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

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

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 (June 4, 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 (March 1, 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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3

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 (September 15, 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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4

Putra, Budiya Surya, and Timothy John Pattiasina. "STUDI ANALISA DATA STORAGE DENGAN MENGGUNAKAN SISTEM NAS-DAS-SAN." Teknika 1, no. 1 (July 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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5

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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6

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

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7

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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8

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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9

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 (January 30, 2020): 471–84. http://dx.doi.org/10.33564/ijeast.2020.v04i09.064.

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10

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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11

Arzymatov, Kenenbek, Andrey Sapronov, Vladislav Belavin, Leonid Gremyachikh, Maksim Karpov, Andrey Ustyuzhanin, Ivan Tchoub, and Artem Ikoev. "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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12

Elmirghani, J., and I. H. White. "Optical storage area networks." IEEE Communications Magazine 43, no. 3 (March 2005): 70. http://dx.doi.org/10.1109/mcom.2005.1404597.

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13

Sarkar, P., K. Voruganti, K. Meth, O. Biran, and J. Satran. "Internet Protocol storage area networks." IBM Systems Journal 42, no. 2 (2003): 218–31. http://dx.doi.org/10.1147/sj.422.0218.

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14

Yongdae Kim, F. Maino, M. Narasimha, Kyung Hyune Rhee, and G. Tsudik. "Storage area networking - Secure group key management for storage area networks." IEEE Communications Magazine 41, no. 8 (August 2003): 92–99. http://dx.doi.org/10.1109/mcom.2003.1222723.

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15

Seigle, Mitchell. "Storage Area Networks in Video Applications." SMPTE Journal 110, no. 4 (April 2001): 236–39. http://dx.doi.org/10.5594/j12432.

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16

Reitman, Greg. "Streaming Video with Storage Area Networks." SMPTE Journal 110, no. 8 (August 2001): 517–22. http://dx.doi.org/10.5594/j17762.

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17

Mahalingam, P., N. Jayaprakash, and S. Karthikeyan. "Storage Requirement Forecasting Analysis Model for Storage Area Networks." International Journal of Computer Applications 19, no. 6 (April 30, 2011): 13–17. http://dx.doi.org/10.5120/2367-3114.

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18

Aziz, Benjamin, Simon N. Foley, John Herbert, and Garret Swart. "Configuring storage-area networks using mandatory security." Journal of Computer Security 17, no. 2 (March 24, 2009): 191–210. http://dx.doi.org/10.3233/jcs-2009-0337.

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19

Burgener, Eric. "Setting the Stage for Storage Area Networks." EDPACS 27, no. 2 (August 1999): 17–18. http://dx.doi.org/10.1201/1079/43248.27.2.19990801/30265.7.

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20

Sadlier, George. "Storage Area Networks: An Information Security Perspective." Information Systems Security 12, no. 5 (November 2003): 29–41. http://dx.doi.org/10.1201/1086/43808.12.5.20031101/78488.4.

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21

Phillips, B. "Have storage area networks come of age?" Computer 31, no. 7 (July 1998): 10–12. http://dx.doi.org/10.1109/2.689672.

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22

He, Xubin, Ming Zhang, and Qing (Ken) Yang. "STICS: SCSI-to-IP cache for storage area networks." Journal of Parallel and Distributed Computing 64, no. 9 (September 2004): 1069–85. http://dx.doi.org/10.1016/j.jpdc.2004.05.005.

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23

Wilkinson, T. D., B. Crossland, N. Collings, Fan Zhang, and M. Fan. "Reconfigurable free-space optical cores for storage area networks." IEEE Communications Magazine 43, no. 3 (March 2005): 93–99. http://dx.doi.org/10.1109/mcom.2005.1404601.

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24

Narasimhamurthy, S. B., P. C. Gurumohan, S. Sreenivasamurthy, and J. Y. Hui. "Quanta data storage: an information processing and transportation architecture for storage area networks." IEEE Journal on Selected Areas in Communications 23, no. 10 (October 2005): 2032–40. http://dx.doi.org/10.1109/jsac.2005.854128.

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25

Chen, Jing. "Building Bridges among Local Area Networks." Advanced Materials Research 605-607 (December 2012): 2383–86. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2383.

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This paper introduces the principles and functions of network bridge. The bridge is a storage and forwarding device that connects two LANs. It can be used to connect networks with the same or similar system structures, then the networks being connected would have the same logical, linked control procedures. It is a data link layer connection, and it helps achieving communications between different LANs.

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26

Wong, Bernard, Becky S. McReynolds, and Wynnie Wong. "Chinese Family Firms in the San Francisco Bay Area." Family Business Review 5, no. 4 (December 1992): 355–72. http://dx.doi.org/10.1111/j.1741-6248.1992.00355.x.

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This study examines the role of ethnicity and kinship in the economic adaptation of Chinese family firms in the San Francisco Bay Area. The development and operation of these Chinese firms are the result of a complex interactive process involving ethnic resources–such as traditional values, kinship relations, and information networks–as well as structural opportunities and constraints. Throughout their history in the Bay Area, Chinese immigrant entrepreneurs have creatively adapted to their social, economic, and political environments with resources from the family.

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27

Si Yin, Yuanqiu Luo, Lei Zong, S. Rago, Jianjun Yu, N. Ansari, and Ting Wang. "Storage area network extension over passive optical networks (S-PONS)." IEEE Communications Magazine 46, no. 1 (January 2008): 44–52. http://dx.doi.org/10.1109/mcom.2008.4427229.

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28

Gumaste, A., and Si Qing Zheng. "Next-generation optical storage area networks: the light-trails approach." IEEE Communications Magazine 43, no. 3 (March 2005): 72–79. http://dx.doi.org/10.1109/mcom.2005.1404598.

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29

Grossman, Robert L., Yunhong Gu, Michael Sabala, and Wanzhi Zhang. "Compute and storage clouds using wide area high performance networks." Future Generation Computer Systems 25, no. 2 (February 2009): 179–83. http://dx.doi.org/10.1016/j.future.2008.07.009.

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30

Lala, T. K. "Guest Editorial - Storage area networking." IEEE Communications Magazine 41, no. 8 (August 2003): 70–71. http://dx.doi.org/10.1109/mcom.2003.1222719.

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31

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 (April 20, 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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32

Kithinji, Joseph. "A Review of Storage Specific Solutions for Providing Quality of Service in Storage Area Networks." International Journal of Computer Applications Technology and Research 5, no. 6 (June 4, 2016): 364–67. http://dx.doi.org/10.7753/ijcatr0506.1007.

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33

Xing, Liudong, Massarrah Tannous, Vinod M. Vokkarane, Honggang Wang, and Jun Guo. "Reliability Modeling of Mesh Storage Area Networks for Internet of Things." IEEE Internet of Things Journal 4, no. 6 (December 2017): 2047–57. http://dx.doi.org/10.1109/jiot.2017.2749375.

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34

DeCusatis, C. M. "Fiber optic cable infrastructure and dispersion compensation for storage area networks." IEEE Communications Magazine 43, no. 3 (March 2005): 86–92. http://dx.doi.org/10.1109/mcom.2005.1404600.

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35

DeCusatis, Casimer. "Dense Wavelength Division Multiplexing in Parallel Sysplex and Storage Area Networks." Optics and Photonics News 11, no. 12 (December 1, 2000): 18. http://dx.doi.org/10.1364/opn.11.12.000018.

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36

Yingping Lu and D. H. C. Du. "Storage area networking - Performance study of iscsi-based storage subsystems." IEEE Communications Magazine 41, no. 8 (August 2003): 76–82. http://dx.doi.org/10.1109/mcom.2003.1222721.

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37

Muknahallipatna, S., J. Miles, J. C. Hamann, and H. L. Johnson. "Large Fabric Storage Area Networks: Fabric Simulator Development and Preliminary Performance Analysis." International Journal of Computers and Applications 32, no. 2 (January 2010): 167–80. http://dx.doi.org/10.1080/1206212x.2010.11441973.

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38

Zhang, F., M. R. Taghizadeh, A. Waddie, T. D. Wilkinson, N. Collings, P. L. Neo, and W. A. Crossland. "Free-space optical fibre ribbon switch for use in storage area networks." IEE Proceedings - Optoelectronics 152, no. 6 (December 1, 2005): 285–91. http://dx.doi.org/10.1049/ip-opt:20050012.

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39

de los Santos, G. Rodriguez, M. Uruena, A. Munoz, and J. A. Hernandez. "Buffer Design Under Bursty Traffic with Applications in FCoE Storage Area Networks." IEEE Communications Letters 17, no. 2 (February 2013): 413–16. http://dx.doi.org/10.1109/lcomm.2012.122012.122313.

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40

Heath, J. R., and P. J. Yakutis. "High speed storage area networks using a fibre channel arbitrated loop interconnect." IEEE Network 14, no. 2 (2000): 51–56. http://dx.doi.org/10.1109/65.826372.

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41

Walker, Cameron, Michael O’Sullivan, and Timothy Thompson. "A mixed-integer approach to Core-Edge design of storage area networks." Computers & Operations Research 34, no. 10 (October 2007): 2976–3000. http://dx.doi.org/10.1016/j.cor.2005.11.009.

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42

Nikolaidis, J. "Practical storage area net working [Book Review}." IEEE Network 17, no. 4 (July 2003): 4. http://dx.doi.org/10.1109/mnet.2003.1220677.

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43

Wang, Wilson Yong Hong, Heng Ngi Yeo, Yao Long Zhu, Tow Chong Chong, Teck Yoong Chai, Luying Zhou, and Jit Bitwas. "Design and development of Ethernet-based storage area network protocol." Computer Communications 29, no. 9 (May 2006): 1271–83. http://dx.doi.org/10.1016/j.comcom.2005.10.004.

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44

Zahoor, Saniya, and Roohie Naaz Mir. "Design and Modeling of Resource-constrained IoT based Body Area Networks." Scalable Computing: Practice and Experience 21, no. 3 (August 1, 2020): 497–514. http://dx.doi.org/10.12694/scpe.v21i3.1770.

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Due to the recent advancement and development of sensing, wireless, and communication technologies, therehas been a shift in attention towards Body Area Networks (BANs). One of the most important services of BAN is the remote monitoring of patients, enabling doctors to observe, diagnose, and prescribe the patients without being physically present. Various vital signs are being monitored by body sensing devices installed inside, on or off the body of patients, but most of these devices are constrained in terms of resources such as storage, processing, bandwidth, and energy due to their smaller size. This paper aims at highlighting the key findings related to BAN applications, constrained resources, and various resource management techniques. The paper also presents the design and modeling of a resource-constrained BAN system and discusses the various scenarios of BAN in the context of resource constraints. It further proposes an Advanced Edge Clustering (AEC) approach to manage the resources such as energy, storage, and processing of BAN devices while performing real-time data capture of critical health parameters and detection of abnormal patterns. The comparison of the AEC approach is done with the Stable Election Protocol (SEP) through simulations and empirical data analysis. The results show an improvement in energy, processing time and storage requirements for the processing of data on BAN devices in AEC as compared to SEP.

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45

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

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46

Meth, K. Z., and J. Satran. "Storage Area Networking - Features of the iscsi protocol." IEEE Communications Magazine 41, no. 8 (August 2003): 72–75. http://dx.doi.org/10.1109/mcom.2003.1222720.

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47

Ríos, Ricardo, Alexandre Ribó, Roberto Mejía, and Giovanni Molina. "COMBINING NEURAL NETWORKS AND GEOSTATISTICS FOR LANDSLIDE HAZARD ASSESSMENT OF SAN SALVADOR METROPOLITAN AREA, EL SALVADOR." Revista de Matemática: Teoría y Aplicaciones 23, no. 1 (April 19, 2017): 155–72. http://dx.doi.org/10.15517/rmta.v23i1.22439.

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This contribution describes the creation of a landslide hazard assessment model for San Salvador, a department in El Salvador. The analysis started with an aerial photointerpretation from Ministry of Environment and Natural Resources of El Salvador (MARN Spanish acronym), where 4792 landslides were identified and georeferenced along with 7 conditioning factors including: geomorphology, geology, rainfall intensity, peak ground acceleration, slope angle, distance to road, and distance to geological fault. Artificial Neural Networks (ANN) were utilized to assess the susceptibility to landslides, achieving results where more than 80% of landslide were properly classified using in-sample and out of sample criteria. Logistic regression was used as base of comparison. Logistic regression obtained a lower performance. To complete the analysis we have performed interpolation of the points using the kriging method from geostatistical approach. Finally, the results show that is possible to derive a landslide hazard map, making use of a combination of ANNs and geostatistical techniques, thus the present study can help landslide mitigation in El Salvador.

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48

Buchholz, Maximilian. "Organizations, institutions and networks in local scenes: The growth of San Francisco Bay Area punk rock." Geoforum 103 (July 2019): 158–66. http://dx.doi.org/10.1016/j.geoforum.2019.04.019.

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49

Cavallo, Angelo, Antonio Ghezzi, and Silvia Sanasi. "Assessing entrepreneurial ecosystems through a strategic value network approach: evidence from the San Francisco Area." Journal of Small Business and Enterprise Development 28, no. 2 (February 23, 2021): 261–76. http://dx.doi.org/10.1108/jsbed-05-2019-0148.

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PurposeThe purpose of this article is to develop a model to assess entrepreneurial ecosystems. Specifically, the authors examine how to measure value creation and value capture mechanisms from a single participant's perspective and at the ecosystem level through a strategic value network-based approach.Design/methodology/approachBuilding on extant research on strategic networks, value networks and business models and leveraging a qualitative survey, the authors develop and test an assessment tool to measure value creation and capture within the entrepreneurial ecosystem of the San Francisco Bay Area.FindingsThe authors show that value-based measures on entrepreneurial ecosystems provide a systemic approach to assess how ecosystems operate, which can guide policymakers, entrepreneurs and all the other stakeholders of entrepreneurial ecosystems in their strategic decision-making process.Originality/valueThe authors provide an original model grounded in the strategic management and entrepreneurship literature for entrepreneurial ecosystems' assessment as few studies have done before. Besides, the authors provide an illustrative attempt to show how to empirically apply the original model by assessing the San Francisco Bay Area's entrepreneurial ecosystem.

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50

Kithinji, Joseph. "A Survey of Packets Scheduling Congestion Control Algorithms in Internet Protocol Storage Area Networks." International Journal of Computer Applications Technology and Research 5, no. 4 (April 14, 2016): 218–25. http://dx.doi.org/10.7753/ijcatr0504.1008.

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