Paper Info
Title
Learning Network Traffic Dynamics Using Temporal Point Process
Abstract
Accurate modeling of network traffic has a wide variety of applications. In this paper, we propose Network Transmission Point Process (NTPP), a probabilistic deep machinery that models the traffic characteristics of hosts on a network and effectively forecasts the network traffic patterns, such as load spikes. Existing stochastic models relied on the network traffic being self-similar in nature, thus failing to account for traffic anomalies. These anomalies, such as short-term traffic bursts, are very prevalent in certain modern-day traffic conditions, e.g. datacenter traffic, thus refuting the assumption of self-similarity. Our model is robust to such anomalies since it effectively leverages the self-exciting nature of the bursty network traffic using a temporal point process model.On seven diverse datasets collected from the fields of cyberdefense exercises (CDX), website access logs, datacenter traffic, and P2P traffic, NTPP offers a substantial performance boost in predicting network traffic characteristics against several baselines, ranging from forecasting the network traffic volume to detecting traffic spikes. We also demonstrate an application of our model to a caching scenario, showing that it can be used to effectively lower the cache miss rate.
Year
Venue
Keywords
2019
ieee international conference computer and communications
Predictive models,History,Stochastic processes,Bandwidth,Load modeling,Internet of Things
Field
DocType
ISSN
Computer science,Point process,Stochastic process,Baseline (configuration management),Ranging,Bandwidth (signal processing),Stochastic modelling,Probabilistic logic,Learning network,Distributed computing
Conference
0743-166X
ISBN
Citations 
PageRank 
978-1-7281-0515-4
2
0.37
References 
Authors
0
4
Name
Order
Citations
PageRank
Avirup Saha1184.71
Niloy Ganguly21306121.03
Sandip Chakraborty310831.71
Abir De47515.05