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Low-resource utilization and high-energy consumption have become progressively protuberant issues in cloud data centers. Virtual Machine (VM) migration is the key objective to resolve this issue. Moreover, extreme VM migration might empower Service-Level Agreement (SLA) violations. Few works are considered for optimizing throughput and energy consumption. An efficient VM migration must consider different parameters like network communication overhead, migration overhead, resource utilization, energy consumption and quality of service which is a multi-objective issue. Hence, in this paper, a Modified Pelican Optimization-based Variable Load Mean Function (MPO-VLMF)-based host overload detection is presented and security enhancement is developed. The main motive of this study is to achieve host overload detection and security enhancement. To obtain host overload detection, the variable load mean function is developed. In this mean function, the weight parameter is selected by considering Modified Pelican Optimization (MPO). The Levy flight (LF) is considered for enhancing the updating process of Pelican Optimization (PO). To enhance the security of the system, the Digital Signature-based Encryption (DSE) is developed. Based on this proposed approach, security and host overload detection are obtained. The proposed technique is implemented and evaluated by performance measures. It is compared with the conventional approaches to justify the performance of the system. [ABSTRACT FROM AUTHOR]

The InfiniBand (IB) interconnection technology is widely used in the networks of modern supercomputers and data centers. Among other advantages, the IB-based network devices allow for building multiple network topologies, and the IB control software (subnet manager) supports several routing engines suitable for the most common topologies. However, the implementation of some novel topologies in IB-based networks may be difficult if suitable routing algorithms are not supported, or if the IB switch or NIC architectures are not directly applicable for that topology. This work describes the implementation of the network topology known as KNS in a real HPC cluster using an IB network. As far as we know, this is the first implementation of this topology in an IB-based system. In more detail, we have implemented the KNS routing algorithm in the OpenSM software distribution of the subnet manager, and we have adapted the available IB-based switches to the particular structure of this topology. We have evaluated the correctness of our implementation through experiments in the real cluster, using well-known benchmarks. The obtained results, which match the expected performance for the KNS topology, show that this topology can be implemented in IB-based clusters as an alternative to other interconnection patterns. [ABSTRACT FROM AUTHOR]

Under the challenges posed by the randomness in ocean systems and the lack of labeled observation datasets, a novel DTW-TRSAX method is presented for detecting anomalies of ocean observing time-series (OOTS), which combines dynamic time warping (DTW) algorithm and trend-based symbolic aggregate approximation (TRSAX) distance. The detecting threshold was constructed according to the tracking distance of sliding window. The trend feature was used to improve SAX to capture the temporal characteristics of different OOTS. The datasets from the National Ocean Test Site of China and the National Marine Data Center were selected for verifying the method. The result shows that 98.5% of confidence level is adequate to provide a reasonable trade-off between the false negative and false positive. Compared with four supervised and unsupervised machine learning algorithms, the DTW-TRSAX method performs more efficient. The approach introduced here is reliable and has the potential to run automatically. [ABSTRACT FROM AUTHOR]

In optical data center networks, the utilization of several software-defined network controllers has been implemented to enhance scalability and reliability. However, during dynamic variations in network traffic, static controller deployment often leads to load imbalance among the controllers. Previous studies have proposed dynamic switch migration as a solution to realize controller load balancing. However, the effectiveness of existing switch migration methods remains insufficient. The load balancing performance of controllers demonstrates limited enhancement following migration, and the migration operations themselves result in supplementary costs. As a result, the issue of switch migration efficiency remains unresolved. To tackle controller load balancing, this research introduces the intelligent controller load balancing (ICLB). ICLB incorporates multiple load metrics to evaluate controller load and selects the ideal target controller based on available resources, leading to enhanced load balancing effectiveness. Moreover, it selects switches while the minimizing migration process, effectively reducing supplementary expenses. Additionally, ICLB can simultaneously manage the workload of multiple controllers during a switch migration process, resulting in enhanced migration efficiency. The outcomes of simulations provide evidence that ICLB offers substantial improvements in controller load balancing performance, along with reduced migration expenses compared to existing solutions. [ABSTRACT FROM AUTHOR]

We use Dark Energy Survey Year 3 (DES Y3) clusters with archival XMM–Newton and Chandra X-ray data to assess the centring performance of the redMaPPer cluster finder and to measure key richness observable scaling relations. We find that 10–20 per cent of redMaPPer clusters are miscentred, both when comparing to the X-ray peak position and to the visually identified central cluster galaxy. We find no significant difference in miscentring in bins of low versus high richness or redshift. The dominant reasons for miscentring include masked or missing data and the presence of other bright galaxies in the cluster. For half of the miscentred clusters, the correct central was one of the possible centrals identified by redMaPPer, while for ∼40 per cent of miscentred clusters, the correct central is not a redMaPPer member mostly due to masking. Additionally, we fit scaling relations of X-ray temperature and luminosity with richness. We find a T X–λ scatter of |$0.21\pm 0.01$|⁠. While the scatter in T X–λ is consistent in redshift bins, we find modestly different slopes, with high-redshift clusters displaying a somewhat shallower relation. Splitting based on richness, we find a marginally larger scatter for our lowest richness bin, 20 < λ < 40. We note that the robustness of the scaling relations at lower richnesses is limited by the unknown selection function, but at λ > 75, we detect nearly all of the clusters falling within existing X-ray pointings. The X-ray properties of detected, serendipitous clusters are generally consistent with those of targeted clusters. [ABSTRACT FROM AUTHOR]

The efficient operation of power-electronic-based systems heavily relies on the reliability and longevity of battery-powered systems. An accurate prediction of the remaining useful life (RUL) of batteries is essential for their effective maintenance, reliability, and safety. However, traditional RUL prediction methods and deep learning-based approaches face challenges in managing battery degradation processes, such as achieving robust prediction performance, to ensure scalability and computational efficiency. There is a need to develop adaptable models that can generalize across different battery types that operate in diverse operational environments. To solve these issues, this research work proposes a TransRUL model to enhance battery RUL prediction. The proposed model incorporates advanced approaches of a time series transformer using a dual encoder with integration positional encoding and multi-head attention. This research utilized data collected by the Centre for Advanced Life Cycle Engineering (CALCE) on CS_2-type lithium-ion batteries that spanned four groups that used a sliding window technique to generate features and labels. The experimental results demonstrate that TransRUL obtained superior performance as compared with other methods in terms of the following evaluation metrics: mean absolute error (MAE), root-mean-squared error (RMSE), and R 2 values. The efficient computational power of the TransRUL model will facilitate the real-time prediction of the RUL, which is vital for power-electronic-based appliances. This research highlights the potential of the TransRUL model, which significantly enhances the accuracy of battery RUL prediction and additionally improves the management and control of battery-based systems. [ABSTRACT FROM AUTHOR]

Switches play a critical role as core components in data center networks. The advent of multi-die chiplet packaging as a prevailing trend in complex chip development presents challenges in designing the multi-die packaging of switch chips. With limited inter-die connections in mind, we propose a scalable, unified switch architecture optimized for efficient connectivity. This architecture includes the strategic mapping of data queues, meticulous planning of data paths, and the integration of a unified interface, all aiming to facilitate efficient switch operations within constrained connectivity environments. Our optimization efforts encompass various areas, including refining arbitration strategies, managing mixed unicast and multicast transmissions, and mitigating network congestion to alleviate bottlenecks in data flow. These enhancements contribute to heightened levels of performance and robustness in the switching process. During the validation phase, the structure we propose reduced interconnection usage between dies by 25%, while supporting functions such as unicast and multicast transmissions. [ABSTRACT FROM AUTHOR]

In this paper, a reduced‐switch‐count two‐phase interleaved LLC converter with precise current balancing is proposed by using switching‐control‐capacitor (SCC) technology. Compared with the conventional SCC‐LLC converter, the proposed two‐phase SCC‐LLC converter reduces the switch count with the SCC circuits, and achieves excellent current sharing under all the tolerance conditions between the two‐phase resonant elements. In the proposed converter, the SCC circuit is only used in the first phase. The design of the SCC circuit parameters is analyzed to reduce the voltage stress of the SCC circuit and to ensure that two‐phase currents are balancing with large tolerance of resonant elements. A 2.16‐kW GaN‐based two‐phase interleaved SCC‐LLC converter prototype is established for data center application, and the experimental results show that the 98.1% peak efficiency, the 97.6% full load efficiency, and higher than 96% efficiency at the wide range from 3‐A to 40‐A load current are achieved. The precise current sharing lower than 1% is achieved by using a cheap ceramic capacitor with high tolerance, which validates the performance of the converter. [ABSTRACT FROM AUTHOR]

Since today's HPC and data center systems can comprise hundreds of thousands of servers and beyond, it is crucial to equip them with a network that provides high performance. New topologies proposed to achieve such performance need to be evaluated under different traffic conditions, aiming to closely replicate real-world scenarios. While most optimizations should be guided by common traffic patterns, it is essential to ensure that no pathological traffic pattern can compromise the entire system. Determining synthetic adversarial traffic patterns for a network typically relies on a thorough understanding of its topology and routing. In this paper, we address the problem of identifying a generic adversarial traffic pattern for low-diameter direct interconnection networks. We first focus on Random Regular Graphs (RRGs), which represent a typical case for these networks. Moreover, RRGs have been proposed as topologies for interconnection networks due to their superior scalability and expandability, among other advantages. We introduce Ant Mill, an adversarial traffic pattern for RRGs when using routes of minimal length. Secondly, we demonstrate that the Ant Mill traffic pattern is also adversarial in other low-diameter direct interconnection networks such as Slimfly, Dragonfly, and Projective networks. Ant Mill is thoroughly motivated and evaluated, enabling future studies of low-diameter direct interconnection networks to leverage its findings. [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]

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]

Erasure coding is extensively deployed in today's data centers to tackle prevalent failures, because it can offer higher reliability at lower storage overhead than data replication. However, for each small write, erasure-coded storage systems have to perform a partial write to an entire erasure coding group, resulting in a time-consuming write-after-read. This paper presents DABRI, an erasure-coded hybrid write approach based on data delta for fast partial writes. DABRI uses data deltas that are the differences between latest data values and original data values, instead of parity deltas to recover the failed data. The data node sends the latest data instead of the parity delta to parity nodes for each partial write. The original data stored on the data node is read and sent to the parity nodes, only when the data stored on the parity nodes is insufficient to maintain the data reliability. This can bypass the computation of parity deltas and reduce the number of data reads. For a series of n partial writes to the same data, DABRI performs log-based updates for data and parity in the first write, performs in-place data updates and log-based parity updates for the last n-1 writes. In addition, the I/O between data nodes and parity nodes is scheduled for parallel I/O in each partial write. We implement an erasure-coded prototype storage system based on DABRI to perform performance evaluation. Experimental results running the real-world traces show that DABRI can significantly improve the I/O throughput, compared with the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Today, there is an ever-increasing number of workloads pushed and executed on the Cloud. Data center operators and Cloud providers have embraced application co-location and multi-tenancy as first-class system design concerns to effectively serve and manage these huge computational demands. In addition, the continuous advancements in the computers' hardware technology have made it possible to seamlessly leverage heterogeneous pools of physical machines in data center environments. Even though current modern Cloud schedulers and orchestrators adopt application-aware policies to achieve automation of time-consuming management tasks at scale, e.g., resource provisioning, they still rely on coarse-grained system metrics, such as CPU and/or memory utilization to place incoming applications, thus, not considering (1) interference effects that are provoked by co-located tasks, and (2) the impact on performance caused by the diversity of heterogeneous systems' characteristics. The lack of such knowledge in existing state-of-the-art orchestration solutions results in their inability to perform efficient allocations, which negatively impacts the overall latency distribution delivered by the infrastructure. In this paper, to alleviate this inefficiency, we present a machine learning (ML) based Cloud orchestration extension that takes into account both resource interference and heterogeneity. The framework adequately schedules data-analytics applications on a pool of heterogeneous resources. We evaluate our proposed solution on different application mixes and co-location scenarios. We show that the proposed framework improves the tail latency of the distribution of the deployed applications by up to 3.6x compared to the state-of-the-art Kubernetes scheduler. [ABSTRACT FROM AUTHOR]

The integration of a blockchain-supported serverless computing framework enhances the performance of computational and analytical operations and the provision of services within internet-based data centers, rather than depending on independent desktop computers. Therefore, in the present research paper, a blockchain-assisted serverless framework for geospatial data visualizations is implemented. The proposed BS-GeoEduNet 1.0 framework leverages the capabilities of AWS Lambda for serverless computing, providing a reliable and efficient solution for data storage, analysis, and distribution. The proposed framework incorporates AES encryption, decryption layers, and queue implementation to achieve a scalable approach for handling larger files. It implements a queueing mechanism during the heavier input/output processes of file processing by using Apache KAFKA, enabling the system to handle large volumes of data efficiently. It concludes with the visualization of all geospatial-enabled NIT/IIT details on the proposed framework, which utilizes the data fetched from MongoDB. The experimental findings validate the reliability and efficiency of the proposed system, demonstrating its efficacy in geospatial data storage and processing. [ABSTRACT FROM AUTHOR]

As this Special Section goes to publication, semiconductor manufacturing in the United Status, and globally, continues to expand at a seemingly torrid pace. Assisted by government funding and driven in part by artificial intelligence workloads that gobble up increasing amounts of data center computing capacity, Intel and TSMC fabs are going up in Arizona, TI and Samsung fabs are coming to Texas, and Micron has big plans in New York. Unfortunately, just like those flying cars we were once promised, AI has not yet eliminated the need for the skilled trades and engineers required to build and successfully operate a fab. As a result, workforce development has become an important part of the increasingly complex semiconductor manufacturing process: Where are the thousands of engineers the semiconductor industry needs to staff these new fabs going to come from? How can we make more students excited about science and engineering? While the Guest Editors don’t have all the answers, we are happy that ASMC contributes to the solution by actively supporting student presentations and posters and annually recognizing the best student paper of the conference. And, maybe some day, the artificial intelligence systems that semiconductor manufacturing has enabled will give us those Star Wars or Star Trek robots that can build fabs and make chips too. [ABSTRACT FROM AUTHOR]

The dynamic changes of sea ice exhibit spatial clustering, and this clustering has characteristics extending from its origin, through its development, and to its dissipation. Current research on sea ice change primarily focuses on spatiotemporal variation trends and remote correlation analysis, and lacks an analysis of spatiotemporal evolution characteristics. This study utilized monthly sea ice concentration (SIC) data from the National Snow and Ice Data Center (NSIDC) for the period from 1979 to 2022, utilizing classical spatiotemporal clustering algorithms to analyze the clustering patterns and evolutionary characteristics of SIC anomalies in key Arctic regions. The results revealed that the central-western region of the Barents Sea was a critical area where SIC anomaly evolutionary behaviors were concentrated and persisted for longer durations. The relationship between the intensity and duration of SIC anomaly events was nonlinear. A positive correlation was observed for shorter durations, while a negative correlation was noted for longer durations. Anomalies predominantly occurred in December, with complex evolution happening in April and May of the following year, and concluded in July. Evolutionary state transitions mainly occurred in the Barents Sea. These transitions included shifts from the origin state in the northwestern margin to the dissipation state in the central-north Barents Sea, from the origin state in the central-north to the dissipation state in the central-south, and from the origin state in the northeastern to the dissipation state in the central-south Barents Sea and southeastern Kara Sea. Various evolutionary states were observed in the same area on the southwest edge of the Barents Sea. These findings provide insights into the evolutionary mechanism of sea ice anomalies. [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]

With technological advancements, much data is being captured by sensors, smartphones, wearable devices, and so forth. These vast datasets are stored in data centres and utilized to forge data‐driven models for the condition monitoring of infrastructures and systems through future data mining tasks. However, these datasets often surpass the processing capabilities of traditional information systems and methodologies due to their significant size. Additionally, not all samples within these datasets contribute valuable information during the model training phase, leading to inefficiencies. The processing and training of Machine Learning algorithms become time‐consuming, and storing all the data demands excessive space, contributing to the Big Data challenge. In this paper, we propose two novel techniques to reduce large time‐series datasets into more compact versions without undermining the predictive performance of the resulting models. These methods also aim to decrease the time required for training the models and the storage space needed for the condensed datasets. We evaluated our techniques on five public datasets, employing three Machine Learning algorithms: Holt‐Winters, SARIMA, and LSTM. The outcomes indicate that for most of the datasets examined, our techniques maintain, and in several instances enhance, the forecasting accuracy of the models. Moreover, we significantly reduced the time required to train the Machine Learning algorithms employed. [ABSTRACT FROM AUTHOR]

Deep learning has revolutionized many sectors of industry and daily life, but as application scale increases, performing training and inference with large models on massive datasets is increasingly unsustainable on existing hardware. Highly parallelized hardware like Graphics Processing Units (GPUs) are now widely used to improve speed over conventional Central Processing Units (CPUs). However, Complementary Metal-oxide Semiconductor (CMOS) devices suffer from fundamental limitations relying on metallic interconnects which impose inherent constraints on bandwidth, latency, and energy efficiency. Indeed, by 2026, the projected global electricity consumption of data centers fueled by CMOS chips is expected to increase by an amount equivalent to the annual usage of an additional European country. Silicon Photonics (SiPh) devices are emerging as a promising energy-efficient CMOS-compatible alternative to electronic deep learning accelerators, using light to compute as well as communicate. In this review, we examine the prospects of photonic computing as an emerging solution for acceleration in deep learning applications. We present an overview of the photonic computing landscape, then focus in detail on SiPh integrated circuit (PIC) accelerators designed for different neural network models and applications deep learning. We categorize different devices based on their use cases and operating principles to assess relative strengths, present open challenges, and identify new directions for further research. [ABSTRACT FROM AUTHOR]

Recent years, the tremendous number of distributed energy resources, electric vehicles are integrated into the Low-Voltage Distribution Network (LVDN), large amount of data are generated by edge devices in LVDN. The cloud data centers are unable to process these data timely and accurately, making it impossible to meet the demand for fine-grained control of LVDN. To solve the above problems, this paper proposes a flexible orchestration of lightweight artificial intelligence (AI) for edge computing in LVDN. Firstly, the application requirements of LVDN are analysed through feature extraction of its historical data, and a lightweight AI library is constructed to meet its requirements. Secondly, based on the multifactor priority, a flexible orchestration model is established, to allow the lightweight AI embedded in the edge devices of the LVDN. Finally, the particle swarm optimization algorithm is used to provide the best solution. The simulation results show that the method proposed in this paper can support the deployment of AI at the edge. It can significantly improve the utilization of edge computing resources, and reduce the pressure of cloud computing and the time of application processes. [ABSTRACT FROM AUTHOR]

The HALO–(AC)3 aircraft campaign was carried out in March and April 2022 over the Norwegian and Greenland Seas, the Fram Strait, and the central Arctic Ocean. Three research aircraft, the High Altitude and Long Range Research Aircraft (HALO), Polar 5, and Polar 6, performed 54 partly coordinated research flights on 23 flight days over areas of open ocean, the marginal sea ice zone (MIZ), and the central Arctic sea ice. The general objective of the research flights was to quantify the evolution of air mass properties during moist and warm air intrusions (WAIs) and cold air outbreaks (CAOs). To gain a comprehensive data set, the three aircraft followed different strategies. HALO was equipped with active and passive remote sensing instruments and dropsondes to cover the regional evolution of cloud and thermodynamic processes. Polar 5 carried a similar remote sensing payload as HALO, and Polar 6 was instrumented with in-situ cloud, aerosol, and trace gas instruments focusing on the initial air mass transformation close to the MIZ. The processed, calibrated, and validated data are published in the World Data Center PANGAEA as instrument-separated data subsets and listed in aircraft-separated collections for HALO (Ehrlich et al., 2024a), Polar 5 (Mech et al., 2024a), and Polar 6 (Herber et al., 2024). A detailed overview of the available data sets is provided here. Furthermore, the campaign-specific instrument setup, the data processing, and data quality are summarized. Based on measurements conducted during a specific CAO, it is shown that the scientific analysis of the HALO– (AC)3 data benefits from the coordinated operation of the three aircraft. [ABSTRACT FROM AUTHOR]

With the continuous expansion of network scale, fault tolerance and security in network operation have become increasingly important. The high vertex connectivity of the interconnection network indicates excellent fault tolerance. In order to address the drawbacks of traditional connectivity, the g-extra connectivity has been proposed to better reflect the fault tolerance of the network. RRect is a newly proposed server-centered data center network with good interconnection structure, and it has excellent flexibility and energy efficiency. In this paper, we focus on the logical structure of RRect, denoted by R R n , m , k . Firstly, we investigate the extra connectivity of R R n , m , k , and, in detail, we prove that for n ≥ 4 , m ≥ 1 , and k ≥ 4 , the { 1 , 2 , 3 } -extra connectivity of R R n , m , k is 2 k m (n - 1) + m n - 2 , 3 k m (n - 1) + m n - 3 , and 4 k m (n - 1) - 4 m , respectively, which are about 2, 3, and 4 times of its traditional connectivity. Secondly, we conduct numerical simulation experiments on the extra connectivity of R R n , m , k and evaluate the experimental performance. The result can provide a more comprehensive measurement of its reliability. [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]

With the rapid growth of the digital economy, data centers have emerged as significant consumers of electricity. This presents challenges due to their high energy demand but also brings opportunities for utilizing waste heat. This paper introduces an operation optimization method for multi-energy systems with data centers, leveraging the information gap decision theory (IGDT) to consider various uncertainties from data requests and the environment. First, a model is established for the operation of a multi-energy system within data centers, considering the integration of server waste heat recovery technology. Second, IGDT is employed to address uncertainties of photovoltaic output and data load requests, thereby formulating an optimal energy management strategy for the data center park. Case studies demonstrate that the electricity purchase cost increased by 5.3%, but the total cost decreased by 30.4%, amounting to 5.17 thousand USD after optimization. It indicates that the operational strategy effectively ensures both efficient and cost-effective power supply for the data center and the park. Moreover, it successfully mitigates the risks associated with fluctuations in data load, thus minimizing the possibility of data load abandonment during uncertain periods. [ABSTRACT FROM AUTHOR]

With the growth of the cloud computing market, the number and scale of cloud data centers are expanding rapidly. While cloud data centers provide a large amount of computing power, generating tremendous energy consumption has become a fundamental issue in the financial and environmental fields. Improving quality of service and reducing energy costs are fundamental challenges for next-generation cloud data centers. Task scheduling in cloud data centers grows increasingly complex due to the heterogeneity of computing resources, intricate dependencies of jobs and rising expenses resulting from high energy consumption. Efficiently utilizing computing resources is crucial, so it is necessary to develop optimal strategies for job scheduling. This paper proposes a reinforcement learning-based task scheduler (E2DSched) for online scheduling of randomly arriving directed acyclic graph jobs in cloud data centers. E2DSched divides the scheduling process into three layers: task selection layer, server selection layer and frequency control layer. It achieves joint optimization of energy consumption and quality of service through three-layer cooperation. Finally, we compare E2DSched with various other algorithms, and the results show that E2DSched can provide excellent service with less energy consumption. [ABSTRACT FROM AUTHOR]

Natural resources and geographic information big data are regarded as essential productive factors in the context of digital government, and an important component of the national integrated government big data system. Due to data dispersion and isolated application program existing in the various departments, it is difficult to realize data sharing and application across departments and businesses, leading to the low utilization rate of data. In response to the issues mentioned above, the digital government spatio-temporal data center was designed with the demands for natural resource and geographic information big data services. The critical components, including storage computation, data structure and application support were introduced clearly. By integrating the natural resources and geographic information database with the population comprehensive database and legal person comprehensive database, an organic composition of people, enterprises and geographic locations was established, which was explained by the specific application practices. [ABSTRACT FROM AUTHOR]

The early diagnosis and treatment of foreign body aspiration (FBA) can significantly improve the overall prognosis of children. There are significant differences in the epidemiology and clinical characteristics of FBA in different regions. Therefore, we conducted a real-world study in the western region of China with over 4000 patients. The aim of this study was to improve the understanding of FBA in terms of its types, the specific months of its occurrence, and the distribution of primary caregiver characteristics in western China. We collected the clinical and epidemiological data of children who were diagnosed with FBA in our hospital over the past 20 years through a big data centre. We matched the data of healthy children who underwent routine physical examinations at the paediatric health clinic during the same period to analyse the differences in the data of actual guardians. A total of 4227 patients from five provinces were included in this study. Foreign bodies were removed by rigid bronchoscopy in 99.4% (4202/4227) of patients, with a median age of 19 months and a median surgical duration 16 min. January was the most common month of onset for 1725 patients, followed by February, with 1027 patients. The most common types of foreign objects were melon peanuts, seeds and walnuts, accounting for 47.2%, 15.3%, and 10.2%, respectively. In the FBA group, the proportion of grandparents who were primary caregivers was 70.33% (2973/4227), which was significantly greater than the 63.05% in the healthy group (2665/4227) (P < 0.01). FBA most commonly occurs in January and February. More than 60% of FBAs occur between the ages of 1 and 2 years, and the incidence of FBA may be greater in children who are cared for by grandparents. A rigid bronchoscope can be used to remove most aspirated foreign bodies in a median of 16 min. [ABSTRACT FROM AUTHOR]

Edge computing serves as a critical intermediary for secure data exchange between IoT devices and data centres in healthcare, where the protection of sensitive patient data is paramount. This study presents UdesMec, a comprehensive edge computing framework designed for efficient and secure data storage and processing in such contexts. Focusing on the challenge of implementing robust data security models in the presence of limited resources at edge nodes, UdesMec employs a unified encryption approach that is particularly suited for expert knowledge-based prediction of complex critical medical events. Using secret sharing and homomorphic encryption, it ensures the confidentiality and integrity of sensitive patient data transmitted by IoT devices, while enabling efficient computation of prediction algorithms on a cloud server. The experimental evaluation confirms the strong encryption performance and the ease of use of UdesMec, positioning it as a promising solution for the secure and reliable prediction of complex critical medical events in healthcare systems based on edge computing. [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 physical river characteristics datasets described here provide spatially consistent information to support hydrological and inundation modelling at a 1 km × 1 km resolution across the United Kingdom, on the British National Grid. The datasets of physical river characteristics provide gridded datasets (outflow drainage directions, catchment areas, widths of bankfull rivers and depths of bankfull rivers) and a comma‐separated table of NRFA (National River Flow Archive) gauging station locations. These datasets are derived from a range of sources: outflow drainage directions, catchment area and bankfull river widths are derived from existing higher resolution datasets, whereas bankfull river depths were harder to source and instead are derived from sparse historical measurements. The new gridded datasets provide a derived value for each UK land cell on the British National Grid (BNG). The comma‐separated NRFA gauging station locations table provides the most appropriate locations of 1,499 river flow gauging stations on the 1 km resolution grids, together with the approximate error in the 1 km × 1 km gridded delineation of the upstream catchment area. This article explains how UK‐wide 1‐km grids of these variables were estimated, their format and how to use them. The data are available from the Environmental Information Data Centre (EIDC). [ABSTRACT FROM AUTHOR]

Overview: Data Collaboratives have gained traction as interorganizational partnerships centered on data exchange. They enhance the collective capacity of responding to contemporary societal challenges using data, while also providing participating organizations with innovation capabilities and reputational benefits. Unfortunately, data collaboratives often fail to advance beyond the pilot stage and are therefore limited in their capacity to deliver systemic change. The governance setting adopted by a data collaborative affects how it acts over the short and long term. We present a governance design model to develop context-dependent data collaboratives. Practitioners can use the proposed model and list of key reflective questions to evaluate the critical aspects of designing a governance model for their data collaboratives. Practitioners can develop a detailed understanding of the governance factors that influence the successful creation of data collaboratives. The governance design process model offers practitioners a tangible roadmap and concrete activities to help them revamp an existing data collaborative or create a new one. The list of reflective questions can guide practitioners' initial design efforts and help them assess the robustness of their data collaboratives' current governance models. [ABSTRACT FROM AUTHOR]

Worldwide, the majority of countries are actively devising strategies to address the challenges associated with unregulated and unmanageable development, the decline in environmental quality and the depletion of valuable agricultural land. This has led to a growing emphasis on understanding land use and land cover. In order to determine a better land use policy, legislators and planners need to know the current distribution of agricultural and urban lands, as well as information about changes in their proportions. Our approach combines data centred on main four themes—geology, slope gradient, hydrographic network and land use—in order to exploit classifier complementarities in our targeted agricultural study area of Tamlouka Basin, Algeria. Landsat 8 OLI-TIRs multispectral imagery and Shuttle Radar Topography Mission (SRTM-1arc v3) were used experimentally for classification and Digital Elevation Model (DEM) analysis. The classification's accuracy is confirmed by comparing the results of the decision tree classification with the validation samples. Results of the combination of several maps of classifications from the different methods show that the Tamlouka alluvial plain, having an area of 19,300 ha and an average slope gradient of less than 2°, drains the elevated reliefs that surround it via hydrographic network. The plain occupies 37% of the total basin area, with over of 60% being used for crop cultivation, regardless of fallow land areas in agricultural rotation at that time. The slope has been identified as a crucial factor determining land use patterns in the study area. This result can be used in prospective watershed management. [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]

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]

Modern service systems, like cloud computing platforms or data center environments, commonly face a high degree of heterogeneity. This heterogeneity is not only caused by different server speeds but also, by binding task-server relations that must be taken into account when assigning incoming tasks. Unfortunately, there are hardly any theoretical performance guarantees as these systems do not fall within the typical supermarket modeling framework which heavily relies on strong symmetry and homogeneity assumptions. In "Heavy-traffic universality of redundancy systems with assignment constraints," Cardinaels, Borst, and van Leeuwaarden provide insight in the performance of these systems operating under redundancy scheduling policies. Surprisingly, when experiencing high demand, these systems exhibit state space collapse and can achieve a similar level of resource pooling and performance as a fully flexible system, even subject to quite strict task-server constraints. Service systems often face task-server assignment constraints because of skill-based routing or geographical conditions. Redundancy scheduling responds to this limited flexibility by replicating tasks to specific servers in agreement with these assignment constraints. We gain insight from product-form stationary distributions and weak local stability conditions to establish a state space collapse in heavy traffic. In this limiting regime, the parallel-server system with redundancy scheduling operates as a multiclass single-server system, achieving full resource pooling and exhibiting strong insensitivity to the underlying assignment constraints. In particular, the performance of a fully flexible (unconstrained) system can be matched even with rather strict assignment constraints. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2385. [ABSTRACT FROM AUTHOR]

Sensor networks generate large volumes of data that require robust processing, storage, and analysis capabilities provided by data centers. Software-defined networking, as a new network technique for designing and building networks, provides new concepts and solutions for the current data center system construction. However, the software-defined networking (SDN) framework based on the OpenFlow protocol cannot support new protocols or actively add unknown protocols, since they cause the control plane protocol to become more bloated. It is difficult to solve the problem of network scalability because components are being continuously redesigned. Therefore, an optimized source routing technology is proposed in combination with the protocol-oblivious forwarding technology. Furthermore, a low-cost and extensible network switch is designed to realize the independent data forwarding mechanism that effectively reduces the amount of network control signaling. The experimental results show that the scheme can significantly reduce the scale of flow table items and improve the hardware forwarding rates as well as the flexibility of forwarding rules. Moreover, the design scheme has a good application prospect. [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]