Towards Rapid Elephant Flow Dection Using Time Series Prediction for OTT Streaming

Broadcast television traditionally employs a unidirectional transmission path to deliver low latency, high-quality media to viewers. To expand their viewing choices, audiences now demand internet OTT (Over The Top) streamed media with the same quality of experience they have become accustomed to with traditional broadcasting. Media streaming over the internet employs elephant flow characteristics and suffers long delays due to the inherent and variable latency of TCP/IP. This paper proposes to perform rapid elephant flow detection on IP networks within 200ms using a data-driven temporal sequence prediction model, reducing the existing detection time by half. Early detection of media streams (elephant flows) as they enter the network allows the controller in a software-defined network to re-route the elephant flows so that the probability of congestion is reduced and the latency-sensitive mice flows can be given priority. We propose a two-stage machine learning method that encodes the inherent and non-linear temporal data and volume characteristics of the sequential network packets using an ensemble of Long Short-Term Memory (LSTM) layers, followed by a Mixture Density Network (MDN) to model uncertainty, thus determining when an elephant flow (media stream) is being sent within 200ms of the flow starting. We demonstrate that on two standard datasets, we can rapidly identify elephant flows and signal them to the controller within 200ms, improving the current count-minsketch method that requires more than 450ms of data to achieve comparable results.