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Journal articles on the topic 'Storage Area Network (SAN)'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 June 2025

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1

XHAFERRA, Edmira, Kristel BOZHIQI, and Charles RAPOZO. "FOCUS “SECURITY” OF STORAGE AREA NETWORK (SAN)." Interdisciplinary Journal of Research and Development 5, no. 2 (2018): 17. http://dx.doi.org/10.56345/ijrdv5n202.

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The Storage Area Network (SAN) is a space-saving storage technology to manage data securely. The amount of data that needs to be stored is growing, and this is due to the growing number of users of Information Technology all over the world. In this paper we will see what this technology is, the basic components that are involved in its construction, the protocols that are being used, and will address security issues in SAN.SAN, provides storage space management and maintains fast data up. Security has been and remains the top priority for any campaign that works with sensitive information and data, another element should be SANs. The vulnerability assessment is one of the critical requirements to make data storage a system safe. Knowledge about security elements and solutions can help data storage administrators to increase the security level and reliability of networks.

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Sarmad, K. Ibrahim, and A. Abdulhussien Saif. "Improved storage area network method for backup approach." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 3 (2020): 1493–98. https://doi.org/10.11591/ijeecs.v17.i3.pp1493-1498.

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Storage Area Network (SAN) could be a dedicated storage network which carries the info between servers and storage devices. SAN offers flexibility to implement and share data between servers and remote devices instead of the traditional method that reduced the performance of service. This paper improves SAN backup method to increase the CPU utilization and reduce the load on the link; the result shows that the improved method is better than of traditional in terms of response time in FTP about 36% while the traffic data is about 3%.

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Kulkarni, Varsha, and Dr Nagaraj Bhat. "A study of Storage Area Networks and issues in its management." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 238–45. http://dx.doi.org/10.51201/jusst/21/05257.

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A data center has hundreds of servers and storage devices running on virtual machines that can be deployed and migrated over servers as per the requirement. If each server uses local storage, migration of this storage and restoration is mandatory. An attempt to organize and track storage throughout the data center is quite tedious. Using a dedicated storage system like a storage array, it possible to collectively monitor and manage such a network. A storage area network is essentially a network dedicated to storage devices. A storage area network can interconnect devices in all its layers, therefore improving storage availability. Interconnecting all elements in SAN also reduces the chances of a single point of failure. Using the storage devices collectively improves their utilization. SAN offers to manage and maintain all devices in the network. Although SAN is beneficial, it has drawbacks when configuring, monitoring, and managing components in a large-scale network. This paper consolidates the problems associated with SAN and offers possible solutions to overcome them.

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K. Ibrahim, Sarmad, and Saif A. Abdulhussien. "Improved storage area network method for backup approach." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (2020): 1493. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1493-1498.

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<span>Storage Area Network (SAN) could be a dedicated storage network which carries the info between servers and storage devices. SAN offers flexibility to implement and share data between servers and remote devices instead of the traditional method that reduced the performance of service. This paper improves SAN backup method to increase the CPU utilization and reduce the load on the link; the result shows that the improved method is better than of traditional in terms of response time in FTP about 36% while the traffic data is about 3%.</span>

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AL-aswad, Muthanna Mohammad, and KHALIL ALWAJEH. "Performance Evaluation of Storage Area Network(SAN)With internet Small Computer System Interface (iSCSI)For Local System PC." Algerian Journal of Signals and Systems 5, no. 3 (2020): 167–78. http://dx.doi.org/10.51485/ajss.v5i3.113.

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SCSI is a newly emerging protocol with the goal of implementing the Storage Area Network (SAN) technology over TCP/IP,where enables to access to remote data that in attached storage disks storages - Direct Attached Storage(DAS) over IP-networked. Also it's brings economy and convenience whereas it also raises performance and reliability issues. This paper investigates about possibility , using storage technology of the SANs, and iSCSI-SAN protocol,in local system PC, to improve access to attached storage disks storages in local system, with using iSCSI-SANs as virtual storage, is rather than DAS storage in local system of PC. Explicates after experiment procedure is that improving throughput of iSCSI-SANs was better than attached storage disks storages - DAS in local system . This means is that it can use iSCSI-SANs in local system of PC as attache storage disks storages as DAS , without cost , high performance, and easy control.

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Talluri Durvasulu, Mohan Babu. "Security in Storage Area Networks: A Technical Overview." International Journal of Advances in Engineering and Management 7, no. 2 (2025): 368–76. https://doi.org/10.35629/5252-0702368376.

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Storage Area Network (SAN) security has emerged as a critical component of modern enterprise infrastructure, addressing the challenges posed by exponential data growth and evolving cyber threats. This technical article examines the fundamental components and implementation strategies for securing SAN environments, encompassing access control management, data protection mechanisms, network security infrastructure, physical security controls, compliance frameworks, and best practices. The comprehensive article covers advanced technologies including artificial intelligence-driven monitoring systems, quantumresistant encryption protocols, and automated incident response capabilities, while highlighting the importance of maintaining a balance between security measures and operational efficiency. Through the integration of multiple security layers and automated controls, organizations can establish robust defense mechanisms against emerging threats while ensuring optimal performance of their storage infrastructure.

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Putra, Budiya Surya, and Timothy John Pattiasina. "STUDI ANALISA DATA STORAGE DENGAN MENGGUNAKAN SISTEM NAS-DAS-SAN." Teknika 1, no. 1 (2012): 47–54. http://dx.doi.org/10.34148/teknika.v1i1.6.

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Teknologi storage (penyimpanan) berkembang cukup pesat. Sampai saat ini, begitu banyak teknologi storage yang tersedia, dengan segala macam keunggulan dan keterbatasan. Di antara teknologi storage yang telah dikenal adalah DAS (Direct Attached Storage), NAS (Network Attached Storage), dan SAN (Storage Area Network). Tujuan penelitian makalah ini, untuk memahami cara kerja data data storage dan memahami kelebihan menggunakan sistem NAS, DAS, SAN. Hasil penelitian membuktikan bahwa terdapat aspek-aspek tertentu yang harus dipertimbangkan dalam pemilihan penagaman data pada sistem penyimpanan data di dalam suatu jaringan komputer.

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Shevtsov, Vadim, and Evgeny Abramov. "The Analysis of Modern Data Storage Systems." NBI Technologies, no. 1 (August 2019): 25–30. http://dx.doi.org/10.15688/nbit.jvolsu.2019.1.5.

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Today, Storage Area Network and Cloud Storage are the common Storage System. Storage Area Network includes NAS, SAN, DAS systems. Cloud Storage includes object storage, file storage, block storage. Storage Area Network is an important technology because it may give a lot of data volume with a high recovery chance and secure access, work and central management with data. Cloud Storage has many advantages: data mobility, teamwork, stability, scalability, quick start. The main threats include destruction, theft, corruption, unauthentication, replacement, blocking. Storage Area Network components (architecture elements, protocols, interfaces, hardware, system software, exploitation) have a lot of vulnerabilities. Cloud Storage may be attacked by software, functional elements, clients, hypervisor, management systems. A lot of companies design storage solutions: DropBox, QNAP, WD, DELL, SEAGATE.

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Masich, Igor S., Vadim S. Tynchenko, Vladimir A. Nelyub, et al. "Prediction of Critical Filling of a Storage Area Network by Machine Learning Methods." Electronics 11, no. 24 (2022): 4150. http://dx.doi.org/10.3390/electronics11244150.

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The introduction of digital technologies into the activities of companies is based on software and hardware systems, which must function reliably and without interruption. The forecasting of the completion of storage area networks (SAN) is an essential tool for ensuring the smooth operation of such systems. The aim of this study is to develop a system of the modelling and simulation of the further loading of SAN on previously observed load measurements. The system is based on machine learning applied to the load prediction problem. Its novelty relates to the method used for forming input attributes to solve the machine learning problem. The proposed method is based on the aggregation of data on observed loading measurements and the formalization of the problem in the form of a regression analysis problem. The artificial dataset, synthesized stochastically according to the given parameter intervals and simulating SAN behavior, allowed for more extensive experimentation. The most effective algorithm is CatBoost (gradient boosting on decision trees), which surpasses other regression analysis algorithms in terms of R2 scores and MAE. The selection of the most significant features allows for the simplification of the prediction model with virtually no loss of accuracy, thereby reducing the number of confessions used. The experiments show that the proposed prediction model is adequate to the situation under consideration and allows for the prediction of the SAN load for the planning period under review with an R2 value greater than 0.9. The model has been validated on a series of real data on SAN.

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Lv, Guixiang, Liudong Xing, Honggang Wang, and Hong Liu. "Load Redistribution-based Reliability Enhancement for Storage Area Networks." International Journal of Mathematical, Engineering and Management Sciences 8, no. 1 (2023): 1–14. http://dx.doi.org/10.33889/ijmems.2023.8.1.001.

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Storage area networks (SANs) are one of the prevalent reliable data storage solutions. However, cascading failures triggered by data overloading have become a major threat to SANs, preventing the desired quality of service from being delivered to users. Based on our preliminary works on studying the impacts of data loading on the reliability performance of SANs, this paper advances the state of the art by implementing node degree-based load redistribution strategies to enhance the SAN reliability, thus mitigating or even preventing the occurrence of cascading failures during the mission time. Load-based and reliability-based node selection rules are considered, which choose nodes with the highest load level and the lowest reliability for load redistribution, respectively. The relationship between data loading and reliability of an individual SAN component is modeled using the accelerated failure-time model with the power law. The SAN reliability is assessed using a combinatorial decision diagram-based approach. The application and effectiveness of the proposed load redistribution strategies are demonstrated and compared through a case study of an SAN with the mesh topology.

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Shen, Jin Xing. "The Design and Implementation of Intelligent High-Speed Disk Storage Protocols." Applied Mechanics and Materials 513-517 (February 2014): 889–92. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.889.

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In this paper, through the analysis of the redundant array of independent disks system (RAID), storage area network system (SAN), network storage system (NAS), based on RAID on the system FPGA is used to design a set of intelligent high speed disk storage protocol, through the test and analysis can meet the continuous data acquisition, the real-time data storage needs, in the software through providing high access speed increases memory buffer, large storage capacity and higher data security.

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Lv, Guixiang, and Liudong Xing. "Influence of Load on Reliability of Storage Area Networks." International Journal of Mathematical, Engineering and Management Sciences 6, no. 6 (2021): 1533–52. http://dx.doi.org/10.33889/ijmems.2021.6.6.091.

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During the coronavirus pandemic, telecommuting is widely required, making remote data access grow significantly. This requires highly reliable data storage solutions. Storage area networks (SANs) are one of such solutions. To guarantee that SANs can deliver the desired quality of service, cascading failures must be prevented, which occur when a single initial incident triggers a cascade of unexpected failures of other devices. One such incident is the data loading/overloading, causing the malfunction of one device and further cascading failures. Thus, it is crucial to address influence of data loading on the SAN reliability modeling and analysis. In this work, we make contributions by modeling the effects of data loading on the reliability of an individual switch device in SANs though the proportional-hazards model and accelerated failure-time model. Effects of loading on the reliability of the entire SAN are further investigated through dynamic fault trees and binary decision diagrams-based analysis of a mesh SAN system.

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Hu, Yuan Yuan, Lu Wang, and Xiao Dong Zhang. "Cloud Storage Virtualization Technology and its Architecture." Applied Mechanics and Materials 713-715 (January 2015): 2435–39. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2435.

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Based on cloud computing and cloud storage, this paper discussed three kinds of typical virtualization technology in cloud computing, that is, storage virtualization based on host or server, storage virtualization based on storage devices, and storage virtualization based on Storage Area Network (SAN). In addition, application value of storage virtualization technology was discussed. What’s more, platform architecture model of cloud storage was proposed, and the characteristics of cloud backup were discussed as an example.

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Kim, Song-Kyoo. "Enhanced management method of storage area network (SAN) server with random remote backups." Mathematical and Computer Modelling 42, no. 9-10 (2005): 947–58. http://dx.doi.org/10.1016/j.mcm.2005.06.006.

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Lyu, Guixiang, Liudong Xing, and Guilin Zhao. "Static and Dynamic Load-Triggered Cascading Failure Mitigation for Storage Area Networks." International Journal of Mathematical, Engineering and Management Sciences 9, no. 4 (2024): 697–713. http://dx.doi.org/10.33889/ijmems.2024.9.4.036.

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Storage area networks (SANs) are a widely used and dependable solution for data storage. Nevertheless, the occurrence of cascading failures caused by overloading has emerged as a significant risk to the reliability of SANs, impeding the delivery of the desired quality of service to users. This paper makes contributions by proposing both static and dynamic load-triggered redistribution strategies to alleviate the cascading failure risk during the mission time. Two types of node selection rules, respectively based on the load level and node reliability, are studied and compared. Based on the SAN component reliability evaluation using the accelerated failure-time model under the power law, the SAN reliability is evaluated using binary decision diagrams. A detailed case study of a mesh SAN is conducted to compare the performance of different cascading failure mitigation schemes using criteria of SAN reliability improvement ratio and resulting SAN reliability after the mitigation.

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Ratnangi, Nirek. "Impact of Storage Solutions on Linux Pacemaker Cluster Performance in Critical Business Operations in cloud." Journal of Scientific and Engineering Research 10, no. 9 (2023): 254–66. https://doi.org/10.5281/zenodo.13918931.

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In modern cloud environments, ensuring high availability (HA) is crucial for maintaining the continuity of critical business operations. Linux Pacemaker, a leading HA cluster manager, plays a pivotal role in orchestrating redundancy and failover mechanisms for essential services. One often overlooked factor in achieving optimal performance of these clusters is the choice of storage solutions. This paper investigates the impact of various storage solutions, such as Network-Attached Storage (NAS), Storage Area Network (SAN), and cloud-native storage, on the performance of Linux Pacemaker clusters, focusing on critical business workloads in cloud environments. We conducted experiments simulating real-time business operations, evaluating key performance metrics including Input/Output Operations Per Second (IOPS), data throughput, latency, and failure recovery times. Our findings demonstrate that while SAN offers superior performance for IOPS-intensive workloads, cloud-native storage provides greater flexibility and scalability, albeit at a potential cost of increased latency. The results provide a detailed trade-off analysis for enterprises seeking the most suitable storage solution for their Linux Pacemaker clusters, depending on workload type and business requirements.

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Caleb, Derek Asir Muthurajan. "AI-Powered Fibre Channel Congestion Detection and Resolution: Transforming SAN Network Resilience Through Intelligent Automation." European Journal of Computer Science and Information Technology 13, no. 22 (2025): 114–25. https://doi.org/10.37745/ejcsit.2013/vol13n22114125.

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Fibre Channel Storage Area Networks (SANs) have long been plagued by congestion issues that degrade performance and disrupt critical business operations. This article explores the transformative role of artificial intelligence in revolutionizing congestion detection and resolution within FC networks. By leveraging advanced machine learning algorithms and neural network models, AI systems can now automatically correlate seemingly disparate network anomalies, accurately identify root causes of credit stalls, and implement autonomous remediation strategies without human intervention. The integration of AI-driven analytics enables a paradigm shift from reactive troubleshooting to proactive management, effectively eradicating persistent congestion through intelligent buffer credit management and dynamic path optimization. Organizations implementing these solutions experience significantly improved network resilience, enhanced application performance, and reduced operational overhead, positioning AI as an essential component in modern SAN infrastructure management.

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Derek Asir Muthurajan Caleb. "Automated fibre channel device onboarding using FIDO protocol: Transforming SAN infrastructure management." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2530–39. https://doi.org/10.30574/wjaets.2025.15.2.0657.

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The onboarding of Fibre Channel (FC) devices in Storage Area Network (SAN) environments presents significant operational challenges for data center administrators, involving multiple manual configuration steps that create bottlenecks in infrastructure expansion. This article explores how adapting the FIDO protocol—originally developed for IoT device authentication—can revolutionize FC device provisioning by enabling automated, secure onboarding processes. Through a comprehensive article of current onboarding workflows and their limitations, it presents a technical framework for implementing FIDO-based zero-touch provisioning that eliminates the need for manual IP assignment, firewall configuration, management system discovery, monitoring setup, and switch parameter synchronization. The proposed methodology substantially reduces deployment timeframes while maintaining security compliance and operational consistency across SAN infrastructures.

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Rao, Pramod Sathyanarayana. "Storage Technologies and Their Protocols: Building the Foundation of Modern Data Infrastructure." European Journal of Computer Science and Information Technology 13, no. 21 (2025): 88–108. https://doi.org/10.37745/ejcsit.2013/vol13n2188108.

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This article examines the evolving landscape of enterprise storage technologies and protocols that form the backbone of modern data infrastructure. With global data expected to grow to hundreds of zettabytes in the near future—a massive increase in just a decade—storage architecture has transformed from a back-office concern into a strategic business imperative. Organizations face not only exponential data growth but increasingly demanding performance requirements, with financial trading platforms now requiring response times measured in microseconds rather than milliseconds to maintain a competitive advantage. The article explores two fundamental storage architectures—Network Attached Storage (NAS) and Storage Area Networks (SAN)—and analyzes their distinct methodologies, use cases, and implementation considerations. It delves into the protocols that enable communication between servers and storage devices, including SCSI, Fibre Channel Protocol, iSCSI, NFS, and SMB/CIFS, highlighting how each addresses specific requirements for reliability, performance, and compatibility. The article further investigates multipathing as a critical high-availability technique that minimizes single points of failure through redundant physical connections, providing both enhanced reliability and performance benefits. Finally, it explores emerging technologies reshaping the storage landscape, including NVMe, NVMe over Fabrics, and object storage, which are driving significant shifts in how organizations architect their data infrastructure to meet future demands across hybrid environments.

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Basir, M. A., and M. H. Yousaf. "TRANSPARENT COMPRESSION SCHEME FOR LINUX FILE SYSTEM." Nucleus 49, no. 2 (2012): 129–37. https://doi.org/10.71330/thenucleus.2012.806.

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Data footprint in data centers is growing day by day. Servers being used in research institutions, commercial web hosting companies and other enterprise server environment are now dealing with more and more bytes. Most of these servers have Linux operating systems and Ext2/3 comes as default file system in latest Linux kernel. A high-end compression system could be introduced in Ext2/3. It will be helpful in reducing data footprint in Linux based server environment. With slight modifications and using the sparse block feature of Ext2/3 file system transparent compression could be achieved. The reduced data footprint will result in reduced power consumption due to lesser disk reads and writes, and of course less data traffic on NAS(Network Attached Storage) and SAN(Storage Area Network). The reduced hardware might result in less management cost and of course less space for hardware.

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Sergeev, Sergey, Sergey Barykin, Olga Kalinina, Elena Naumova, Natalia Dedyukhina, and Tatiana Dmitrieva. "Solving the problem of energy efficiency of remote agricultural farms." E3S Web of Conferences 244 (2021): 03010. http://dx.doi.org/10.1051/e3sconf/202124403010.

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The research focuses on the viability of a mathematical model of energy efficiency of agricultural sector for the case of Russia. The article describes the methodology and results of research aimed at improving energy efficiency in the activities of remote farms of the agro-industrial complex. The emphasis is placed on the application of a scientific approach based on mathematical modeling of work on a limited resource. For this purpose, the most common solutions are SAN (Storage Area Network), while the programs that are common for enterprises of the network are located on cloud servers. The results of the research were applied in a joint project with the Catalan Polytechnic University (Polytechnic University of Catalonia).

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PETER BEN, OKPE, and MUHAMMAD SANUSI. "SECURITY EVALUATION AND PERFORMANCE ASSESSMENT OF STORAGE AREA NETWORK (SAN) - A CASE STUDY OF NATIONAL ASSEMBLY (NASS), ABUJA - NIGERIA." International Journal of Engineering Applied Sciences and Technology 04, no. 09 (2020): 471–84. http://dx.doi.org/10.33564/ijeast.2020.v04i09.064.

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Arzymatov, Kenenbek, Andrey Sapronov, Vladislav Belavin, et al. "SANgo: a storage infrastructure simulator with reinforcement learning support." PeerJ Computer Science 6 (May 4, 2020): e271. http://dx.doi.org/10.7717/peerj-cs.271.

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We introduce SANgo (Storage Area Network in the Go language)—a Go-based package for simulating the behavior of modern storage infrastructure. The software is based on the discrete-event modeling paradigm and captures the structure and dynamics of high-level storage system building blocks. The flexible structure of the package allows us to create a model of a real storage system with a configurable number of components. The granularity of the simulated system can be defined depending on the replicated patterns of actual system behavior. Accurate replication enables us to reach the primary goal of our simulator—to explore the stability boundaries of real storage systems. To meet this goal, SANgo offers a variety of interfaces for easy monitoring and tuning of the simulated model. These interfaces allow us to track the number of metrics of such components as storage controllers, network connections, and hard-drives. Other interfaces allow altering the parameter values of the simulated system effectively in real-time, thus providing the possibility for training a realistic digital twin using, for example, the reinforcement learning (RL) approach. One can train an RL model to reduce discrepancies between simulated and real SAN data. The external control algorithm can adjust the simulator parameters to make the difference as small as possible. SANgo supports the standard OpenAI gym interface; thus, the software can serve as a benchmark for comparison of different learning algorithms.

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Bodaniuk, M. E., O. K. Karnaukhov, O. I. Rolik, and S. F. Telenyk. "Storage area network management." Electronics and Communications 18, no. 5 (2013): 81–90. http://dx.doi.org/10.20535/2312-1807.2013.18.5.142749.

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Nalini, S., Harini Vaikund, and A. R. Raja. "Fuzzy Controller Based Power Quality Improvement for a Microgrid with Energy Management System." Journal of Computational and Theoretical Nanoscience 17, no. 11 (2020): 4957–64. http://dx.doi.org/10.1166/jctn.2020.8994.

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There is a drastic decrease in availability of conventional energy sources such as coal, natural gas and oil. The need for using non-conventional energy sources has increased due to the fast rate depletion in conventional energy sources and the ill effects of using them. Research work in the area of renewable energy sources has hence been increased throughout the world. The rate at which power demands are increasing, requires the need for interconnections of various power generation methods. For the proper use of conventional and non-conventional distributed generation microgrid is essential. Microgrid combines one or more generation units and helps to use the power generated in efficient manner. Microgrid includes the generating units, battery storage system with power electronic converters. The use of power converter is important, but the presence of converter generates harmonics usually so that the power quality issues have become serious. Many controllers are used to solve power quality problems, where one of them is the Unified Power Quality Controller (UPQC). Problems linked to power quality become serious due to the use of power electronics appliances. Microgrid power quality is maintained by using UPQC compensating devices. To control UPQC, there must be some controller to intimate the voltage sag, swell and flickers. In this paper the fuzzy logic controller is used for UPQC. An energy management system (EMS) is a system used by electrical utility network operators to monitor, regulate and optimize transmission and generation system efficiency. The system model is created using MATLAB by merging all DC-microgrid, EMS and UPQC, to solve power quality issues.

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Minor, David, Don Sutton, Ardys Kozbial, Brad Westbrook, Michael Burek, and Michael Smorul. "Chronopolis Digital Preservation Network." International Journal of Digital Curation 5, no. 1 (2010): 119–33. http://dx.doi.org/10.2218/ijdc.v5i1.147.

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The Chronopolis Digital Preservation Initiative, one of the Library of Congress’ latest efforts to collect and preserve at-risk digital information, has completed its first year of service as a multi-member partnership to meet the archival needs of a wide range of domains.Chronopolis is a digital preservation data grid framework developed by the San Diego Supercomputer Center (SDSC) at UC San Diego, the UC San Diego Libraries (UCSDL), and their partners at the National Center for Atmospheric Research (NCAR) in Colorado and the University of Maryland's Institute for Advanced Computer Studies (UMIACS).Chronopolis addresses a critical problem by providing a comprehensive model for the cyberinfrastructure of collection management, in which preserved intellectual capital is easily accessible, and research results, education material, and new knowledge can be incorporated smoothly over the long term. Integrating digital library, data grid, and persistent archive technologies, Chronopolis has created trusted environments that span academic institutions and research projects, with the goal of long-term digital preservation.A key goal of the Chronopolis project is to provide cross-domain collection sharing for long-term preservation. Using existing high-speed educational and research networks and mass-scale storage infrastructure investments, the partnership is leveraging the data storage capabilities at SDSC, NCAR, and UMIACS to provide a preservation data grid that emphasizes heterogeneous and highly redundant data storage systems.In this paper we will explore the major themes within Chronopolis, including:a) The philosophy and theory behind a nationally federated data grid for preservation. b) The core tools and technologies used in Chronopolis. c) The metadata schema that is being developed within Chronopolis for all of the data elements. d) Lessons learned from the first year of the project.e) Next steps in digital preservation using Chronopolis: how we plan to strengthen and broaden our network with enhanced services and new customers.

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Frey, Mark, Mike Perlmutter, Andrea Williams, and Dan Gluesenkamp. "The San Francisco Bay Area Early Detection Network." Management of Biological Invasions 6, no. 3 (2015): 231–41. http://dx.doi.org/10.3391/mbi.2015.6.3.02.

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Li, Zheng, Zhongyuan Yao, Xuhui Shen, Haiyan Tang, Lei Ba, and Yu Zhang. "Operation control technology of source network load storage area based on flexible application of energy storage." Journal of Physics: Conference Series 2591, no. 1 (2023): 012044. http://dx.doi.org/10.1088/1742-6596/2591/1/012044.

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Abstract This paper studies the operation control technology of source-network-load-storage area. Firstly, the flexible application mode of energy storage in the source-network-load-storage area is analyzed. Secondly, the definitions of absolute mileage and relative mileage are proposed, and the power coordination control model of energy storage units based on mileage balance is established to achieve the purpose of efficient utilization of energy storage resources. Finally, the operation control strategy of the source network load storage area is proposed, which verifies the effectiveness and practicability of the power coordination control model of the energy storage unit and the operation control strategy of the source network load storage area.

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Zemtsov, A. N., and Dung Tran. "MULTI-CRITERIA SELECTION OF STORAGE AREA NETWORK EQUIPMENT." Современные наукоемкие технологии (Modern High Technologies) 2, no. 6 2020 (2020): 241–47. http://dx.doi.org/10.17513/snt.38099.

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B H, Swathi. "A Survey on Security in Storage Area Network." International Journal for Research in Applied Science and Engineering Technology 8, no. 12 (2020): 604–10. http://dx.doi.org/10.22214/ijraset.2020.32556.

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Mercier, C. "No more blind SAN's bluff [storage area network]." Information Professional 4, no. 4 (2007): 41–42. http://dx.doi.org/10.1049/inp:20070411.

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Samuel, S. "Delivering the Promise of the Storage Area Network." IEEE Distributed Systems Online 5, no. 9 (2004): 4. http://dx.doi.org/10.1109/mdso.2004.22.

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M., Sneha. "Performance Analysis of RAIDs in Storage Area Network." International Journal of Computer Applications 126, no. 13 (2015): 26–31. http://dx.doi.org/10.5120/ijca2015906231.

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Su, Yu, Wan Lin Gao, Li Na Yu, Hui Hu, and Xuan Luo. "Wireless Body Area Network Data Storage Method Based on HBase." Applied Mechanics and Materials 427-429 (September 2013): 2273–77. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2273.

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With the further development of cloud computing and wireless body area network (WBAN), uploading the massive body signs parameters to the Internet for storage in real time has become possible. For the requirement of how to store the massive WBAN data, this thesis proposes the WBAN data storage method based on HBase, and elaborates how to design the storage architecture, the storage system and data query client by using HBase, a kind of tool in Hadoop platform. The WBAN data storage method based on HBase is important to efficiently manage the massive WBAN data.

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Zhou, Quan, Yongchang Lao, Yongliang Yin, and Qingyun Lin. "A new data-driven distributed power grid load storage resource-wide area voltage self-optimization control method for distribution system." Journal of Physics: Conference Series 2831, no. 1 (2024): 012016. http://dx.doi.org/10.1088/1742-6596/2831/1/012016.

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Abstract Different from the conventional energy distribution network, which solely relies on energy distribution, the novel distribution system gradually exhibits a complex and interconnected form of various resources, such as source network load storage. These resources possess characteristics of “multi-point, wide area, and small amount” distribution. Investigating the potential for wide-area voltage regulation by using distributed resources like renewable energy power generation, distributed energy storage, and flexible loads is crucial for constructing a highly integrated source network load storage in the new type of distribution system. This paper proposes a novel data-driven method for optimizing control of wide-area voltage through distributed power network load storage resources. By comprehensively and cooperatively controlling renewable energy power generation, energy storage systems, and flexible loads, this approach enhances the quality of wide-area voltage in the distribution network while ensuring optimal utilization of energy storage capacity and minimizing network losses. The effectiveness and advanced nature of this method are demonstrated by applying it to an urban distribution network example. Compared to traditional voltage regulation methods, this approach aligns better with the requirements for voltage regulation in the new type of distribution network.

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Ali, Moh, Yudi Prayudi, and Bambang Sugiantoro. "Storage Area Network Architecture to support the Flexibility of Digital Evidence Storage." International Journal of Computer Applications 182, no. 41 (2019): 30–35. http://dx.doi.org/10.5120/ijca2019918496.

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Yao, Jun, Ji-Wu Shu, and Wei-Min Zheng. "Distributed Storage Cluster Design for Remote Mirroring Based on Storage Area Network." Journal of Computer Science and Technology 22, no. 4 (2007): 521–26. http://dx.doi.org/10.1007/s11390-007-9075-x.

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Stergiou, E. C., and A. S. Kiremidjian. "Seismic performance of the San Francisco Bay Area transportation network bridges." Bridge Structures 1, no. 3 (2005): 319–26. http://dx.doi.org/10.1080/15732480500247728.

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Gresham-Lancaster, Scot. "Computer Music Network." Leonardo 47, no. 3 (2014): 266–67. http://dx.doi.org/10.1162/leon_a_00771.

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The social climate and cultural atmosphere of the San Francisco Bay Area in the late 70's early 80's plus the emergence of the nascent microcomputer industry made for a social network and approach that fostered the creation of a new type of collaborative electronic music ensemble with techniques that have come to be known as “Computer Music Network”. A transformation from initial heterogeneous to a more homogeneous underlying paradigm has brought with it aesthetic questions about the reason and evolution of this new genre.

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Paul Chinnaraju, Swaraj, G. Gunasekaran, N. Kumar, and R. Anandan. "Transformation from legacy storage to software defined storage–a review." International Journal of Engineering & Technology 7, no. 2.21 (2018): 306. http://dx.doi.org/10.14419/ijet.v7i2.21.12387.

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In IT Industry the new trend is everything to be in Software defined. And so the storage industry has started its transformation from Legacy storage like storage area network and network attached storage. This is enable through the software defined networks. Software defined networks helps organizations to accelerate its application deployment, thus reducing IT costs by applying some policy enabled workflows. In this paper, we will have a review over the need for it from the storage vendor perspective.

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Brothers, T., N. Mandagere, S. Muknahallipatna, J. C. Hamann, and H. Johnson. "Microsoft Exchange Implementation on a Distributed Storage Area Network." International Journal of Computers and Applications 30, no. 3 (2008): 251–64. http://dx.doi.org/10.1080/1206212x.2008.11441905.

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Telikepalli, R., T. Drwiega, and J. Yan. "Storage area network extension solutions and their performance assessment." IEEE Communications Magazine 42, no. 4 (2004): 56–63. http://dx.doi.org/10.1109/mcom.2004.1284930.

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Zeng, Zhiqiang, Ji Liang, Mingxiang Yang, Zhaocai Zeng, and Yu Lang. "A hydrodynamic model coupled multiple scenarios for plain river network and its application." MATEC Web of Conferences 246 (2018): 01016. http://dx.doi.org/10.1051/matecconf/201824601016.

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The plain river network is not only complex in shape but also contains many complex scenarios, such as interval inflow and outflow, hydraulic structures and water storage area, etc., which increases the difficulty of runoff simulation in plain river network. To address this problem, a hydrodynamic (HD) model coupled multiple scenarios that may occur in plain river network was proposed, which was used to simulate the runoff process of the plain river network. To illustrate the proposed model, Xi River was chosen as a study area. We designed some experiments for each scenario, and the experimental results show that simulation results have good consistency with the observation. It is worth noting that the simulation accuracy of the water level is always higher than the simulation accuracy of the flow. Moreover, the runoff simulation accuracy of runoff events with large runoff is relatively high. It turns out that HD model is suitable for runoff simulation of plain river network. In addition, we compared the flood diversion effects of water storage area and sluice, and the results show that the effect of water storage area is more obvious than sluice, and the flood diversion method combined with sluice and water storage area has better flood diversion effect. In conclusion, HD model is good at simulating floodplain storage effects, backwater and the change of water level and flow under the condition of engineering dispatching, which has important guiding significance for flood control in plain river network.

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PLANK, JAMES S., SCOTT ATCHLEY, YING DING, and MICAH BECK. "ALGORITHMS FOR HIGH PERFORMANCE, WIDE-AREA DISTRIBUTED FILE DOWNLOADS." Parallel Processing Letters 13, no. 02 (2003): 207–23. http://dx.doi.org/10.1142/s0129626403001240.

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As peer-to-peer and wide-area storage systems become in vogue, the issue of delivering content that is cached, partitioned and replicated in the wide area, with high performance, becomes of great importance. This paper explores three algorithms for such downloads. The storage model is based on the Network Storage Stack, which allows for flexible sharing and utilization of writable storage as a network resource. The algorithms assume that data is replicated in various storage depots in the wide area, and the data must be delivered to the client either as a downloaded file or as a stream to be consumed by an application, such as a media player. The algorithms are threaded and adaptive, attempting to get good performance from nearby replicas, while still utilizing the faraway replicas. After defining the algorithms, we explore their performance downloading a 50 MB file replicated on six storage depots in the U.S., Europe and Asia, to two clients in different parts of the U.S. One algorithm, called progress-driven redundancy, exhibits excellent performance characteristics for both file and streaming downloads.

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Singh, Garima, Anubhav Patrick, and Lucky Rajpoot. "A Clustering based Intrusion Detection System for Storage Area Network." International Journal of Computer Applications 88, no. 9 (2014): 14–18. http://dx.doi.org/10.5120/15380-3804.

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Niu Dejiao, Cai Tao, Song Lili, and Ju Shiguang. "Abnormal Access Request Detection based Secure Storage Area Network System." International Journal of Advancements in Computing Technology 3, no. 6 (2011): 337–44. http://dx.doi.org/10.4156/ijact.vol3.issue6.39.

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Si Yin, Yuanqiu Luo, Lei Zong, et al. "Storage area network extension over passive optical networks (S-PONS)." IEEE Communications Magazine 46, no. 1 (2008): 44–52. http://dx.doi.org/10.1109/mcom.2008.4427229.

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Xiangqun Qiu, R. Telikepalli, T. Drwiega, and J. Yan. "Reliability and availability assessment of storage area network extension solutions." IEEE Communications Magazine 43, no. 3 (2005): 80–85. http://dx.doi.org/10.1109/mcom.2005.1404599.

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Wang, Wilson Yong Hong, Heng Ngi Yeo, Yao Long Zhu, et al. "Design and development of Ethernet-based storage area network protocol." Computer Communications 29, no. 9 (2006): 1271–83. http://dx.doi.org/10.1016/j.comcom.2005.10.004.

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Fu, Xianglin, Changsheng Xie, Ruifang Liu, and Shenggang Pan. "The architecture and performance evaluation of iSCSI-based United Storage Network merging NAS and SAN." International Journal of Computer Applications in Technology 19, no. 2 (2004): 84. http://dx.doi.org/10.1504/ijcat.2004.003637.

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