Estimation of RTT and Loss Rate of WideArea Connections Using MPI Measurements Abstractxe2x80x94Scientific computations are expected to be increasingly distributed across widearea networks, and the Message Passing Interface (MPI) has been shown to scale to support their communications over long distances. These computations should account for certain network parameters to ensure an effective execution, for example, by avoiding highly congested and long connections. The execution times of MPI basic operations reflect the connection parameters, including the Round Trip Time (RTT) and loss rate. We describe five machine leaning methods to estimate the connection RTT and loss rate using execution times of MPI basic operations. We utilize execution time measurements of MPI Sendrecv operations collected over emulated 10 Gbps connections with 0366 ms roundtrip times, wherein the longest connection spans the globe, under up to 20% periodic losses. These methods provide disparate, namely, linear and nonlinear, and smooth and nonsmooth, estimates of RTT and loss rate. Our results show that accurate estimates can be generated at low loss rates but they become inaccurate at loss rates 10% and higher. Overall, these results constitute a case study of the strengths and limitations of machine learning methods in inferring networklevel parameters using applicationlevel measurements.

A Deep Learning Approach to Dynamic Passive RTT Prediction Model for TCP The RoundTrip Time (RTT) is a property of the path between a sender and a receiver communicating with Transmission Control Protocol (TCP) over an IP network and over the public Internet. The endtoend RTT value influences significantly the dynamics and performance of TCP, which is by far the most used communication protocol. Thus, in communication networks, RTT is an important network performance variable. By measuring the traffic at an intermediate node, a network operator or service provider can estimate the RTT and use the estimation to study and troubleshoot the perconnection characteristics and performance. This paper aims at improving the accuracy and timeliness of the RTT estimation, to help network operators improving their analysis. We propose and evaluate a novel deep learningbased model capable of dynamically predicting at realtime the RTT between the sender and receiver with high accuracy based on passive measurements collected at an intermediate node, taking advantage of the commonly used TCP timestamps. We validate extensively our prediction methodology in a controlled experimental testbed and in a realistic scenario on the Google Cloud platform. We show that our model, which is based on classical deep learning algorithms, gives reasonably effective stateoftheart performance results across multiple TCP congestion control variants. We also show that the model works well for transfer learning. Even though the RTT prediction model was trained on an emulated network, it performs well also when applied to a realistic scenario setting, as demonstrated in our experimental evaluation.