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Uncertainty-driven ensemble forecasting of QoS in Software Defined Networks
2017
2017 IEEE Symposium on Computers and Communications (ISCC)
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Software Defined Networking (SDN) is the key technology for combining networking and Cloud solutions to provide novel applications. ...
In this paper, we propose an intelligent mechanism that agglomerates the benefits of SDNs with real-time 'Big Data' forecasting analytics. ...
INTRODUCTION Software Defined Networks (SDNs) management is an emerging network paradigm that has proven to confront a number of constrained topics, e.g., static configuration of network components, in ...
doi:10.1109/iscc.2017.8024701
dblp:conf/iscc/KolomvatsosAMNH17
fatcat:kxgtjjweq5dlxkvg5jbzlotnza
Orchestrating BigData Analysis Workflows
2017
IEEE Cloud Computing
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ACKNOWLEDGEMENT The authors would like thank Mazin Yousif (EIC, IEEE Cloud Computing) and Ivona Brandic (TU Wien, Austria) for their valuable feedback, which significantly contributed to improving quality of ...
following run-time QoS prediction modelling uncertainties: 1) it is difficult to estimate activity-specific data flow behaviours in terms of data volume to be analysed, data velocity, data processing ...
various types of uncertainties (e.g., changing data volume and velocity). ...
doi:10.1109/mcc.2017.55
fatcat:i7xqytpawnhbveohyc2kfwnx3q
Machine Learning Based Classifiers for QoE Prediction Framework in Video Streaming over 5G Wireless Networks
2023
Computers Materials & Continua
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With the brisk advancement in 5G network usage and the massive popularity of threedimensional video streaming, the quality of experience (QoE) of video in 5G systems has been receiving overwhelming significance ...
Recently, the combination of video services and 5G networks have been gaining attention in the wireless communication realm. ...
Acknowledgement: The authors would like to acknowledge Vellore Institute of Technology, Chennai, India, for their valuable support. ...
doi:10.32604/cmc.2023.036013
fatcat:hyeqdr35tfav3gpgtwdfkkcs74
Grid Technology Reliability for Flash Flood Forecasting: End-user Assessment
2011
Journal of Grid Computing
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The anticipation of extreme hydrological scenarios through rainfall-runoff models is still limited, mainly because of the high uncertainty of rainfall forecasts and of limited computing resources. ...
The flash flood forecasting is one of the most important challenges for research in hydrology. ...
Acknowledgements This work makes use of results produced by the Enabling Grids for E-science E project, a project co-funded by the ...
doi:10.1007/s10723-010-9173-9
fatcat:gxz6wunjrjfhzpinqumhf6u5ui
Cyber-Physical Microgrids: Toward Future Resilient Communities
[article]
2019
arXiv
pre-print
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Research trends in monitoring have recently shifted from normal situational awareness in forecasting, state estimation, and prediction to anomalies' analysis and cyber-physical attacks' detection to support ...
In addition, confounding the interpretation of research findings is the lack of a widely accepted definition, analytical methods, and metrics to consistently describe the resilience of power grids, especially ...
network QoS (quality of service). ...
arXiv:1912.05682v1
fatcat:nqmvi2rpmreate75cbrhm7bdtu
Survey on Machine Learning for Traffic-Driven Service Provisioning in Optical Networks
[article]
2022
arXiv
pre-print
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In these networks, traffic-driven service provisioning can address the problem of network over-provisioning and better adapt to traffic variations, while keeping the quality-of-service at the required ...
The evolution of service provisioning in optical networks is initially presented, followed by an overview of the ML techniques utilized for traffic-driven service provisioning. ...
networking, software defined networking, optical burst switching, etc. ...
arXiv:2209.05080v1
fatcat:wtqhw72z2bdbfg77bc5l2pihve
Machine Learning Based Network Traffic Predictive Analysis
2022
Review of Computer Engineering Research
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Because in the last decade, ML has had a tremendous impact on handling the massive amount of data. ...
The primary objective of this paper is to predict the network traffic using the machine learning (ML) models before the performance of the network start degrading. ...
Acknowledgement: PTC Software India Pvt. Ltd. contributed the data utilized in this paper. ...
doi:10.18488/76.v9i2.3065
fatcat:6j52jzkphrgkjp377prwnuy2cu
The Handbook of Engineering Self-Aware and Self-Expressive Systems
[article]
2015
arXiv
pre-print
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Drawing on the knowledge obtained from the previous investigations, we proposed a pattern driven methodology for engineering self-aware and self-expressive systems to assist in utilising the patterns and ...
The results reveal that our pattern driven methodology covers the main aspects of engineering self-aware and self-expressive systems, and that the resulted systems perform significantly better than the ...
The system must be aware of the changes in workload and deployment. This is the cause of dynamic and uncertainty in cloud The system should be able to aware of QoS interference. ...
arXiv:1409.1793v3
fatcat:3vatvmhqbbdqvcw5lnvg7c2ocu
Trust Management in the World of Cloud Computing. Past Trends and Some New Directions
2021
Scalable Computing : Practice and Experience
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We proposed an approach for verifying whether the right software is running for the correct services in a trusted manner by analyzing features generated from the output cloud processed data. ...
There is a lack of review on trust models in this research domain. ...
QoS trust is defined by the level of assurance in the node to deliver the requested service [37] . ...
doi:10.12694/scpe.v22i4.1952
fatcat:s2yz7ihudbdqnjkjml5yorbvim
Intelligent, smart and scalable cyber-physical systems
2019
Journal of Intelligent & Fuzzy Systems
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core of these systems through the development of self-adaptive and context-aware software. ...
Interactions occurring in the physical world are capable of changing the processinga behavior in the virtual world, in a causal relationship * Corresponding author. V. ...
Acknowledgments The guest editors would like to thank all reviewers for their efforts in reviewing manuscripts submitted to this special issue. We also thank the Editor-in-Chief, Dr. ...
doi:10.3233/jifs-179108
fatcat:4hghoxr4prccxjpfg5juwzoie4
AI and ML – Enablers for Beyond 5G Networks
2020
Zenodo
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In network diagnostics, attention is given to forecasting network conditions, characteristics and undesired events, such as security incidents. Estimating user location is part of network insights. ...
They are typically used to model complex relationships between input and output parameters of a system or to find patterns in data. ...
Cross-layer optimization framework using ML In this section, a framework for Autonomic Network Management (ANM) and network reconfiguration combining Software Defined Networks (SDN) with Software Defined ...
doi:10.5281/zenodo.4299895
fatcat:ngzbopfm6bb43lnrmep6nz5icm
Ensemble Subsurface Modeling Using Grid Computing Technology
2007
Second International Multi-Symposiums on Computer and Computational Sciences (IMSCCS 2007)
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Ensemble Kalman Filter (EnKF) uses a randomized ensemble of subsurface models for error and uncertainty estimation. ...
Two synthetic cases in reservoir studies indicate that the enhanced ResGrid efficiently performs EnKF inversions to obtain accurate, uncertainty-ware predictions on reservoir production. ...
or vulnerable network connection. ...
doi:10.1109/imsccs.2007.4392607
fatcat:scclzfuzargrdmxqlro5oxfeba
Ensemble Subsurface Modeling Using Grid Computing Technology
2007
Second International Multi-Symposiums on Computer and Computational Sciences (IMSCCS 2007)
Preserved Fulltext
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The file type is application/pdf.
Ensemble Kalman Filter (EnKF) uses a randomized ensemble of subsurface models for error and uncertainty estimation. ...
Two synthetic cases in reservoir studies indicate that the enhanced ResGrid efficiently performs EnKF inversions to obtain accurate, uncertainty-ware predictions on reservoir production. ...
or vulnerable network connection. ...
doi:10.1109/imsccs.2007.98
dblp:conf/imsccs/LiLWA07
fatcat:rjuicxx73vehflwpnwmyvbxxxy
Wireless industrial sensor networks: Framework for QoS assessment and QoS management
2006
ISA transactions
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The example focuses on WISN operating in a time-varying RF interference environment in order to manage application-driven QoS latency constraints. ...
This paper presents a framework that addresses Quality of Service ͑QoS͒ for industrial wireless sensor networks as a real-time measurable set of parameters within the context of feedback control, thereby ...
In ͓7͔, based on a least-square error fit to the entire ensemble of data collected from the factories, n = 2.2 and = 7.9 dB. ...
doi:10.1016/s0019-0578(07)60217-1
pmid:16856632
fatcat:wrhhp3tdmrhbrkeln6xxawtb64
A survey on Machine Learning Techniques for Routing Optimization in SDN
2021
IEEE Access
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The introduction of Software-Defined Networking (SDN) separated these planes, and provided additional features and tools to solve some of the problems of traditional network (i.e., latency, consistency ...
In conventional networks, there was a tight bond between the control plane and the data plane. ...
of self-driven networks [66] . ...
doi:10.1109/access.2021.3099092
fatcat:flp25cn2mbhohjxvuxgfupflny
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