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Large ensembles of global temperature are provided for three climate scenarios: historical (2006–16), 1.5 °C and 2.0 °C above pre-industrial levels. Each scenario has 700 members (70 simulations per year for ten years) of 6-hourly mean temperatures at a resolution of 0.833° ´ 0.556° (longitude ´ latitude) over the land surface. The data was generated using the climateprediction.net (CPDN) climate simulation environment, to run HadAM4 Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre. Biases in simulated temperature were identified and corrected using quantile mapping with reference temperature data from ERA5. The data is stored within the UK Natural and Environmental Research Council Centre for Environmental Data Analysis repository as NetCDF V4 files. [ABSTRACT FROM AUTHOR]

Snapshots derived from the World Soil Information Service (WoSIS) are served freely to the international community. These static datasets provide quality-assessed and standardised soil profile data that can be used to support digital soil mapping and environmental applications at broad scale levels. Since the release of the preceding snapshot in 2019, new ETL (Extract, Load, Transform) procedures for screening, ingesting and standardising disparate source data have been developed. In conjunction with this, the WoSIS data model was overhauled making it compatible with the ISO 28258 and Observations and Measurements (O&M) domain models. Additional procedures for querying, serving, and downloading the publicly available standardised data have been implemented using open software (e.g. GraphQL API). Following up on a short discussion of these methodological developments we discuss the structure and content of the "WoSIS 2023-snapshot". A range of new soil datasets was shared with us, registered in the ISRIC World Data Centre for Soils (WDC-Soils) data repository, and subsequently processed in accordance with the licences specified by the data providers. An important effort has been the processing of forest soil data collated in the framework of the EU-HoliSoils project. We paid special attention to the standardisation of soil property definitions, description of the soil analytical procedures, and standardisation of the units of measurement. The "2023 snapshot" considers the following soil chemical properties (total carbon, organic carbon, inorganic carbon (total carbonate equivalent), total nitrogen, phosphorus (extractable-P, total-P, and P-retention), soil pH, cation exchange capacity, and electrical conductivity) and physical properties (soil texture (sand, silt, and clay), bulk density, coarse fragments, and water retention), grouped according to analytical procedures that are operationally comparable. Method options are defined for each analytical procedure (e.g. pH measured in water, KCl or CaCl2 solution, molarity of the solution, and soil/solution ratio). For each profile we also provide the original soil classification (i.e. FAO, WRB and USDA system with their version) and pedological horizon designations as far as these have been specified in the source databases. Three measures for "fitness-for-intended-use" are provided to facilitate informed data use: a) positional uncertainty of the profile's site location, b) possible uncertainty associated with the operationally defined analytical procedures, and c) date of sampling. The most recent (i.e. dynamic) dataset, called wosis_latest , is freely accessible via various webservices. To permit consistent referencing and citation we also provide a static snapshot (in casu, December 2023). This snapshot comprises quality-assessed and standardised data for 228 k geo-referenced profiles. The data come from 174 countries and represent more than 900 k soil layers (or horizons) and over 6 million records. The number of measurements for each soil property vary (greatly) between profiles and with depth, this generally depending on the objectives of the initial soil sampling programmes. In the coming years, we aim to gradually fill gaps in the geographic distribution of the profiles, as well as in the soil observations themselves, this subject to the sharing of a wider selection of "public" soil data by prospective data contributors. The WoSIS 2023-snapshot is archived and freely available at https://doi.org/10.17027/isric-wdcsoils-20231130 (Calisto et al., 2023). [ABSTRACT FROM AUTHOR]

As information propels are changing day-to-day and creating at an incredible speed, the data society is becoming more complex, competitive, and trapped to inventive changes and information blasts. They ought to for E-information organisations to clients in expansion to expanding and getting to be exceptionally essential, the result of the web-based e-learning and teaching environment has affected each perspective of library and data organisations in educational libraries and given present-day openings and challenges to the library table for consideration interior the knowledge-based society tallying electronic and intelligently media conveying, and Web-based information organisations, around the world organising, web-based computerised resources, etc. Parents are tasked for selecting and organising resources coordinating advocates on the way to find and utilise these and securing information no matter the course of action or development. The data change, and in this way, the data that are available online have made unused challenges to those routinely capable ethics. The rising challenges of getting and giving electronic data resources require overseers to vary their portion from customary overseers to information analysts by learning and applying present-day aptitudes to know the advancing innovations to supervise and supply quality online information organisations to the data society. Thus, the vision of the longer-term educational library capability must be to form a World Course Organised around the World Library and Data Center to supply helpful web-based quality information advantage to the client in time interior the e-learning environment. The longer-term dreams require changes in scholarly libraries, designs, and challenges a few times as of late, the library capability interior the learning environment, and in this way, the diverse changing parts of the directions work out library capability have been talked around amid this study. [ABSTRACT FROM AUTHOR]

This study examines the characteristics of school shootings and investigates the relationship between perpetrators' race and how shootings are reported by the media. Findings, utilizing data from the Center for Homeland Defense and Security, demonstrate differences by race in the characteristics of school shootings and media reporting of school shootings. Inaccurate and incomplete portrayals of school gun violence in data repositories and the media may lead to public perceptions and policy responses that do not adequately address root causes of violence. In addition, these inaccurate depictions shroud the reality of gun violence in K-12 schools. Accurate information regarding the circumstances of gun violence in schools is a necessary component of preventing future violence. [ABSTRACT FROM AUTHOR]

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain. Characterizing cellular diversity is necessary to understand the function of different cell types in the brain. This Consensus View provides a guide to the cellular and spatial approaches used in cell type surveys by the BRAIN Initiative Cell Census Network (BICCN), as well as information on accessing and using the BICCN data and its extensive resources. [ABSTRACT FROM AUTHOR]

With the development of data centers, network bandwidth has rapidly increased, reaching hundreds of Gbps. However, the network I/O processing performance of CPU improvement has not kept pace with this growth in recent years, which leads to the CPU being increasingly burdened by network applications in data centers. To address this issue, Data Processing Unit (DPU) has emerged as a hardware accelerator designed to offload network applications from the CPU. As a new hardware device, the DPU architecture design is still in the exploration stage. Previous DPU benchmarks are not neutral and comprehensive, making them unsuitable as general benchmarks. To showcase the advantages of their specific architectural features, DPU vendors tend to provide some particular architecture-dependent evaluation programs. Moreover, they fail to provide comprehensive coverage and cannot adequately represent the full range of network applications. To address this gap, we propose an application-driven scalable benchmark suite called DPUBench. DPUBench classifies DPU applications into three typical scenarios — network, storage, and security, and includes a scalable benchmark framework that contains essential Operator Set in these scenarios and End-to-end Evaluation Programs in real data center scenarios. DPUBench can easily incorporate new operators and end-to-end evaluation programs as DPU evolves. We present the results of evaluating the NVIDIA BlueField-2 using DPUBench and provide optimization recommendations. [ABSTRACT FROM AUTHOR]

The main task of the National Research Program "Smart crop production", supported by the Ministry of Education and Science of Bulgaria and approved by the Council of Ministers, is the development of a regional data center to facilitate the work of farmers. The regional data center is part of the implementation of a smart crop production environment called ZEMEL which provides personal assistants supporting the work of farmers. The environment provides intelligent services for crop analysis and prevention and assists farmers in performing basic tasks related to crop production. The objective of the proposed article is to present the implementation of the architecture, infrastructure, and data architecture of a regional data center in the Plovdiv region. In order to clearly present the results of this work, which are the architectural and physical implementations of a regional data center and the storage of dynamic data and background knowledge, a methodology consisting of several steps is followed: the system infrastructure of the data center and the data architecture are discussed; one of the local pieces of infrastructure, implemented in the Institute of Plant Genetic Resources (IPGR) in the town of Sadovo in the Plovdiv region, is presented in detail, including the different types of sensors and their connection to the data center in wheat cultivation; the data repositories are discussed where dynamic data and background knowledge are stored. The paper pays special attention to background knowledge developed as ontologies for winter wheat cultivation. The results are summarized by drawing some conclusions and recommendations for the design of the local infrastructure of the center and the stored data to improve its performance. [ABSTRACT FROM AUTHOR]

Copyright of Soziale Welt is the property of Nomos Verlagsgesellschaft mbH & Co. KG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Federated learning techniques aim to train and build machine learning models based on distributed datasets across multiple devices while avoiding data leakage. The main idea is to perform training on remote devices or isolated data centers without transferring data to centralized repositories, thus mitigating privacy risks. Data analytics in education, in particular learning analytics, is a promising scenario to apply this approach to address the legal and ethical issues related to processing sensitive data. Indeed, given the nature of the data to be studied (personal data, educational outcomes, and data concerning minors), it is essential to ensure that the conduct of these studies and the publication of the results provide the necessary guarantees to protect the privacy of the individuals involved and the protection of their data. In addition, the application of quantitative techniques based on the exploitation of data on the use of educational platforms, student performance, use of devices, etc., can account for educational problems such as the determination of user profiles, personalized learning trajectories, or early dropout indicators and alerts, among others. This paper presents the application of federated learning techniques to a well-known learning analytics problem: student dropout prediction. The experiments allow us to conclude that the proposed solutions achieve comparable results from the performance point of view with the centralized versions, avoiding the concentration of all the data in a single place for training the models. [ABSTRACT FROM AUTHOR]

Monitoring observations of solar microwave fluxes and their polarization began in Japan during the 1950s at Toyokawa and Mitaka. At present (April 2022), monitoring observations continues with the Nobeyama Radio Polarimeters (NoRP) at the Nobeyama campus of the National Astronomical Observatory of Japan (NAOJ). In this paper, we present a brief history of the solar microwave monitoring observations preceding those now carried out by NoRP. We then review the solar microwave flux and polarization measurements obtained at Toyokawa and Nobeyama, and their metadata. The datasets are publicly provided by the Solar Data Archive System (SDAS) operated by the Astronomy Data Center of the NAOJ, via http (https://solar.nro.nao.ac.jp/norp/) and FTP (ftp://solar -pub.nao.ac.jp/pub/nsro/norp/) protocols. [ABSTRACT FROM AUTHOR]

The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC) celebrates its 25th anniversary in 2022. The DKRZ (German Climate Computing Center; German: Deutsches Klimarechenzentrum) is the only remaining DDC Partner from the original group jointly managing the DDC. In spite of changes in prioritization, it has been supporting the IPCC Assessments and preserving the quality-assured, citable climate model data underpinning the Assessment Reports over these years over the long term. An active and engaged collaborative community achieved advances in data standardization, data management best practices, and infrastructure developments. These evolving standards are reflected in the activities of the DDC. The introduction of the IPCC FAIR Guidelines into the current Sixth IPCC Assessment Report (AR6) has significantly changed the role of the DDC Partner DKRZ from an independent partner for long-term data preservation into an active partner involved in the IPCC's Sixth Assessment cycle. As a result, the DDC has gained exposure and visibility, posing a challenge and an opportunity to operationalize the IPCC's FAIR Guidelines and long-term preservation approaches. While the value of DDC services has been recognized, DDC sustainability remains unresolved and is currently being discussed within the IPCC as part of a general AR6 review process to formulate recommendations for the AR7 data management. [ABSTRACT FROM AUTHOR]

Coded caching is an emerging technique to reduce the data transmission load during the peak-traffic times. In such a scheme, each file in the data center or library is divided into a number of packets to pursue a low broadcasting rate based on the designed placements at each user’s cache. However, the implementation complexity of this scheme increases with the number of packets. It is crucial to design a scheme with a small subpacketization level, while maintaining a relatively low transmission rate. Recently, a combinatorial structure called placement delivery array (PDA) was proposed as an effective tool to design coded caching schemes with a relatively low subpacketization level. This paper proposes a novel PDA construction by selecting proper orthogonal arrays (POAs), which generalizes the existing construction but with a more flexible memory size. Based on the proposed PDA construction, an effective transform is further proposed to enable a coded caching scheme to achieve a smaller subpacketization level. Moreover, two new coded caching schemes with the coded placement are derived. It is shown that the proposed schemes can yield a lower subpacketization level or transmission rate over the benchmark schemes. [ABSTRACT FROM AUTHOR]

The European Soil Data Centre (ESDAC), hosted by the European Commission's Joint Research Centre (JRC), is the focal point for soil data, support to policy making and awareness raising for the European Union (EU). Established in 2006 to provide harmonised soil‐related information for the EU Member States, and ESDAC currently hosts 88 datasets, 6000 maps, six atlases, 500 scientific publications, and a copious amount of soil‐related material. Through its data repository publishing activity, ESDAC has licensed over 50,000 datasets during the past 15 years; 8500 of them in 2021 alone. It has published 140 monthly newsletters and is followed by more than 12,000 subscribed users, which receive regular updates. This article addresses the use, usability, and usefulness of ESDAC. About 75% of the ESDAC users come from academia and the research community while the remaining 25% includes public administration (at EU, national, regional, and local level) and the private sector. In addition, we provide some insights of the datasets evaluation and how they have been developed. The general ESDAC vision is to provide evidence underlying EU soil‐relevant policies and to facilitate the access to relevant data for research. ESDAC is an integral part of the recently established European Union Soil Observatory (EUSO), with a target to have an even stronger role in supporting EU and regional policies. Highlights: ESDAC is a central place from where to find European wide relevant soil data.ESDAC is an integral part of the EU Soil Observatory in creating data and knowledge for policy support.The ESDAC website shows a high volume of traffic; 10,000 of user licenses are granted per year.ESDAC key success: open access data policy, documentation, operational helpdesk and regular updates. [ABSTRACT FROM AUTHOR]

Metadata describe information about data source, type of creation, structure, status and semantics and are prerequisite for preservation and reuse of medical data. To overcome the hurdle of disparate data sources and repositories with heterogeneous data formats a metadata crosswalk was initiated, based on existing standards. FAIR Principles were included, as well as data format specifications. The metadata crosswalk is the foundation of data provision between a Medical Data Integration Center (MeDIC) and researchers, providing a selection of metadata information for research design and requests. Based on the crosswalk, metadata items were prioritized and categorized to demonstrate that not one single predefined standard meets all requirements of a MeDIC and only a maximum data set of metadata is suitable for use. The development of a convergence format including the maximum data set is the anticipated solution for an automated transformation of metadata in a MeDIC. [ABSTRACT FROM AUTHOR]

The Nadir Mapper (NM) and Nadir Profiler (NP) within the Ozone Mapping and Profiler Suites (OMPS) are ultraviolet spectrometers to measure Earth radiance and Solar irradiance spectra from 300–380 nm and 250–310 nm, respectively. The OMPS NM and NP instruments flying on the Suomi-NPP (SNPP) satellite have provided over ten years of operational Sensor Data Records (SDRs) data sets to support a variety of OMPS Environmental Data Record (EDR) applications. However, the discrepancies of quality remain in the operational OMPS SDR data prior to 28 June 2021 due to changes in calibration algorithms associated with the calibration coefficient look-up tables (LUTs) during this period. In this study, we present results for the newly ( v 2 ) reprocessed SNPP OMPS NM and NP SDR data prior to 30 June 2021, which uses consistent calibration tables with improved accuracy. Compared with a previous ( v 1 ) reprocessing, this new reprocessing includes the improvements associated with the following updated tables or error correction: an updated stray light correction table for the NM, an off-nadir geolocation error correction for the NM, an artificial offset error correction in the NM dark processing code, and biweekly solar wavelength LUTs for the NP. This study further analyzes the impact of each improvement on the quality of the OMPS SDR data by taking advantage of the existing OMPS SDR calibration/validation studies. Finally, this study compares the v 2 reprocessed OMPS data sets with the operational and the v 1 reprocessed data sets. The results demonstrate that the new reprocessing significantly improves the accuracy and consistency of the life-time SNPP OMPS NM and NP SDR data sets. It also advances the uniformity of the data over the dichroic range from 300 to 310 nm between the NM and NP. The normalized radiance differences at the same wavelength between the NM and NP observations are reduced from 0.001 order ( v 1 reprocessing) or 0.01 order (operational processing) to 0.001 order or smaller. The v 2 reprocessed data are archived in the NOAA CLASS data center with the same format as the operational data. [ABSTRACT FROM AUTHOR]

The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC) celebrates its 25th anniversary in 2022. DKRZ is the last remaining founding member among the DDC Partners. The continuous developments in data standardization, data management best practices and infrastructure developments are reflected in the DDC’s operations. Examples for such milestones are the NetCDF/CF data standard, the DataCite data DOIs enabling data citation, the data preservation and stewardship standards of the World Data System (WDS), or the IPCC FAIR Guidelines for the current Sixth IPCC Assessment Report (AR6). The FAIR Guidelines significantly changed the role of the DDC Partner DKRZ from an independent partner for long-term data preservation into an active partner involved in IPCC’s Sixth Assessment cycle. As a result, the DDC has become more exposed and visible, posing a challenge and an opportunity to operationalize IPCC’s FAIR Guidelines and long-term preservation approaches. Combined with a long-term DDC strategy, the DDC can be positioned in the developing FAIR digital object research landscape as well as in the political and societal realm of climate change. [ABSTRACT FROM AUTHOR]

We propose a dynamic automated infrastructure model for the cloud data centre which is aimed as an efficient service stipulation for the enormous number of users. The data center and cloud computing technologies have been at the moment rendering attention to major research and development efforts by companies, governments, and academic and other research institutions. In that, the difficult task is to facilitate the infrastructure to construct the information available to application-driven services and make business-smart decisions. On the other hand, the challenges that remain are the provision of dynamic infrastructure for applications and information anywhere. Further, developing technologies to handle private cloud computing infrastructure and operations in a completely automated and secure way has been critical. As a result, the focus of this article is on service and infrastructure life cycle management. We also show how cloud users interact with the cloud, how they request services from the cloud, how they select cloud strategies to deliver the desired service, and how they analyze their cloud consumption. [ABSTRACT FROM AUTHOR]

Copyright of Informationswissenschaft: Theorie, Methode & Praxis / Sciences de l’information: Théorie, Méthode & Pratique is the property of University Library of Bern and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper we propose SCDP, a general-purpose data transport protocol for data centres that, in contrast to all other protocols proposed to date, supports efficient one-to-many and many-to-one communication, which is extremely common in modern data centres. SCDP does so without compromising on efficiency for short and long unicast flows. SCDP achieves this by integrating RaptorQ codes with receiver-driven data transport, packet trimming and Multi-Level Feedback Queuing (MLFQ); (1) RaptorQ codes enable efficient one-to-many and many-to-one data transport; (2) on top of RaptorQ codes, receiver-driven flow control, in combination with in-network packet trimming, enable efficient usage of network resources as well as multi-path transport and packet spraying for all transport modes. Incast and Outcast are eliminated; (3) the systematic nature of RaptorQ codes, in combination with MLFQ, enable fast, decoding-free completion of short flows. We extensively evaluate SCDP in a wide range of simulated scenarios with realistic data centre workloads. For one-to-many and many-to-one transport sessions, SCDP performs significantly better compared to NDP and PIAS. For short and long unicast flows, SCDP performs equally well or better compared to NDP and PIAS. [ABSTRACT FROM AUTHOR]

The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community. [ABSTRACT FROM AUTHOR]

Cloud computing model offers various platforms services and provides a scalable, on-demand service at any time-anywhere manner. However, in the outsourcing strategy, users no longer have direct control and ownership of data, which causes serious major concerns related to proper secure auditing. Previously, many schemes have been drafted to admit proper public verification through third-party authorities. On the other hand, these schemes may leak identity or data value to third party auditor (TPA) by making improbable expectations in a manner that the TPA has the adequate computation capability and can only handle static archive data blocks. So an efficient, secure, and dynamic auditing model is craved to prompt users that their sensitive data are accurately stored in the cloud data center. To accommodate this, we introduce a secure novel public auditing scheme named Dynamic Large Branching Hash Tree (DLBHT) with Homomorphic Verifiable Authenticator (HVA) based aggregate signature scheme, which performs auditing with less communication and computational overhead. This model supports batch auditing and batch dynamic updating operations by maintaining a provably secure dynamic large branching tree (DLBHT) at the cloud server-side. The security and performance analysis of our model shows that it is efficient and secure for practical arrangements, which specifically diminishes communication and computational cost of auditing procedure. [ABSTRACT FROM AUTHOR]

In recent years, there has been a great increase in usage of cloud data centers which leads the energy consumption growth by about 10% a year continuously. Further, due to the increase in temperature of cloud data center, the hardware failure rate increases and maintenance cost is also increased. Hotspots and an irregular temperature distribution are the major research issues. Therefore, accurate and reliable thermal management of the data center is a challenging task. In order to select appropriate and efficient thermal management approach, this survey presents a state of art review on the development in this field, discusses the classification of thermal management approaches and thermal mapping models. This review also reveals the thermal management and heat management strategies at data center level to make the data centre more energy efficient. Further, it discusses various thermal aware task scheduling strategies. In thermal management, computing equipments are scheduled with the objective to minimize the hotspot and cooling cost. Finally, evaluation metrics for measuring thermal efficiency and open research issues in this field are provided. Both researchers and academicians find this review useful since it presents the significant research in the field of thermal management. [ABSTRACT FROM AUTHOR]

How do you help people improve their data management skills? For our team at the University of Illinois at Urbana-Champaign, we decided the answer was "one nudge at a time". A study conducted by Wiley and Mischo (2016) found that Illinois researchers are aware of data services available but under-utilize them. Many researchers do not consider data management as a concern distinct from researching and producing scholarly work products. In 2017, the RDS piloted the Data Nudge -- a monthly, opt-in email service to "nudge" Illinois researchers toward good data management practices, and towards utilizing data services on campus. The aim of the Data Nudge was to address the gap between knowing about a service and using it by highlighting best practices and campus resources. The topics covered in the Data Nudge center around data. Some topics are applicable to everyone, such as data back-up, documentation, and file naming conventions. Other topics are specific to Illinois, like storage options, events, and conferences. After four years, the Data Nudge has accumulated over 400 subscribers through word-of-mouth, marketing channels on campus and inclusion in subject liaisons' instructional workshops. It receives stable open rates averaging at 52% (compared to 19.44% average industry rate for Higher Education*) and many compliments from subscribers. We expect the Data Nudge to continue supplementing workshops and training as an effective means of communication to reach researchers on our campus. In the spirit of re-use, we are in the process of archiving the Data Nudge topics in a reusable format, readily adaptable by other institutions. [ABSTRACT FROM AUTHOR]

• The free cooling potential of data centers is assessed for Brazilian conditions. • A graphical methodology is presented for all economizer modes based on heat exchanger approach. • Free cooling hours are identified on a psychrometric chart. • Free cooling potential for 14 Brazilian cities are assessed. Data centers cooling loads may reach about 40% of their electricity demands. The number of that type of facility is continuously growing around worldwide and actions addressing energy saving are welcome to rationalize their operation. Free cooling appears as an effective solution to achieve energy savings, and the present work proposes a methodology for assessing data center free cooling operation based on the heat exchanger approach point. The methodology can be applied to both airside and waterside economizer modes. The proposed methodology is applied to fourteen Brazilian state capitals that represent all country climatic conditions. Free cooling environmental air temperature and humidity criteria are based on literature review and on ASHRAE thermal guidelines. Results show that cities such as Curitiba, São Paulo, Porto Alegre and Brasília can potentially operate in both airside and waterside economizer modes for more than 3000 h per year, even considering conservative data center thermal operating limits. Facilities placed on North and Northeast regions can only run under free cooling conditions if operational parameters are to be relaxed to consider more flexible operating limits. It is shown that indirect economizers are potentially more suitable to operate under ASHRAE recommended limits. The proposed methodology is shaped for evaluating free cooling hours during system conception. [ABSTRACT FROM AUTHOR]

Recently scientists, politicians, students, associations and actors are sounded the alarm to save our planet. The slogan of Greta Thunberg "Our house is on fire" urges any person to act on the climate. As researchers in the field of databases, one of the most active research communities, we are compelled to propose little and big steps to save our planet. It should be noticed that DBMSs are one of the main energy consumers, as responsible to store and efficiently process data. In data stores, research on energy consumption has been mainly focused on some specific types of stores: data centers, database clusters, known as big infrastructures. These stores are computer warehouses dedicated to store and process in a parallel manner a large amount of data. They include different servers and network infrastructures. Energy consumption in traditional DBMSs got less attention compared to data centers, and at the same time, they are widely used in the actual applications. In DB-Engine (https://db-engines.com/en/ranking) ranking DBMSs according to their popularity, traditional DBMSs (Oracle, MySQL, SQL Server, PostgreSQL, DB2) are the top 5 of the most popular systems. This motivates us to integrate energy consumption in the components of these DBMSs. Query optimizers are one of the energy consumer's components. The actual studies were focused on integrating energy in query optimization in the mono-core processor architecture. Recently, thanks to multi-core, these studies have to be revisited. In this paper, we propose a new approach to integrate the energy dimension into query optimizers in the multi-core processor architecture. Firstly, we present a rich state of the art on energy consumption in the context of traditional databases. Secondly, a crossing from sequential query processing mode to parallel mode is given. Thirdly, we propose a cost model capturing energy in a multicore architecture. Its parameter values are obtained by using non-linear regression and neural network techniques. Finally, our cost model is integrated into the query optimizer in PostgreSQL on which several experiments were conducted showing the efficiency and effectiveness of our proposal. [ABSTRACT FROM AUTHOR]

This article aims to identify the reality of cloud computing in Data Center of Saudi Airlines, and to understand the concept of cloud computing, its inception, characteristics, roles, advantages, disadvantages, types, benefits and challenges when used, as well as to identify its role and its advantages in the performance of employees, and then to identify the problems and difficulties faced by employees of Data Center in Saudia Airlines, from their own point of view. The researchers used the descriptive analytical approach. One online questionnaire has been applied to the current research community, including all the staff in Data centre at Information Technology Sector in Saudi Arabia - Jeddah. The study community consists of 85 employees of the Data Center at the Saudia Airlines. The most important findings of the study show that the cloud computing helps to reduce the burden of routine functional as it allows employees to work effectively, assists in the speed of access required data from anywhere, at any time and at a tremendous speed. It also reduces the costs of periodic maintenance and reduce the operational burdens of legacy systems. One of the most important difficulties faced by cloud computing; it needs strong network architecture, and that users are worried about the privacy of the data and heightened security concerns when used. [ABSTRACT FROM AUTHOR]

The tremendous growth of computational clouds has attracted and enabled intensive computation on resource-constrained client devices. Predominantly, smart mobiles are enabled to deploy data and computational intensive applications by leveraging on the demand service model of remote data centres. However, outsourcing personal and confidential data to the remote data servers is challenging for the reason of new issues involved in data privacy and security. Therefore, the traditional advanced encryption standard (AES) algorithm needs to be enhanced in order to cope with the emerging security threats in the cloud environment. This research presents a framework with key features including enhanced security and owner's data privacy. It modifies the 128 AES algorithm to increase the speed of the encryption process, 1000 blocks per second, by the double round key feature. However, traditionally, there is a single round key with 800 blocks per second. The proposed algorithm involves less power consumption, better load balancing, and enhanced trust and resource management on the network. The proposed framework includes deployment of AES with 16, 32, 64, and 128 plain text bytes. Simulation results are visualized in a way that depicts suitability of the algorithm while achieving particular quality attributes. Results show that the proposed framework minimizes energy consumption by 14.43%, network usage by 11.53%, and delay by 15.67%. Hence, the proposed framework enhances security, minimizes resource utilization, and reduces delay while deploying services of computational clouds. [ABSTRACT FROM AUTHOR]

Airflow management plays an important role in ensuring secured and energy-efficient operation of data centres (DCs). Higher power density in DC is a risk resulting in serious deterioration of airflow distribution accompanied by the increasing cooling energy demand. In this study, the airflow optimization effects of rack baffles and server lower-side terminal baffles on the thermal environment are first validated by experiments. Then, the optimization mechanisms of the two baffle systems are analysed by numerical simulations. The results show that the optimization mechanisms of these two systems are different and the combination can contribute to better airflow distribution than separate baffles. The analysis is validated by altogether 12 cases in 3 scenarios where different combinations of the two systems were included. The results validated the combination of 8 cm × 45° server lower-side terminal baffles and 20 cm × 75° rack baffles can further improve the airflow distribution and relieve heat accumulation in the DC. The impacts of combined baffle system on the thermal environment in DC are analysed by experiments and simulations. The combined system superposes the airflow optimization functions of the two systems. The maximum temperature drop of the hotspot is 0.7 °C, which is 0.4 °C lower than that when only optimal rack baffles or server lower-side terminal baffles are installed. [ABSTRACT FROM AUTHOR]

Cloud Computing has emerged as a new paradigm that permits to deliver Information Technology resources on demand and outsource software and hardware infrastructure. To support dynamic load, the system should select the best location for serving the request to achieve best performance. Selection of data center plays an important role to improve the performance as well as to reduce the cost. Service broker controls the traffic routing between user bases and data centers based on different service broker policies. In this paper we propose a new service broker algorithm which combines the cost and efficiency for optimized the cost and response time. [ABSTRACT FROM AUTHOR]

As data centers proliferate, their energy intensity deserves close attention. Always-on operations and growing usage for cloud and other backend processes make servers the fundamental driver of data center energy use. Yet servers' power draw under real-world conditions is poorly understood. This paper explores characteristics of volume servers that affect energy use, quantifying differences in power draw between higher-performing Standard Performance Evaluation Corporation (SPEC) and ENERGY STAR servers and that of a typical server. First, we establish general characteristics of the US installed base, before reporting hardware configurations from a major online retail website. We then compare idle power across three datasets (one unique to this paper) and explain their differences via the hardware characteristics to which power draw is most sensitive. We find idle server power demand to be significantly higher than benchmarks from ENERGY STAR and the industry-released SPEC database, and SPEC server configurations—and likely their power scaling—to be atypical of volume servers. Next, we examine power draw trends among high-performing servers across their load range to consider whether these trends are representative of volume servers, before inputting average idle power load values into a recent national server energy use model. Lastly, results from two surveys of IT professionals illustrate the incidence of more efficient equipment and operational practices in server rooms/closets. Future work should include server power field measurements in data centers of different sizes, accounting for variations in configurations and setting changes post-purchase, as well as investigating the linkage between time and server energy efficiency. [ABSTRACT FROM AUTHOR]

The characteristics of cloud computing, such as large-scale, dynamics, heterogeneity and diversity, present a range of challenges for the study on modeling and performance evaluation on cloud data centers. Performance evaluation not only finds out an appropriate trade-off between cost-benefit and quality of service (QoS) based on service level agreement (SLA), but also investigates the in uence of virtualization technology. In this paper, we propose an Energy-Aware Optimization (EAO) algorithm with considering energy consumption, resource diversity and virtual machine migration. In addition, we construct a stochastic model for Energy- Aware Migration-Enabled Cloud (EAMEC) data centers by introducing Dynamic Scalable Stochastic Petri Net (DSSPN). Several performance parameters are defined to evaluate task backlogs, throughput, reject rate, utilization, and energy consumption under different runtime and machines. Finally, we use a tool called SPNP to simulate analytical solutions of these parameters. The analysis results show that DSSPN is applicable to model and evaluate complex cloud systems, and can help to optimize the performance of EAMEC data centers. [ABSTRACT FROM AUTHOR]

This article presents a design framework aiming to reduce mass data storage cost in data centers. Its underlying principle is simple: Assume one may noticeably reduce the HDD manufacturing cost by significantly (i.e., at least several orders of magnitude) relaxing raw HDD reliability, which ensures the eventual data storage integrity via low-cost system-level redundancy. This is called system-assisted HDD bit cost reduction. To better utilize both capacity and random IOPS of HDDs, it is desirable to mix data with complementary requirements on capacity and random IOPS in each HDD. Nevertheless, different capacity and random IOPS requirements may demand different raw HDD reliability vs. bit cost trade-offs and hence different forms of system-assisted bit cost reduction. This article presents a software-centric design framework to realize dataadaptive system-assisted bit cost reduction for data center HDDs. Implementation is solely handled by the filesystem and demands only minor change of the error correction coding (ECC) module inside HDDs. Hence, it is completely transparent to all the other components in the software stack (e.g., applications, OS kernel, and drivers) and keeps fundamental HDD design practice (e.g., firmware, media, head, and servo) intact. We carried out analysis and experiments to evaluate its implementation feasibility and effectiveness. We integrated the design techniques into ext4 to further quantitatively measure its impact on system speed performance. [ABSTRACT FROM AUTHOR]

Containerization has been used in many applications for isolation purposes due to its lightweight, scalable, and highly portable properties. However, to apply containerization in large-scale Internet data centers faces a big challenge. Services in data centers are always instantiated as a group of containers, which often generate heavy communication workloads and therefore resulting in inefficient communications and downgraded service performance. Although assigning the containers of the same service to the same server can reduce the communication overhead, this may cause heavily imbalanced resource utilization since containers of the same service are usually intensive to the same resource. To reduce communication cost as well as balance the resource utilization in large-scale data centers, we further explore the container distribution issues in a real industrial environment and find that such conflict lies in two phases—container placement and container reassignment. The objective of this paper is to address the container distribution problem in these two phases. For the container placement problem, we propose an efficient communication aware worst fit decreasing algorithm to place a set of new containers into data centers. For the container reassignment problem, we propose a two-stage algorithm called Sweep&Search to optimize a given initial distribution of containers by migrating containers among servers. We implement the proposed algorithms in Baidu’s data centers and conduct extensive evaluations. Compared with the state-of-the-art strategies, the evaluation results show that our algorithms perform better up to 70% and increase the overall service throughput up to 90% simultaneously. [ABSTRACT FROM AUTHOR]

Various topologies have been proposed for high-performance computing (HPC), i.e., fat-tree, Torus topology. Compared with conventional fat-tree topology, Torus performs much better when applied in HPC. Unfortunately, due to its wraparound links, Torus topology naturally has the tendency to trigger deadlock incidents inside the network. Researchers solve this problem by means of virtual channel, but this approach will also restrict the routing of message. In this paper, we propose a deadlock-free topology for HPC, called Mesh-of-Torus, which incarnates the good characteristics of Mesh and Torus topology. Comparing with mesh, Mesh-of-Torus has shorter network diameter. Furthermore, we have proposed a corresponding port assignment rules in consideration of complicated internal arbitration or scheduling mechanism incurred by the employment of virtual channel. Deadlock avoidance can be achieved when dimension-order routing algorithm and our port assignment rules are applied to Mesh-of-Torus. Finally, simulations and mathematical analysis have shown that Mesh-of-Torus outperforms Mesh in terms of average end-to-end latency and network load distribution. [ABSTRACT FROM AUTHOR]