emerged as a crucial challenge for attaining energy efficiency in a fictitious grid computing architecture where a variety of users' workloads are distributed across several virtual machines. We propose a more effective optimization technique known as the twin fold moth flame algorithm. This algorithm considers multiple constraints, including computation time, stability, and placement cost. The proposed model's effectiveness will be evaluated based on relocation costs, reaction times, and stability assessments. The most significant gains of the presented work are 4.24%, 9.73%, 11.10%, 28.83%, 7.63%, and 10.62% for 20 count data of nodes for artificial bee colony–bat algorithm, ant colony optimization, crow search algorithm, krill herd, whale optimization genetic algorithm, and improved Lévy-based whale optimization algorithm, respectively. [ABSTRACT FROM AUTHOR]

With the vigorous development of emerging technology and the advent of the Internet generation, high-speed Internet and fast transmission 5G wireless networks contribute to interpersonal communication. Now, the Internet has become popular and widely available, and human life is inseparable from various experiences on the Internet. Many base stations and data centers have been established to convert and switch from electrical transmission to optical transmission; thus, it is entering the new era of optical fiber networks and optical communication technologies. For optical communication, the manufacturing of components for the purpose of high-speed networks is a key process, and the requirement for the stability of its production conditions is very strict. In particular, product yields are always low due to the restriction of high-precision specifications associated with the limitations of too many factors. Given these reasons, this study proposes a hybrid fuzzy control-based model for industry data applications to organize advanced techniques of box-and-whisker plot method, association rule, and decision trees to find out the determinants that affect the yield rate of products and then use the fuzzy control Proportional-Integral-Derivative (PID) method to manage the determinants. Since it is unrealistic to test the real machine online operation at the manufacturing stage, the simulation software supersedes this for improved results, and a mathematical neural network is used to verify the given data to confirm whether its result is similar to that of the simulation. The study suggests that excessive temperature differentials between substrate and cavity can lead to low yields. It suggests using fuzzy control technology for temperature management, which could increase yield, reduce labor costs, and accelerate the transition to high-speed networks by mass-producing high-precision optical filters. [ABSTRACT FROM AUTHOR]

This paper introduces the forbidden subgraph conditions for Hamilton-connected graphs. If the degree sequence of the graph is (4 , 2 , 2 , 2 , 2) and it is connected, then it is called hourglass Γ 0 . For integers i ≥ 1 , the graph Z i is paw, which is obtained by attaching one of the vertices of the triangle to one of the end vertices of a path with a number of edges i. We show that every graph G is Hamilton-connected if G is a Γ 0 -free, K 1 , 3 -free, Z 14 -free, and a 3-connected graph. Moreover, we give an example to show the sharpness of a paw-type forbidden subgraph in a 3-connected, Hamilton-connected graph. Our focus on the Hamilton-connected problem can be applied to data center networks (DCNs). In the future, we will remove the forbidden subgraph families from our conclusions when building the network to obtain the optimal communication cost. Our result extends the result of Ryjáček and Vrána (Discrete Mathematics 344: 112350, 2021). [ABSTRACT FROM AUTHOR]

Large amounts of data are created from sensors in Internet of Things (IoT) services and applications. These data create a challenge in directing these data to the cloud, which needs extreme network bandwidth. Fog computing appears as a modern solution to overcome these challenges, where it can expand the cloud computing model to the boundary of the network, consequently adding a new class of services and applications with high-speed responses compared to the cloud. Cloud and fog computing propose huge amounts of resources for their clients and devices, especially in IoT environments. However, inactive resources and large number of applications and servers in cloud and fog computing data centers waste a huge amount of electricity. This paper will propose a Dynamic Power Provisioning (DPP) system in fog data centers, which consists of a multi-agent system that manages the power consumption for the fog resources in local data centers. The suggested DPP system will be tested by using the CloudSim and iFogsim tools. The outputs show that employing the DPP system in local fog data centers reduced the power consumption for fog resource providers. [ABSTRACT FROM AUTHOR]

The evolution of interconnected systems and the evolving demands in service requirements have led to data centers integrating multiple heterogeneous technologies that must coexist. Consequently, the resource management and the security of the infrastructure are becoming more complex than in traditional scenarios. In this context, technologies such as Software-Defined Networking (SDN) or Network Function Virtualization (NFV) are being embraced as mechanisms that facilitate communication management. The integration of both technologies into a single framework, termed Software-Defined NFV (SDNFV) introduces a multitude of tools for managing the security of the data center's resources. This work delineates the primary characteristics of the evolution of these communication networks and their application to information security and communications within a data center. It presents an illustrative use case demonstrating the application of these next-generation technologies to detect and mitigate a security issue through virtualized network functions deployed in containers. [ABSTRACT FROM AUTHOR]

A large amount of high-value data are stored in smart grid data centers, but since the resource diversity of a single data center is limited, data sharing becomes especially important in conducting an effective data mining process. However, traditional data-sharing models often use centralized schemes without authentication of the shared objects, making it difficult to establish trust relationships and ensure data privacy. This makes it difficult to break through the problem of data islands. To solve the above problems, this paper proposes a blockchain-based data-sharing incentive model for edge smart grid scenarios. First, the model uses blockchain and proxy re-encryption technology to achieve the security and traceability of the smart grid data-sharing process. Secondly, the data-sharing incentive algorithm is designed using game theory to maximize the willingness of data owners to share data. Finally, the model in this paper is compared and analyzed with other existing data-sharing models, and the designed performance test shows that the scheme in this paper has significant advantages over the other literature schemes in terms of functionality and computational overhead, and the increase in costs is not significant, and the model can meet the requirements for large-scale data sharing in edge smart grid scenarios. [ABSTRACT FROM AUTHOR]