Large-Scale Mobile Traffic Analysis: A Survey
نویسندگان
چکیده
منابع مشابه
Large Scale Traffic Simulations
SFI WORKING PAPER: 1996-10-080 SFI Working Papers contain accounts of scientific work of the author(s) and do not necessarily represent the views of the Santa Fe Institute. We accept papers intended for publication in peer-reviewed journals or proceedings volumes, but not papers that have already appeared in print. Except for papers by our external faculty, papers must be based on work done at ...
متن کاملMontra: A large-scale DHT traffic monitor
This paper presents a new technique, called Montra, for accurately capturing traffic in a widely deployed DHT. The basic idea is to make the traffic monitors minimally visible to participating peers to avoid disruption in the system. We describe how Montra leverages the required redundancy in published content and routing to minimize disruption of the system. Validations of Montra over two wide...
متن کاملLarge Scale Structure survey
Galaxy groups and low-mass clusters provide important laboratories in which to study X-ray gas physics and the interplay between galaxy evolution and environmental effects. The X-ray Multi-Mirror (XMM) Large Scale Structure (LSS) survey has currently imaged 5 deg 2 to a nominal extended source flux limit of order 10 −14 ergs s −1 cm −2 and is dominated numerically by low-mass groups and cluster...
متن کاملPerformance Analysis of Large-Scale IP Networks Considering TCP Traffic
In this paper, we propose a novel analysis method for large-scale networks with consideration of the behavior of the congestion control mechanism of TCP. In the analysis, we model the behavior of TCP at end-host and network link as an independent system, and combine them into a single system in order to analyze the entire network. Using this analysis, we can analyze a large-scale network, i.e. ...
متن کاملParallelized Unsupervised Feature Selection for Large-Scale Network Traffic Analysis
In certain domains, where model interpretability is highly valued, feature selection is often the only possible option for dimensionality reduction. However, two key problems arise. First, the size of data sets today makes it unfeasible to run centralized feature selection algorithms in reasonable amounts of time. Second, the impossibility of labeling data sets rules out supervised techniques. ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Communications Surveys & Tutorials
سال: 2016
ISSN: 1553-877X,2373-745X
DOI: 10.1109/comst.2015.2491361