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Wyświetlanie 1-3 z 3
Tytuł:
Resilient critical infrastructure management with a service oriented architecture: A test case using airport collaborative decision making
Autorzy:
Hall-May, M.
Surridge, M.
Nossal-Tüyeni, R.
Powiązania:
https://bibliotekanauki.pl/articles/907800.pdf
Data publikacji:
2011
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sprężystość
QoS
SOA
infrastruktura krytyczna
resilience
critical infrastructure
SLA
Opis:
The SERSCIS approach aims to support the use of interconnected systems of services in Critical Infrastructure (CI) applications. The problem of system interconnectedness is aptly demonstrated by 'Airport Collaborative Decision Making' (A-CDM). Failure or underperformance of any of the interlinked ICT systems may compromise the ability of airports to plan their use of resources to sustain high levels of air traffic, or to provide accurate aircraft movement forecasts to the wider European air traffic management systems. The proposed solution is to introduce further SERSCIS ICT components to manage dependability and interdependency. These use semantic models of the critical infrastructure, including its ICT services, to identify faults and potential risks and to increase human awareness of them. Semantics allow information and services to be described in a way that makes them understandable to computers. Thus when a failure (or a threat of it) is detected, SERSCIS components can take action to manage the consequences, including changing the interdependency relationships between services. In some cases, the components will be able to take action autonomously, e.g., to manage 'local' issues such as the allocation of CPU time to maintain service performance, or the selection of services where there are redundant sources available. In other cases the components will alert human operators so they can take action instead. The goal of this paper is to describe a Service Oriented Architecture (SOA) that can be used to address the management of ICT components and interdependencies in critical infrastructure systems.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2011, 21, 2; 259-274
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Ensemble learning techniques for transmission quality classification in a Pay&Require multi-layer network
Autorzy:
Żelasko, Dariusz
Pławiak, Paweł
Powiązania:
https://bibliotekanauki.pl/articles/1838182.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
Pay&Require
ensemble learning
machine learning
resource allocation
QoS
uczenie zespołowe
uczenie maszynowe
alokacja zasobu
Opis:
Due to a continuous increase in the use of computer networks, it has become important to ensure the quality of data transmission over the network. The key issue in the quality assurance is the translation of parameters describing transmission quality to a certain rating scale. This article presents a technique that allows assessing transmission quality parameters. Thanks to the application of machine learning, it is easy to translate transmission quality parameters, i.e., delay, bandwidth, packet loss ratio and jitter, into a scale understandable by the end user. In this paper we propose six new ensembles of classifiers. Each classification algorithm is combined with preprocessing, cross-validation and genetic optimization. Most ensembles utilize several classification layers in which popular classifiers are used. For the purpose of the machine learning process, we have created a data set consisting of 100 samples described by four features, and the label which describes quality. Our previous research was conducted with respect to single classifiers. The results obtained now, in comparison with the previous ones, are satisfactory—high classification accuracy is reached, along with 94% sensitivity (overall accuracy) with 6/100 incorrect classifications. The suggested solution appears to be reliable and can be successfully applied in practice.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 135-153
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hybrid scheduler for many task computing in big data systems
Autorzy:
Vasiliu, L.
Pop, F.
Negru, C.
Mocanu, M.
Cristea, V.
Kolodziej, J.
Powiązania:
https://bibliotekanauki.pl/articles/907647.pdf
Data publikacji:
2017
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
many task computing
scheduling heuristics
QoS
big data system
simulation
obliczenia wielofunkcyjne
szeregowanie zadań
duży zbiór danych
Opis:
With the rapid evolution of the distributed computing world in the last few years, the amount of data created and processed has fast increased to petabytes or even exabytes scale. Such huge data sets need data-intensive computing applications and impose performance requirements to the infrastructures that support them, such as high scalability, storage, fault tolerance but also efficient scheduling algorithms. This paper focuses on providing a hybrid scheduling algorithm for many task computing that addresses big data environments with few penalties, taking into consideration the deadlines and satisfying a data dependent task model. The hybrid solution consists of several heuristics and algorithms (min-min, min-max and earliest deadline first) combined in order to provide a scheduling algorithm that matches our problem. The experimental results are conducted by simulation and prove that the proposed hybrid algorithm behaves very well in terms of meeting deadlines.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2017, 27, 2; 385-399
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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