Готові списки джерел за темами / Network management models

Добірка наукової літератури з теми "Network management models"

Автор: Grafiati
Опубліковано: 12 травня 2022

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Зміст

  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Статті в журналах з теми "Network management models":

1

Šubrt, T. "Multiple criteria network models for project management." Agricultural Economics (Zemědělská ekonomika) 50, No. 2 (February 24, 2012): 71–76. http://dx.doi.org/10.17221/5169-agricecon.

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The aim of the paper is to present one possibility of how to model and solve a resource oriented critical path problem. As a starting point, a single criteria model for critical path finding is shortly mentioned. Lately, more criteria functions for this model are defined. If any project task uses more resources for its completion, its duration usually depends on only one of them – other resources are not fully used. In here defined multiple criteria approach, these dependencies are not assumed. Each criteria function is derived from a theoretical task duration based on a number of units of only one resource and on its importance. Using either linear programming model with aggregated criteria function or simple Excel calculation with Microsoft Project software support, a so-called compromise critical path can be found. On this path, some resources are overallocated and some are underallocated but the total sum of all underallocations and all overallocations is minimized. All resources are used as effectively as possible and the project is as short as possible too.
2

Pilz, Andreas, and Joachim Swoboda. "Network Management Information Models." AEU - International Journal of Electronics and Communications 58, no. 3 (January 2004): 165–71. http://dx.doi.org/10.1078/1434-8411-54100224.

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3

Vemuganti, R. R., M. Oblak, and A. Aggarwal. "Network Models for Fleet Management." Decision Sciences 20, no. 1 (March 1989): 182–97. http://dx.doi.org/10.1111/j.1540-5915.1989.tb01406.x.

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4

van Hemmen, L. J. G. T. "Models supporting the network management organization." International Journal of Network Management 10, no. 6 (2000): 299–314. http://dx.doi.org/10.1002/1099-1190(200011/12)10:6<299::aid-nem377>3.0.co;2-l.

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5

Chinneck, J. W., and R. H. H. Moll. "Processing network models for forest management." Omega 23, no. 5 (October 1995): 499–510. http://dx.doi.org/10.1016/0305-0483(95)00023-h.

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6

Szyrkowiec, Thomas, Achim Autenrieth, and Wolfgang Kellerer. "Optical Network Models and Their Application to Software-Defined Network Management." International Journal of Optics 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5150219.

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Software-defined networking is finding its way into optical networks. Here, it promises a simplification and unification of network management for optical networks allowing automation of operational tasks despite the highly diverse and vendor-specific commercial systems and the complexity and analog nature of optical transmission. Common abstractions and interfaces are a fundamental component for software-defined optical networking. Currently, a number of models for optical networks are available. They all claim to provide open and vendor agnostic management of optical equipment. In this work, we survey and compare the most important models and propose an intent interface for creating virtual topologies which is integrated in the existing model ecosystem.
7

Van Wambeke, N., F. Armando, and A. Abdelkefi. "Models and Architecture for Autonomic Network Management." International Journal of Business Data Communications and Networking 5, no. 2 (April 2009): 35–51. http://dx.doi.org/10.4018/jbdcn.2009040103.

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8

Lopez de Vergaro, J. E., V. A. Villagra, J. I. Asensio, and J. Berrocat. "Ontologies: giving semantics to network management models." IEEE Network 17, no. 3 (May 2003): 15–21. http://dx.doi.org/10.1109/mnet.2003.1201472.

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9

Колесников, M. Kolesnikov, Киба, and M. Kiba. "Management in Social and Professional Networks." Administration 3, no. 3 (September 17, 2015): 68–72. http://dx.doi.org/10.12737/13340.

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The extended idea about “professional network” has been developed based on the concept of “social network”. The main features and tasks of professional social networks have been revealed. The task of management in social and professional networks which demanded formalization of criteria and models of their functioning has been set. Professional social network has been represented as a graph model. Known models of influence in social and professional networks have been considered and described in detail from the point of view of their analytical representation. The main features of professional networks influence on society and production have been revealed. Possibility for mass distribution of information by means of professional social networks has been analyzed. A mechanism for network agents’ opinion formation on the basis of authoritative opinion and a trust vector has been revealed.
10

Scott, Brenda M. "Analysis of Network and System Management Tasks." Proceedings of the Human Factors Society Annual Meeting 33, no. 4 (October 1989): 215–18. http://dx.doi.org/10.1177/154193128903300403.

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Network and system management tasks revolve around the activities necessary to establish and maintain data communication networks. This can include tasks such as: • monitoring the network hardware and software components for problems and performance information • identifying and correcting problems in the network • planning and making changes to the network. Task analysis was performed on data available from a customer case study database, (Beith, Moore, Pendley, and Percival, 1985). Models were developed for several network and system management tasks. This paper discusses one of the task models, problem determination, and presents recommendations for information structuring and task flow.
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Статті в журналах Дисертації Книги

Дисертації з теми "Network management models":

1

Yao, Zhonghui. "ATM network models for traffic management." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq23559.pdf.

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2

Frank, Simon James. "Predicting corporate credit ratings using neural network models." Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/913.

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Thesis (MBA (Business Management))--University of Stellenbosch, 2009.
ENGLISH ABSTRACT: For many organisations who wish to sell their debt, or investors who are looking to invest in an organisation, company credit ratings are an important surrogate measure for the marketability or risk associated with a particular issue. Credit ratings are issued by a limited number of authorised companies – with the predominant being Standard & Poor’s, Moody’s and Fitch – who have the necessary experience, skills and motive to calculate an objective credit rating. In the wake of some high profile bankruptcies, there has been recent conjecture about the accuracy and reliability of current ratings. Issues relating specifically to the lack of competition in the rating market have been identified as possible causes of the poor timeliness of rating updates. Furthermore, the cost of obtaining (or updating) a rating from one of the predominant agencies has also been identified as a contributing factor. The high costs can lead to a conflict of interest where rating agencies are obliged to issue more favourable ratings to ensure continued patronage. Based on these issues, there is sufficient motive to create more cost effective alternatives to predicting corporate credit ratings. It is not the intention of these alternatives to replace the relevancy of existing rating agencies, but rather to make the information more accessible, increase competition, and hold the agencies more accountable for their ratings through better transparency. The alternative method investigated in this report is the use of a backpropagation artificial neural network to predict corporate credit ratings for companies in the manufacturing sector of the United States of America. Past research has shown that backpropagation neural networks are effective machine learning techniques for predicting credit ratings because no prior subjective or expert knowledge, or assumptions on model structure, are required to create a representative model. For the purposes of this study only public information and data is used to develop a cost effective and accessible model. The basis of the research is the assumption that all information (both quantitive and qualitative) that is required to calculate a credit rating for a company, is contained within financial data from income statements, balance sheets and cash flow statements. The premise of the assumption is that any qualitative or subjective assessment about company creditworthiness will ultimately be reflected through financial performance. The results show that a backpropagation neural network, using 10 input variables on a data set of 153 companies, can classify 75% of the ratings accurately. The results also show that including collinear inputs to the model can affect the classification accuracy and prediction variance of the model. It is also shown that latent projection techniques, such as partial least squares, can be used to reduce the dimensionality of the model without making any assumption about data relevancy. The output of these models, however, does not improve the classification accuracy achieved using selected un-correlated inputs.
AFRIKAANSE OPSOMMING: Vir baie organisasies wat skuldbriewe wil verkoop, of beleggers wat in ʼn onderneming wil belê is ʼn maatskappy kredietgradering ’n belangrike plaasvervangende maatstaf vir die bemarkbaarheid van, of die risiko geassosieer met ʼn betrokke uitgifte. Kredietgraderings word deur ʼn beperkte aantal gekeurde maatskappye uitgereik – met die belangrikste synde Standard & Poor’s, Moody’s en Fitch. Hulle het almal die nodige ervaring, kundigheid en rede om objektiewe kredietgraderings te bereken. In die nadraai van ʼn aantal hoë profiel bankrotskappe was daar onlangs gissings oor die akkuraatheid en betroubaarheid van huidige graderings. Kwessies wat spesifiek verband hou met die gebrek aan kompetisie in die graderingsmark is geïdentifiseer as ‘n moontlike oorsaak vir die swak tydigheid van gradering opdatering. Verder word die koste om ‘n gradering (of opdatering van gradering) van een van die dominante agentskappe te bekom ook geïdentifiseer as ʼn verdere bydraende faktor gesien. Die hoë koste kan tot ‘n belange konflik lei as graderingsagentskappe onder druk kom om gunstige graderings uit te reik om sodoende volhoubare klante te behou. As gevolg van hierdie kwessies is daar voldoende motivering om meer koste doeltreffende alternatiewe vir die skatting van korporatiewe kredietgraderings te ondersoek. Dit is nie die doelwit van hierdie alternatiewe om die toepaslikheid van bestaande graderingsagentskappe te vervang nie, maar eerder om die inligting meer toeganklik te maak, mededinging te verhoog en om die agentskappe meer toerekenbaar vir hul graderings te maak deur beter deursigtigheid. Die alternatiewe manier wat in hierdie verslag ondersoek word, is die gebruik van ‘n kunsmatige neurale netwerk om die kredietgraderings van vervaardigingsmaatskappye in die VSA te skat. Vorige navorsing het getoon dat neurale netwerke doeltreffende masjienleer tegnieke is om kredietgraderings te skat omdat geen voorafkennis of gesaghebbende kundigheid, of aannames oor die modelstruktuur nodig is om ‘n verteenwoordigende model te bou. Vir doeleindes van hierdie navorsingsverslag word slegs openbare inligting en data gebruik om ʼn kostedoeltreffende en toeganklike model te bou. Die grondslag van hierdie navorsing is die aanname dat alle inligting (beide kwantitatief en kwalitatief) wat benodig word om ʼn kredietgradering vir ʼn onderneming te bereken, opgesluit is in die finansiële data in die inkomstestate, balansstate en kontantvloei state. Die aanname is dus dat alle kwalitatiewe of subjektiewe assessering oor ‘n maatskappy se kredietwaardigheid uiteindelik in die finansiële prestasie sal reflekteer. Die resultate toon dat ʼn neurale netwerk met 10 toevoer veranderlikes op ‘n datastel van 153 maatskappye 75% van die graderings akkuraat klassifiseer. Die resultate toon ook dat die insluiting van kollineêre toevoere tot die model die klassifikasie akkuraatheid en die variansie van die skatting kan beïnvloed. Daar word verder getoon dat latente projeksietegnieke, soos parsiële kleinste kwadrate, die dimensies van die model kan verminder sonder om enige aannames oor data toepaslikheid te maak. Die afvoer van hierdie modelle verhoog egter nie die klassifikasie akkuraatheid wat behaal is met die gekose ongekorreleerde toevoere nie. 121 pages.
3

Yang, Xi. "Applying stochastic programming models in financial risk management." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4068.

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This research studies two modelling techniques that help seek optimal strategies in financial risk management. Both are based on the stochastic programming methodology. The first technique is concerned with market risk management in portfolio selection problems; the second technique contributes to operational risk management by optimally allocating workforce from a managerial perspective. The first model involves multiperiod decisions (portfolio rebalancing) for an asset and liability management problem and deals with the usual uncertainty of investment returns and future liabilities. Therefore it is well-suited to a stochastic programming approach. A stochastic dominance concept is applied to control the risk of underfunding. A small numerical example and a backtest are provided to demonstrate advantages of this new model which includes stochastic dominance constraints over the basic model. Adding stochastic dominance constraints comes with a price: it complicates the structure of the underlying stochastic program. Indeed, new constraints create a link between variables associated with different scenarios of the same time stage. This destroys the usual tree-structure of the constraint matrix in the stochastic program and prevents the application of standard stochastic programming approaches such as (nested) Benders decomposition and progressive hedging. A structure-exploiting interior point method is applied to this problem. Computational results on medium scale problems with sizes reaching about one million variables demonstrate the efficiency of the specialised solution technique. The second model deals with operational risk from human origin. Unlike market risk that can be handled in a financial manner (e.g. insurances, savings, derivatives), the treatment of operational risks calls for a “managerial approach”. Consequently, we propose a new way of dealing with operational risk, which relies on the well known Aggregate Planning Model. To illustrate this idea, we have adapted this model to the case of a back office of a bank specialising in the trading of derivative products. Our contribution corresponds to several improvements applied to stochastic programming modelling. First, the basic model is transformed into a multistage stochastic program in order to take into account the randomness associated with the volume of transaction demand and with the capacity of work provided by qualified and non-qualified employees over the planning horizon. Second, as advocated by Basel II, we calculate the probability distribution based on a Bayesian Network to circumvent the difficulty of obtaining data which characterises uncertainty in operations. Third, we go a step further by relaxing the traditional assumption in stochastic programming that imposes a strict independence between the decision variables and the random elements. Comparative results show that in general these improved stochastic programming models tend to allocate more human expertise in order to hedge operational risks. The dual solutions of the stochastic programs are exploited to detect periods and nodes that are at risk in terms of expertise availability.
4

Wolff, Janik. "IT-Security Investment Models." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-6390.

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5

Haskose, Ahmed. "Queueing network models for workload control in the make-to-order sector." Thesis, Lancaster University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274277.

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6

Marufuzzaman, Mohammad. "Models for a carbon constrained, reliable biofuel supply chain network design and management." Thesis, Mississippi State University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3631817.

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This dissertation studies two important problems in the field of biomass supply chain network. In the first part of the dissertation, we study the impact of different carbon regulatory policies such as carbon cap, carbon tax, carbon cap-and-trade and carbon offsetmechanism on the design and management of a biofuel supply chain network under both deterministic and stochastic settings. These mathematical models identify locations and production capacities for biocrude production plants by exploring the trade-offs that exist between transportations costs, facility investment costs and emissions. The model is solved using a modified L-shaped algorithm. We used the state of Mississippi as a testing ground for our model. A number of observations are made about the impact of each policy on the biofuel supply chain network.

In the second part of the dissertation, we study the impact of intermodal hub disruption on a biofuel supply chain network. We present mathematical model that designs multimodal transportation network for a biofuel supply chain system, where intermodal hubs are subject to site-dependent probabilistic disruptions. The disruption probabilities of intermodal hubs are estimated by using a probabilistic model which is developed using real world data. We further extend this model to develop a mixed integer nonlinear program that allocates intermodal hub dynamically to cope with biomass supply fluctuations and to hedge against natural disasters. We developed a rolling horizon based Benders decomposition algorithm to solve this challenging NP-hard problem. Numerical experiments show that this proposed algorithm can solve large scale problem instances to a near optimal solution in a reasonable time. We applied the models to a case study using data from the southeast region of U.S. Finally, a number of managerial insights are drawn into the impact of intermodal-related risk on the supply chain performance.

7

Wang, Shuo. "Optimization Models for Network-Level Transportation Asset Preservation Strategies." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1416578565.

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8

Wilson, Cynthia M. (Cynthia Marie). "Development of operations based long range network capacity planning models." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66039.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering; in conjunction with the Leaders for Global Operations Program at MIT, June 2011.
"June 2011." Cataloged from PDF version of thesis.
Includes bibliographical references (p. 77-80).
Planning for vaccines manufacturing capacity is both a complex task requiring many inputs and an important function of manufacturers to ensure the supply of vaccines that prevent life-threatening illnesses. This thesis explores the development of an operations based long range capacity planning model to facilitate the annual strategic capacity planning review at Novartis Vaccines. This model was developed in conjunction with process owners at Novartis Vaccines and utilizes operations principles, non-linear optimization, and process data to efficiently calculate the capacity of the vaccine manufacturing network. The resulting network capacity is then compared to the long range demand for vaccine production to determine capacity deficits and surpluses in the current manufacturing network as well as analyzing options for more efficient capacity usage. Although this model was developed specifically with respect to the Novartis Vaccines manufacturing network, the capacity calculation and gap analysis tools for single and multiproduct facilities as well as batch allocation for in multi-product, multi-facility networks are also applicable to other companies and industries that utilize batch processing. The model was validated utilizing process information from a production line that was already operating near capacity and showed a 95% agreement with the data from this line. Additionally, this operations based planning model was able to achieve buy-in from both process owners and the global strategy organization allowing it to be implemented in the planning cycle. Use of this tool enables efficiency and transparency in capacity analysis as well as the tools to examine the impact of a range of scenarios on the manufacturing network.
by Cynthia M. Wilson.
S.M.
M.B.A.
9

PONCANO, VERA M. L. "Estudo de organização em rede na metrologia em química." reponame:Repositório Institucional do IPEN, 2007. http://repositorio.ipen.br:8080/xmlui/handle/123456789/11659.

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Made available in DSpace on 2014-10-09T12:54:25Z (GMT). No. of bitstreams: 0
Made available in DSpace on 2014-10-09T14:07:57Z (GMT). No. of bitstreams: 1 12761.pdf: 16017152 bytes, checksum: 54635689e6f56c65e84f7fe9bf404148 (MD5)
Tese (Doutoramento)
IPEN/T
Instituto de Pesquisas Energéticas e Nucleares - IPEN/CNEN-SP
10

Bsaybes, Sahar. "Models and algorithms for fleet management of autonomous vehicles." Thesis, Université Clermont Auvergne‎ (2017-2020), 2017. http://www.theses.fr/2017CLFAC114/document.

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Résumé indisponible
The VIPAFLEET project aims at developing a framework to manage a fleet of IndividualPublic Autonomous Vehicles (VIPA). We consider a fleet of cars distributed at specifiedstations in an industrial area to supply internal transportation, where the cars can beused in different modes of circulation (tram mode, elevator mode, taxi mode). The goalis to develop and implement suitable algorithms for each mode in order to satisfy all therequests either under an economic point aspect or under a quality of service aspect, thisby varying the studied objective functions.We model the underlying online transportation system as a discrete event basedsystem and propose a corresponding fleet management framework, to handle modes,demands and commands. We consider three modes of circulation, tram, elevator andtaxi mode. We propose for each mode appropriate online algorithms and evaluate theirperformance, both in terms of competitive analysis and practical behavior by computationalresults. We treat in this work, the pickup and delivery problem related to theTram mode and the Elevator mode the pickup and delivery problem with time windowsrelated to the taxi mode by means of flows in time-expanded networks
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Книги з теми "Network management models":

1

Pentico, David W. Management science: Mathematical programming and network models. Fort Worth: Harcourt Brace Jovanovich College Publishers, 1992.

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2

Tavares, L. Valadares. Advanced models for project management. Boston, MA: Kluwer Academic Publishers, 1999.

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3

Bhattarakosol, Pattarasinee. Intelligent quality of service technologies and network management: Models for enhancing communication. Hershey, PA: Information Science Reference, 2010.

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4

Borzemski, Leszek. Information systems architecture and technology: Information models, concepts, tools and applications. Wrocław: Oficyna Wydawnicza Politechniki Wrocławskiej, 2006.

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5

Kosecki, Andrzej. Stochastyczne modele sieciowe przy planowaniu zadań budowlanych. Kraków: Politechnika Krakowska, 1989.

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6

Dioguardi, Gianfranco. Network enterprises: The evolution of organizational models from guilds to assembly lines to innovation clusters. New York: Springer, 2010.

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7

International, Conference on FRIEND (3rd 1997 Postojna Slovenia). FRIEND'97: Regional hydrology : concepts and models for sustainable water resource management. Wallingford: International Association of Hydrological Sciences, 1997.

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8

Schöbel, Anita. Optimization in public transportation: Stop location, delay management and tariff zone design in a public transportation network. New York: Springer, 2006.

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9

Wallace, Brett Patrick. Evaluation of travel demand management strategies in the trip generation phase of a network-based modeling approach. [Olympia]: Washington State Dept. of Transportation, 1998.

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10

Poland) ISAT Scientific School (25th 2004 Wrocław. Information systems architecture and technology, ISAT 2004: Proceedings of the 25th International Scientific School : information models, concepts, tools and applications. Edited by Grzech Adam, Wilimowska Zofia, and Politechnika Wrocławska. Wrocław: Oficyna Wydawnicza Politechniki Wrocławskiej, 2004.

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Частини книг з теми "Network management models":

1

Casares-Giner, Vicente, Vicent Pla, and Pablo Escalle-García. "Mobility Models for Mobility Management." In Network Performance Engineering, 716–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-02742-0_30.

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2

Jabłoński, Adam. "The Economization of Network Business Models." In Management of Network Organizations, 169–80. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17347-4_12.

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3

Shen, Jun, and Yun Yang. "RDF-Based Knowledge Models for Network Management." In Integrated Network Management VIII, 123–26. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-0-387-35674-7_15.

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4

Grégoire, J.-Ch. "Models and Support Mechanisms for Distributed Management." In Integrated Network Management IV, 17–28. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-0-387-34890-2_2.

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5

García-Pont, Carlos. "Network Analysis and Corporate Alliances." In Statistical Models for Strategic Management, 345–63. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-2614-5_16.

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6

Masuda, Takeshi. "Process Management and Control for Heterogeneous Domain Models." In Integrated Network Management VIII, 127–30. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-0-387-35674-7_16.

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7

Akishin, Vladimir, Alex Goldstein, and Boris Goldstein. "Cognitive Models for Access Network Management." In Lecture Notes in Computer Science, 375–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67380-6_34.

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8

Štefko, Róbert, and Peter Gallo. "Using Management Tools to Manage Network Organizations and Network Models." In Management of Network Organizations, 249–63. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17347-4_17.

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9

François, Jérôme, Radu State, and Olivier Festor. "Malware Models for Network and Service Management." In Inter-Domain Management, 192–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72986-0_23.

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Eberhardt, Rolf, Sandro Mazziotta, and Dominique Sidou. "Design and Testing of Information Models in a Virtual Environment." In Integrated Network Management V, 461–72. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-0-387-35180-3_34.

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Тези доповідей конференцій з теми "Network management models":

1

Han, Wonkyu, Hongxin Hu, Ziming Zhao, Adam Doupé, Gail-Joon Ahn, Kuang-Ching Wang, and Juan Deng. "State-aware Network Access Management for Software-Defined Networks." In SACMAT 2016: The 21st ACM Symposium on Access Control Models and Technologies. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2914642.2914643.

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2

Fallon, Liam, John Keeney, and Sven van der Meer. "Distributed Management Information Models." In 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). IEEE, 2017. http://dx.doi.org/10.23919/inm.2017.7987306.

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3

Plyaskina, Nina. "Economic System Management Using Network Models." In 2021 17th International Asian School-Seminar "Optimization Problems of Complex Systems (OPCS). IEEE, 2021. http://dx.doi.org/10.1109/opcs53376.2021.9588666.

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4

Rubio-Medrano, Carlos E., Ziming Zhao, Adam Doupe, and Gail-Joon Ahn. "Federated Access Management for Collaborative Network Environments." In SACMAT '15: 20th ACM Symposium on Access Control Models and Technologies. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2752952.2752977.

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5

Garroppo, R. G., S. Giordano, G. Nencioni, and M. G. Scutella. "Network Power Management: Models and Heuristic Approaches." In 2011 IEEE Global Communications Conference (GLOBECOM 2011). IEEE, 2011. http://dx.doi.org/10.1109/glocom.2011.6133918.

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6

Marcek, Dusan. "Statistical and granular neural network models in managerial prediction systems." In International Conference on Information Management and Management Engineering. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/imme140251.

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7

Dongqi Fu and Liri Fang. "Application of quantitative data models in network management." In 2016 2nd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2016. http://dx.doi.org/10.1109/compcomm.2016.7924682.

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8

Yingdong Lu. "Workforce Management and Optimization using Stochastic Network Models." In 2006 IEEE International Conference on Service Operations and Logistics, and Informatics. IEEE, 2006. http://dx.doi.org/10.1109/soli.2006.236839.

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9

Lu, Yingdong, Ana Radovanovic, and Mark S. Squillante. "Workforce Management and Optimization using Stochastic Network Models." In 2006 IEEE International Conference on Service Operations and Logistics, and Informatics. IEEE, 2006. http://dx.doi.org/10.1109/soli.2006.328911.

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10

"Time-based management of large scale network models." In 2009 IEEE/PES Power Systems Conference and Exposition. IEEE, 2009. http://dx.doi.org/10.1109/psce.2009.4840216.

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Звіти організацій з теми "Network management models":

1

Narayan, K., and D. Nelson. Remote Authentication Dial-In User Service (RADIUS) Usage for Simple Network Management Protocol (SNMP) Transport Models. RFC Editor, August 2009. http://dx.doi.org/10.17487/rfc5608.

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2

Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3178.

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Анотація:
The authors of the given article continue the series presented by the 2018 paper “Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot”. This time, they consider mathematical informatics as the basis of higher engineering education fundamentalization. Mathematical informatics deals with smart simulation, information security, long-term data storage and big data management, artificial intelligence systems, etc. The authors suggest studying basic principles of mathematical informatics by applying cloud-oriented means of various levels including those traditionally considered supplementary – spreadsheets. The article considers ways of building neural network models in cloud-oriented spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.
3

Brand, John H., and George W. Hartwig. Management of Tactical Ad Hoc Networks With C2 Data Models. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada405063.

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4

Harrington, D., and W. Hardaker. Transport Security Model for the Simple Network Management Protocol (SNMP). RFC Editor, June 2009. http://dx.doi.org/10.17487/rfc5591.

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5

Harrington, D., J. Salowey, and W. Hardaker. Secure Shell Transport Model for the Simple Network Management Protocol (SNMP). RFC Editor, June 2009. http://dx.doi.org/10.17487/rfc5592.

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6

Galvin, J., and K. McCloghrie. Administrative Model for version 2 of the Simple Network Management Protocol (SNMPv2). RFC Editor, April 1993. http://dx.doi.org/10.17487/rfc1445.

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7

Hardaker, W. Transport Layer Security (TLS) Transport Model for the Simple Network Management Protocol (SNMP). RFC Editor, July 2011. http://dx.doi.org/10.17487/rfc6353.

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8

Hardaker, W. Transport Layer Security (TLS) Transport Model for the Simple Network Management Protocol (SNMP). RFC Editor, August 2010. http://dx.doi.org/10.17487/rfc5953.

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9

Wijnen, B., R. Presuhn, and K. McCloghrie. View-based Access Control Model (VACM) for the Simple Network Management Protocol (SNMP). RFC Editor, December 2002. http://dx.doi.org/10.17487/rfc3415.

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10

Wijnen, B., R. Presuhn, and K. McCloghrie. View-based Access Control Model (VACM) for the Simple Network Management Protocol (SNMP). RFC Editor, April 1999. http://dx.doi.org/10.17487/rfc2575.

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