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Relevant bibliographies by topics / Hierarchical storage management

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Academic literature on the topic 'Hierarchical storage management'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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Journal articles on the topic "Hierarchical storage management"

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Lugar, J. "Hierarchical storage management: leveraging new capabilities." IT Professional 3, no. 2 (2001): 53–55. http://dx.doi.org/10.1109/6294.918223.

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Chun, Yan, Sheng Xi Li, and Ya Zhou Li. "Research on Data Storage Structure and Management Model in Hierarchical Storage." Advanced Materials Research 121-122 (June 2010): 198–203. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.198.

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As hierarchical storage is one of the highlights in researching and realizing of mass data storage, this paper mainly describes a study on data storage structure, evaluation of data importance and data management model of hierarchical storage.

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Mookerjee, Vijay S. "Policies for data archival in hierarchical storage management." European Journal of Operational Research 138, no. 2 (April 2002): 413–35. http://dx.doi.org/10.1016/s0377-2217(01)00125-4.

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Brubeck, D. W., and L. A. Rowe. "Hierarchical storage management in a distributed VOD system." IEEE Multimedia 3, no. 3 (1996): 37–47. http://dx.doi.org/10.1109/93.556538.

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Sovetov, Boris, Tatiana Tatarnikova, and Ekaterina Poymanova. "Storage scaling management model." Information and Control Systems, no. 5 (October 20, 2020): 43–49. http://dx.doi.org/10.31799/1684-8853-2020-5-43-49.

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Introduction: The implementation of data storage process requires timely scaling of the infrastructure to accommodate the data received for storage. Given the rapid accumulation of data, new models of storage capacity management are needed, which should take into account the hierarchical structure of the data storage, various requirements for file storage and restrictions on the storage media size. Purpose: To propose a model for timely scaling of the storage infrastructure based on predictive estimates of the moment when the data storage media is fully filled. Results: A model of storage capacity management is presented, based on the analysis of storage system state patterns. A pattern is a matrix each cell of which reflects the filling state of the storage medium at an appropriate level in the hierarchical structure of the storage system. A matrix cell is characterized by the real, limit, and maximum values of its carrier capacity. To solve the scaling problem for a data storage system means to predict the moments when the limit capacity and maximum capacity of the data carrier are reached. The difference between the predictive estimatesis the time which the administrator has to connect extra media. It is proposed to calculate the values of the predictive estimates programmatically, using machine learning methods. It is shown that when making a short-term prediction, machine learning methods have lower accuracy than ARIMA, an integrated model of autoregression and moving average. However, when making a long-term forecast, machine learning methods provide results commensurate with those from ARIMA. Practical relevance: The proposed model is necessary for timely allocation of storage capacity for incoming data. The implementation of this model at the storage input allows you to automate the process of connecting media, which helps prevent the loss of data entering the system.

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QIU, Jian-Ping, Guang-Yan ZHANG, and Ji-Wu SHU. "DMStone: A Tool for Evaluating Hierarchical Storage Management Systems." Journal of Software 23, no. 4 (August 3, 2012): 987–95. http://dx.doi.org/10.3724/sp.j.1001.2012.04046.

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Fan, Songli, Qian Ai, and Longjian Piao. "Hierarchical Energy Management of Microgrids including Storage and Demand Response." Energies 11, no. 5 (May 1, 2018): 1111. http://dx.doi.org/10.3390/en11051111.

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Moinzadeh, Kamran, and Emre Berk. "An archiving model for a hierarchical information storage environment." European Journal of Operational Research 123, no. 1 (May 2000): 206–25. http://dx.doi.org/10.1016/s0377-2217(99)00079-x.

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Ding, Wei, Yuanrui Zhang, Mahmut Kandemir, and Seung Woo Son. "Compiler-Directed File Layout Optimization for Hierarchical Storage Systems." Scientific Programming 21, no. 3-4 (2013): 65–78. http://dx.doi.org/10.1155/2013/167581.

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File layout of array data is a critical factor that effects the behavior of storage caches, and has so far taken not much attention in the context of hierarchical storage systems. The main contribution of this paper is a compiler-driven file layout optimization scheme for hierarchical storage caches. This approach, fully automated within an optimizing compiler, analyzes a multi-threaded application code and determines a file layout for each disk-resident array referenced by the code, such that the performance of the target storage cache hierarchy is maximized. We tested our approach using 16 I/O intensive application programs and compared its performance against two previously proposed approaches under different cache space management schemes. Our experimental results show that the proposed approach improves the execution time of these parallel applications by 23.7% on average.

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Ye, Chang, Shihong Miao, Qi Lei, and Yaowang Li. "Dynamic Energy Management of Hybrid Energy Storage Systems with a Hierarchical Structure." Energies 9, no. 6 (May 24, 2016): 395. http://dx.doi.org/10.3390/en9060395.

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Dissertations / Theses on the topic "Hierarchical storage management"

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Ma, Shanshan Wiedenbeck Susan McCain Katherine Wootton. "Using hierarchical folders and tags for file management /." Philadelphia, Pa. : Drexel University, 2010. http://hdl.handle.net/1860/3271.

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Stroe, Ionel Daniel. "Scalable visual hierarchy exploration." Link to electronic version, 2000. http://www.wpi.edu/Pubs/ETD/Available/etd-0510100-142928.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: semantic caching; prefetching; recursive queries; hierarchical structures; database backend; visual exploration. Includes bibliographical references (p. 81-84).

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Haun, Stefan, Robert Krüger, and Peter Wehner. "SENSE: Combining Mashup and HSM technology by semantic means to improve usability and performance." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-125715.

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The amount of data stored and consumed on a daily basis as well as the complexity of the data structure have grown rapidly in past years [1]. Especially business companies try to reduce the rising expenses from storage infrastructure as well as from re-implementation of user interfaces to adapt to evolving tasks. (...)

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Cho, Chia-Ying, and 卓佳穎. "Hierarchical Storage Management for Multimedia Data." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/45758272232507431028.

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碩士
國立交通大學
資訊工程研究所
83
A hierarchical storage manager uses tertiary storage such as tape or CD jukeboxes for archive data, and fast magnetic disks as a local file server to cache frequently accessed data. If the requested data is not on the disks, the server should download it from the jukeboxes and may replace an old one. In this thesis, we propose four data replacement policies: popularity, partitioned LRU+LFU, partitioned LRU+LFU with popularity, partitioned LRU with popularity. The popularity replacement policy has a better performance than the existing policies. We also propose a storage management strategy which can reduce the seek time in retrieval and the CPU cost in allocation and deallocation of disk space.

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Liu, Xin. "Optimizing Hierarchical Storage Management For Database System." Thesis, 2014. http://hdl.handle.net/10012/8531.

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Caching is a classical but effective way to improve system performance. To improve system performance, servers, such as database servers and storage servers, contain significant amounts of memory that act as a fast cache. Meanwhile, as new storage devices such as flash-based solid state drives (SSDs) are added to storage systems over time, using the memory cache is not the only way to improve system performance. In this thesis, we address the problems of how to manage the cache of a storage server and how to utilize the SSD in a hybrid storage system. Traditional caching policies are known to perform poorly for storage server caches. One promising approach to solving this problem is to use hints from the storage clients to manage the storage server cache. Previous hinting approaches are ad hoc, in that a predefined reaction to specific types of hints is hard-coded into the caching policy. With ad hoc approaches, it is difficult to ensure that the best hints are being used, and it is difficult to accommodate multiple types of hints and multiple client applications. In this thesis, we propose CLient-Informed Caching (CLIC), a generic hint-based technique for managing storage server caches. CLIC automatically interprets hints generated by storage clients and translates them into a server caching policy. It does this without explicit knowledge of the application-specific hint semantics. We demonstrate using trace-based simulation of database workloads that CLIC outperforms hint-oblivious and state-of-the-art hint-aware caching policies. We also demonstrate that the space required to track and interpret hints is small. SSDs are becoming a part of the storage system. Adding SSD to a storage system not only raises the question of how to manage the SSD, but also raises the question of whether current buffer pool algorithms will still work effectively. We are interested in the use of hybrid storage systems, consisting of SSDs and hard disk drives (HDD), for database management. We present cost-aware replacement algorithms for both the DBMS buffer pool and the SSD. These algorithms are aware of the different I/O performance of HDD and SSD. In such a hybrid storage system, the physical access pattern to the SSD depends on the management of the DBMS buffer pool. We studied the impact of the buffer pool caching policies on the access patterns of the SSD. Based on these studies, we designed a caching policy to effectively manage the SSD. We implemented these algorithms in MySQL's InnoDB storage engine and used the TPC-C workload to demonstrate that these cost-aware algorithms outperform previous algorithms.

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Books on the topic "Hierarchical storage management"

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Russo, Vincent. A class hierarchical, object-oriented approach to virtual memory management. [Washington, DC: National Aeronautics and Space Administration, 1989.

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Redbooks, IBM. Using Adsm Hierarchical Storage Management. Ibm, 1996.

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Redbooks, IBM. Complementing As/400 Storage Management Using Hierarchical Storage Management Apis. Ibm, 1999.

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Blokdyk, Gerardus. Hierarchical Storage Management a Complete Guide - 2020 Edition. Emereo Pty Limited, 2020.

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Book chapters on the topic "Hierarchical storage management"

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Xu, Yaoqiang, Chunxiao Xing, and Lizhu Zhou. "A Cache Replacement Algorithm in Hierarchical Storage of Continuous Media Object." In Advances in Web-Age Information Management, 157–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27772-9_17.

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Reiner, Bernd, Karl Hahn, Gabriele Höfling, and Peter Baumann. "Hierarchical Storage Support and Management for Large-Scale Multidimensional Array Database Management Systems." In Lecture Notes in Computer Science, 689–700. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46146-9_68.

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Reiner, Bernd, and Karl Hahn. "HEAVEN: A Hierarchical Storage and Archive Environment for Multidimensional Array Database Management Systems." In Advances in Database Technology - EDBT 2004, 854–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24741-8_57.

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Habyarimana, Ephrem, and Sofia Michailidou. "Genomics Data." In Big Data in Bioeconomy, 69–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_6.

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AbstractIn silico prediction of plant performance is gaining increasing breeders’ attention. Several statistical, mathematical and machine learning methodologies for analysis of phenotypic, omics and environmental data typically use individual or a few data layers. Genomic selection is one of the applications, where heterogeneous data, such as those from omics technologies, are handled, accommodating several genetic models of inheritance. There are many new high throughput Next Generation Sequencing (NGS) platforms on the market producing whole-genome data at a low cost. Hence, large-scale genomic data can be produced and analyzed enabling intercrosses and fast-paced recurrent selection. The offspring properties can be predicted instead of manually evaluated in the field . Breeders have a short time window to make decisions by the time they receive data, which is one of the major challenges in commercial breeding. To implement genomic selection routinely as part of breeding programs, data management systems and analytics capacity have therefore to be in order. The traditional relational database management systems (RDBMS), which are designed to store, manage and analyze large-scale data, offer appealing characteristics, particularly when they are upgraded with capabilities for working with binary large objects. In addition, NoSQL systems were considered effective tools for managing high-dimensional genomic data. MongoDB system, a document-based NoSQL database, was effectively used to develop web-based tools for visualizing and exploring genotypic information. The Hierarchical Data Format (HDF5), a member of the high-performance distributed file systems family, demonstrated superior performance with high-dimensional and highly structured data such as genomic sequencing data.

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"Hierarchical Storage Management." In Encyclopedia of Database Systems, 1308. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_2760.

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Ghandeharizadeh, Shahram, Douglas J. Ierardi, and Roger Zimmermann. "Management of Space in Hierarchical Storage Systems." In Computing the Brain, 265—IV. Elsevier, 2001. http://dx.doi.org/10.1016/b978-012059781-9/50019-5.

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Nagaty, Khaled Ahmed. "Hierarchical Organization as a Facilitator of Information Management in Human Collaboration." In Open Information Management, 44–109. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-246-6.ch004.

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The purpose of this chapter is to discuss the relationship between three entities: hierarchical organization, information management and human collaboration. This relationship is composed of two parts: the first part is the relationship between the hierarchical organization and information management where the role of the hierarchical organization to facilitate the information management processes is discussed. The second part is the relationship between information management and human collaboration where the role of information management to improve human collaboration in problem solving is discussed. The information management processes are illustrated through an information management life cycle model. This model has three major stages: active, semi-active and inactive stages and has three major phases: creation, searching and utilization phases. The creation phase includes: information creation and using, information authoring and modifying and information organization and indexing. The searching phase includes: information storage and retrieving and information exchange. The utilization phase includes: information accessing and filtering processes. The arguments about the role of hierarchical organization in information management and human collaboration are also discussed. The author showed that the hierarchical organization acts as a facilitator for common information management processes which are required in team collaboration such as: information gathering, organization, retrieving, filtering, exchange, integration or fusion, display and visualization. Human collaboration models are discussed with emphasis on the team collaboration structural model which has four unique but interdependent stages of team collaboration. These stages are: team knowledge construction, collaborative team problem solving, team consensus, and product evaluation and revision. Each stage has four levels: meta-cognition process which guides the overall problem solving process, the information processing tasks which is required by the team to complete each collaboration stage, the knowledge required to support the information processing tasks and the communication mechanisms for knowledge building and information processing. The author focused on the role of information management to improve human collaboration across the four collaboration stages of the team collaboration structural model. He showed that the hierarchical organization is more efficient for information management processes and team collaboration rather than other alternative organizations such as flat, linear and network organizations.

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Chhaya, Lipi, Paawan Sharma, Adesh Kumar, and Govind Bhagwatikar. "Application of Data Mining in Smart Grid Technology." In Encyclopedia of Information Science and Technology, Fifth Edition, 815–27. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3479-3.ch056.

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Smart grid technology is a radical approach for improvisation in existing power grid. Some of the significant features of smart grid technology are bidirectional communication, AMI, SCADA, renewable integration, active consumer participation, distribution automation, and complete management of entire grid through wireless communication standards and technologies. Management of complex, hierarchical, and heterogeneous smart grid infrastructure requires data collection, storage, processing, analysis, retrieval, and communication for self-healing and complete automation. Data mining techniques can be an effective solution for smart grid operation and management. Data mining is a computational process for data analysis. Data scrutiny is unavoidable for unambiguous knowledge discovery as well as decision making practices. Data mining is inevitable for analysis of various statistics associated with power generation, distribution automation, data communications, billing, consumer participation, and fault diagnosis in smart power grid.

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Dadashzadeh, Mohammad. "Set Comparison in Relational Query Languages." In Encyclopedia of Database Technologies and Applications, 624–31. IGI Global, 2005. http://dx.doi.org/10.4018/978-1-59140-560-3.ch103.

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Today’s de facto database standard, the relational database, was conceived in the late 1960’s by Edgar F. Codd at IBM. The relational data model offered the user a logical view of the data that was shielded from consideration of how the data would, in fact, be physically organized in storage. This feat was accomplished in large part by the introduction of relational query languages that would specify the desired set of records in a non-procedural fashion. In contrast to the prevailing record-at-a-time, loop-oriented, procedural query languages of the hierarchical and network database management systems, relational query languages were set-oriented in that they would operate on sets of records (i.e., relations or tables) at-a-time in order to produce the desired set of output records. Codd introduced both a relational algebra and a relational calculus as a basis for dealing with data in relational form. Indeed, he defined what the first relational language was: Data Sublanguage Alpha (Codd, 1971).

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Koukia, Spiridoula, Maria Rigou, and Spiros Sirmakessis. "Content Personalization for Mobile Interfaces." In Human Computer Interaction, 992–96. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-87828-991-9.ch061.

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The contribution of context information to content management is of great importance. The increase of storage capacity in mobile devices gives users the possibility to maintain large amounts of content to their phones. As a result, this amount of content is increasing at a high rate. Users are able to store a huge variety of content such as contacts, text messages, ring tones, logos, calendar events, and textual notes. Furthermore, the development of novel applications has created new types of content, which include images, videos, MMS (multi-media messaging), e-mail, music, play lists, audio clips, bookmarks, news and weather, chat, niche information services, travel and entertainment information, driving instructions, banking, and shopping (Schilit & Theimer, 1994; Schilit, Adams, & Want, 1994; Brown, 1996; Brown, Bovey, & Chen, 1997). The fact that users should be able to store the content on their mobile phone and find the content they need without much effort results in the requirement of managing the content by organizing and annotating it. The purpose of information management is to aid users by offering a safe and easy way of retrieving the relevant content automatically, to minimize their effort and maximize their benefit (Sorvari et al., 2004). The increasing amount of stored content in mobile devices and the limitations of physical mobile phone user interfaces introduce a usability challenge in content management. The physical mobile phone user interface will not change considerably. The physical display sizes will not increase since in the mobile devices the display already covers a large part of the surface area. Text input speed will not change much, as keyboard-based text input methods have been the most efficient way to reduce slowness. While information is necessary for many applications, the human brain is limited in terms of how much information it can process at one time. The problem of information management is more complex in mobile environments (Campbell & Tarasewich, 2004). One way to reduce information overload and enhance content management is through the use of context metadata. Context metadata is information that describes the context in which a content item was created or received and can be used to aid users in searching, retrieving, and organizing the relevant content automatically. Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and the applications themselves (Dey, 2001). Some types of context are the physical context, such as time, location, and date; the social context, such as social group, friends, work, and home; and the mental context, which includes users’ activities and feelings (Ryan, Pascoe, & Morse, 1997; Dey, Abowd, & Wood, 1998; Lucas, 2001). By organizing and annotating the content, we develop a new way of managing it, while content management features are created to face efficiently the usability challenge. Context metadata helps the user find the content he needs by enabling single and multi-criteria searches (e.g., find photos taken in Paris last year), example-based searches (e.g., find all the video clips recorded in the same location as the selected video clip), and automatic content organization for efficient browsing (e.g., location-based content view, where the content is arranged hierarchically based on the content capture location and information about the hierarchical relationships of different locations).

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Conference papers on the topic "Hierarchical storage management"

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Lin, Song, Benjamin Arai, and Dimitrios Gunopulos. "Reliable Hierarchical Data Storage in Sensor Networks." In 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007). IEEE, 2007. http://dx.doi.org/10.1109/ssdbm.2007.39.

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Lillaney, Kunal, Dean Kleissas, Alexander Eusman, Eric Perlman, William Gray Roncal, Joshua T. Vogelstein, and Randal Burns. "Building NDStore Through Hierarchical Storage Management and Microservice Processing." In 2018 IEEE 14th International Conference on e-Science (e-Science). IEEE, 2018. http://dx.doi.org/10.1109/escience.2018.00037.

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Gerofi, Balazs, Akio Shimada, Atsushi Hori, and Yutaka Ishikawa. "Abstract: Toward Operating System Assisted Hierarchical Memory Management for Heterogeneous Architectures." In 2012 SC Companion: High Performance Computing, Networking, Storage and Analysis (SCC). IEEE, 2012. http://dx.doi.org/10.1109/sc.companion.2012.181.

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Gerofi, Balazs, Akio Shimada, Atsushi Hori, and Yutaka Ishikawa. "Poster: Toward Operating System Assisted Hierarchical Memory Management for Heterogeneous Architectures." In 2012 SC Companion: High Performance Computing, Networking, Storage and Analysis (SCC). IEEE, 2012. http://dx.doi.org/10.1109/sc.companion.2012.182.

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Ma, Hongyuan, Zhenjun Liu, Huan Zhang, Shuo Feng, Xiaoming Han, and Lu Xu. "Experiences with Hierarchical Storage Management Support in Blue Whale File System." In 2010 International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE, 2010. http://dx.doi.org/10.1109/pdcat.2010.35.

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Wong, Albert W. K., H. K. Huang, and Ronald L. Arenson. "Hierarchical image storage management: design and implementation for a distributed PACS." In Medical Imaging 1995, edited by R. Gilbert Jost and Samuel J. Dwyer III. SPIE, 1995. http://dx.doi.org/10.1117/12.208826.

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Zhao, Xiaonan, Zhanhuai Li, and Leijie Zeng. "A Hierarchical Storage Strategy Based on Block-Level Data Valuation." In 2008 Fourth International Conference on Networked Computing and Advanced Information Management (NCM). IEEE, 2008. http://dx.doi.org/10.1109/ncm.2008.101.

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Westermann, D., S. Nicolai, and P. Bretschneider. "Energy management for distribution networks with storage systems — A hierarchical approach." In Energy Society General Meeting. IEEE, 2008. http://dx.doi.org/10.1109/pes.2008.4596533.

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D'Alessio, A., A. Bertini, F. Ciferri, G. Ferrari, and M. Strambini. "i-DHSM: Dynamic Hierarchical Storage Manager: Media Management for Audiovisual Digital Archiving." In SMPTE Advanced Motion Imaging Conference. IEEE, 2001. http://dx.doi.org/10.5594/m00355.

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Pangborn, Herschel C., Cary E. Laird, and Andrew G. Alleyne. "Hierarchical Hybrid MPC for Management of Distributed Phase Change Thermal Energy Storage*." In 2020 American Control Conference (ACC). IEEE, 2020. http://dx.doi.org/10.23919/acc45564.2020.9147698.

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