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Estimating the reliability of the elements of cloud services

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Cloud technologies are a very considerable area that influence IT infrastructure, network services and applications. Research has highlighted difficulties in the functioning of cloud infrastructure. For instance, if a server is subjected to malicious attacks or a force majeure causes a failure in the cloud’s service, it is required to determine the time that it takes the system to return to being fully functional after the crash. This will determine the technological and financial risks faced by the owner and end users of cloud services. Therefore, to solve the problem of determining the expected time before service is resumed after a failure, we propose to apply Markovian queuing systems, specifically a model of a multi-channel queuing system with Poisson input flow and denial-of-service (breakdown).
Rocznik
Strony
65--80
Opis fizyczny
Bibliogr. 22 poz., rys.
Twórcy
  • Department of Economic and Mathematical Modelling, Kyiv National Economic University named after Vadym Hetman, 54/1 Prospect Peremogy, 03057 Kyiv, Ukraine
autor
  • Department of Information Management, Kyiv National Economic University named after Vadym Hetman, 54/1 Prospect Peremogy, 03057 Kyiv, Ukraine
Bibliografia
  • [1] ALTMAN E., AVRACHENKOV K., BARAKAT C., A stochastic model of TCP/IP with stationary random losses, Proc. ACM SIGCOMM Computer Communication Review, 2000, 30 (4), 231–242.
  • [2] BACCELLI F., HONG D., AIMD, fairness and fractal scaling of TCP traffic, Proc. IEEE INFOCOM, New York 2000, 1, 229–238.
  • [3] BINI D., LATOUCHE G., MEINI B., Numerical Methods for Structured Markov Chains, Oxford University Press, New York 2005.
  • [4] BRANDT A., BRANDT M., On the M(n)/M(n)/s queue with impatient calls, Perf. Eval., 1999, 35 (1–2), 1–18.
  • [5] BRUNEEL H., KIM B., Discrete-Time Models for Communication Systems Including ATM, Kluwer Academic, Boston 1993.
  • [6] BUCHHOLZ P., A class of hierarchical queueing networks and their analysis, Queuing Syst., 1994, 15 (1–4), 59–80.
  • [7] BUCKLEW J., Large Deviation Techniques in Decision, Simulation and Estimation, Wiley, New York 1990.
  • [8] BUNKE H., CAELLI T., Hidden Markov models. Applications in computer vision, World Scientific, Singapore 2001, 244.
  • [9] BUZACOTT J., SHANTHIKUMAR J., Stochastic Models of Manufacturing Systems, Prentice-Hall, New Jersey 1993.
  • [10] CHANG C., Performance Guarantees in Communication Networks, Springer-Verlag, London 2000.
  • [11] CHEN H., YAO D., Fundamentals of Queueing Networks. Performance, Asymptotics and Optimization, Springer-Verlag, New York 2001.
  • [12] GNEDENKO B., KOVALENKO I., Introduction to Queueing Theory, Birkhauser Boston, Inc., Cambridge 1968.
  • [13] KLEINROCK L., Queueing Systems. Vol. 1. Computer Systems Modelling Fundamentals, 2nd Ed., Wiley, 2009, 576.
  • [14] LE BOUDEC J., THIRAN P., Network Calculus.: A Theory of Deterministic Queueing Systems for the Internet, Springer-Verlag, Berlin 2001.
  • [15] MARINESCU D., Cloud Computing: Cloud vulnerabilities, TechNet Magazine, July 2013. Available at: https://technet.microsoft.com/en-au/library/dn271884.aspx
  • [16] RIORDAN J., Stochastic Service Systems, Wiley, New York 1962.
  • [17] SMITH J., TAN B., Handbook of Stochastic Models and Analysis of Manufacturing System Operations, Springer-Verlag, New York 2013, 373.
  • [18] SRIDHAR T., Cloud Computing – a primer. Part 1. Models and Technologies, Int. Protocol J., 2009, 12 (3). Available at: https://www.cisco.com/c/en/us/about/press/internet-protocol-journal/back-issues/table-contents-45/123-cloud1.html
  • [19] SRIDHAR T., Cloud Computing – a primer. Part 2. Infrastructure and Implementation Topics, Int. Protocol J., 2009, 12 (3). Available at: ttps://www.cisco.com/c/en/us/about/press/internet-protocol-journal/back-issues/ table-contents-46/124-cloud2.html
  • [20] SRIKANT R., YING L., Communication Networks. An Optimization, Control and Stochastic Networks Perspective, Cambridge University Press, Cambridge 2013, 363.
  • [21] Estimate capacity and performance for Web Content Management (SharePoint Server 2013), TechNet Magazine, December 2016. Available at: https://technet.microsoft.com/en-gb/library/gg398060.aspx
  • [22] A language and environment for statistical computing, R Core Team R, R Foundation for Statistical Computing, Vienna 2015. Available at: https://www.R-project.org/
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3067ea9a-1f55-4478-af72-6df88239d053
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