End-to-end available bandwidth: measurement methodology, dynamics, and relation with TCP throughput
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Achieving moderate fairness for UDP flows by path-status classification
LCN '00 Proceedings of the 25th Annual IEEE Conference on Local Computer Networks
Distinguishing Congestion Losses from Wireless Transmission Losses: A Negative Result
IC3N '98 Proceedings of the International Conference on Computer Communications and Networks
Measurement and analysis of end-to-end delay and loss in the internet
Measurement and analysis of end-to-end delay and loss in the internet
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Perception-based approach to time series data mining
Applied Soft Computing
An agent-based dynamic routing strategy for automated material handling systems
International Journal of Computer Integrated Manufacturing
Real-time packet loss prediction based on end-to-end delay variation
IEEE Transactions on Network and Service Management
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There are certain performance parameters like packet delay, delay variation (jitter) and loss, which are decision factors for online quality of service (QoS) traffic routing. Although considerable efforts have been placed on the Internet to assure QoS, the dominant TCP/IP - like the best-effort communications policy - does not provide sufficient guarantee without abrupt change in the protocols. Estimation and forecasting end-to-end delay and its variations are essential tasks in network routing management for detecting anomalies. A large amount of research has been done to provide foreknowledge of network anomalies by characterizing and forecasting delay with numerical forecasting methods. However, the methods are time consuming and not efficient for real-time application when dealing with large online datasets. Application is more difficult when the data is missing or not available during online forecasting. Moreover, the time cost in statistical methods for trivial forecasting accuracy is prohibitive. Consequently, many researchers suggest a transition from computing with numbers to the manipulation of perceptions in the form of fuzzy linguistic variables. The current work addresses the issue of defining a delay approximation model for packet switching in communications networks. In particular, we focus on decision-making for smart routing management, which is based on the knowledge provided by data mining (informed) agents. We propose a historical symbolic delay approximation model (HDAX) for delay forecasting. Preliminary experiments with the model show good accuracy in forecasting the delay time--series as well as a reduction in the time cost of the forecasting method. HDAX compares favourably with the competing Autoregressive Moving Average (ARMA) algorithm in terms of execution time and accuracy.