A Machine Learning Approach to End-to-End RTT Estimation and its Application to TCP

Astuto, Bruno; Veenstra, Kerry; Ballenthin, William; Lukin, Stephanie M.

doi:10.1109/icccn.2011.6006098

Cited by 22 publications

(11 citation statements)

References 11 publications

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“…Nunes et al [5] use the Experts Framework machine learning technique in order to provide accurate estimates of the round trip time (RTT) in TCP transfers. Their framework quickly adapts the predicted value based on average distance of previously predicted RTT from the actual value.…”

Section: Related Workmentioning

confidence: 99%

A machine learning approach for packet loss prediction in science flows

Giannakou

Dwivedi

Peisert

2020

Future Generation Computer Systems

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Science networks and their hosted applications require large and frequent data transfers, but these transfers are subject to network performance degradation, including queuing delays and packet drops. However, well known network dynamics along with limited instrumentation access complicate the creation of an accurate method that predicts different performance aspects of data transfers. In this study, we develop a lightweight machine learning tool to predict end-to-end packet retransmission in science flows of arbitrary size. We also identify the minimum set of necessary path and host measurements needed as input features in our predictor in order to achieve high accuracy. In our evaluation process our predictor demonstrated low training times and was able to provide accurate estimates (97-99%) for packet retransmissions of data transfers of arbitrary sizes. The results also manifest that the our solution was able to predict retransmit behavior reasonably well (66%) even for previously unseen data if training and testing datasets had similar statistics.

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Section: Related Workmentioning

confidence: 99%

A machine learning approach for packet loss prediction in science flows

Giannakou

Dwivedi

Peisert

2020

Future Generation Computer Systems

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“…Some classic machine-learning algorithms (e.g., Recurrent Neural Networks [5], Fixed-Share Experts Algorithm [6]) are utilized to estimate the RTT based on the collected traces, with the training and validation phases.…”

Section: B Training Black-boxesmentioning

confidence: 99%

“…Belhaj et al [5] constructed a mathematical model of the proposed Recurrent Neural Networks (RNNs), with two phases, (i.e., a learning process characterizing the RTT and a validation phase) to estimate RTT. A machine-learning technique known as the Fixed-Share Experts Algorithm was used by Nunes et al [6] and each of several "experts" provides an estimated value. The weighted average of these estimate values is used to estimate the final RTT, with the weights updated after every RTT measurement.…”

Section: Introductionmentioning

confidence: 99%

Guyot: a hybrid learning- and model-based RTT predictive approach

Wang

Sun

2015

2015 IEEE International Conference on Communications (ICC)

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Knowing the Round-Trip Time (RTT) between a client and a server is important for an online interactive multimedia service to provide satisfactory quality of user experience. Due to the intrinsic dynamics of the topology and routing strategies in the Internet, it is however challenging to predict the RTT accurately from limited information, e.g., only the IP pair of the client and server. To address this challenge, we propose Guyot, a hybrid learning-and model-based approach to predict RTT, which requires significantly smaller amount of data to be collected than traditional approaches, while achieving a similar prediction accuracy. Our design is based on a largescale measurement study from a content provider's perspective. Based on an information gain analysis, we design a hybrid RTT prediction approach involving two types of predictions: (1) Learning-based prediction: We train a decision tree to predict RTT between IP pairs with large geographic distance, requiring only a small set of features to be collected. (2) Model-based prediction: We use a model-based framework to predict RTT between IP pairs with small distance, providing an accurate RTT prediction over time. By strategically dividing RTT prediction tasks to these two types according to the distance of the inferred geo-locations of the IPs, our prediction approach can scale with satisfactory accuracy. Our experiments further confirm the superiority of our design.

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“…Another study developed a novel method (expert framework) to estimate the end-to-end round-trip time using a machine learning approach. The simulation of this expert framework based on machine learning could adapt to changes in round-trip time, thereby reducing the number of transmitted packets while increasing the throughput [18]. As a part of machine learning, clustering was used in congestion control for a vehicular ad-hoc network that included three parts, namely, detecting congestion, clustering messages, and controlling data congestion.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

et al. 2019

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The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

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A Machine Learning Approach to End-to-End RTT Estimation and its Application to TCP

Cited by 22 publications

References 11 publications

A machine learning approach for packet loss prediction in science flows

A machine learning approach for packet loss prediction in science flows

Guyot: a hybrid learning- and model-based RTT predictive approach

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

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