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Identifier |
000413378 |
Title |
Farcast : improving forecasting via SDN |
Alternative Title |
Farcast: Βελτίωση του Forecasting μέσω των SDN |
Author
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Μπαμιεδάκης – Πανανός, Μιχαήλ Ν.
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Thesis advisor
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Δημητρόπουλος, Ξενοφώντας
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Reviewer
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Παπαδοπούλη, Μαρία
Μούχταρης, Αθανάσιος
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Abstract |
Forecasting in computer networks is the process of anticipating future traffic demands
based on present and past data and by analysis of trends. Network providers use forecasting
in order to examine whether the traffic is routed efficiently, also they use it for bandwidth
allocation and congestion control. In this work, we propose an approach, which we
call Farcast, for improving the accuracy of network traffic load forecasting using Software
Defined Networking (SDN) principles. In Farcast, the traffic load predictions for monitored
links are sent to an SDN controller. The SDN controller has a global view of the
network and monitors the network state in time frames called inform intervals. In these
intervals, the SDN controller receives messages from network switches, about the current
traffic flows traversing the network. Then, the SDN controller assesses whether the
predictions will be accurate or not, based on the current traffic demands. If there are imminent
inaccurate predictions, the controller ensures that the prediction error is reduced,
by re-routing, when possible, the flows in the network accordingly.
Farcast was implemented in a simulated SDN environment. The experiments simulate
traffic bursts that are difficult to predict with traditional forecasting approaches. We
compare the performance of the AutoRegressive Integrated Moving Average (ARIMA)
forecasting method with Farcast. The results show that Farcast reduces the Mean Absolute
Percentage Error (MAPE) of the traffic load predictions, by up to one order of
magnitude, when the goal is to satisfy the predictions for a single link in the network.
Moreover, when adjusting the load of multiple monitored links concurrently, Farcast reduces
the MAPE by up to 50%. Our approach can be deployed independently of the
machine learning algorithm used for the predictions.
For example, such a approach would be useful in the 40-Gigabit-capable Passive
Optical Network (XG-PON) standard. The PON is composed of Optical Netwok Units
(ONUs) and an Optical Line Terminator (OLT). The ONUs predict the future traffic load
and send bandwidth requests to the OLT, based on their predictions. The OLT performs
the dynamic bandwidth allocation based on the ONUs’ requests. In such schemes, the
dependability on forecasting is critical, as wrong predictions can lead to underutilization
of system resources or traffic congestion.
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Language |
English |
Subject |
ARIMA |
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Computer networks |
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OMNET |
Issue date |
2017-11-24 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/0/9/0/metadata-dlib-1513683799-149937-5016.tkl
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Views |
496 |