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Ralph Knag

dblp:29/8356 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Content delivery and video streaming · 67% Network measurement and analytics · 33%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Performance modeling and evaluation · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Content delivery and video streaming › video broadcasting
IPTV
0.112010
Characterizing Interactive Behavior in a Large-Scale Operational IPTV Environment · INFOCOM 2010
Content delivery and video streaming
video-on-demand
0.112010
Characterizing Interactive Behavior in a Large-Scale Operational IPTV Environment · INFOCOM 2010
Network measurement and analytics
workload characterization
0.112010
Characterizing Interactive Behavior in a Large-Scale Operational IPTV Environment · INFOCOM 2010
Performance modeling and evaluation
trace analysis
0.012010
Characterizing Interactive Behavior in a Large-Scale Operational IPTV Environment · INFOCOM 2010
Performance modeling and evaluation
workload characterization
0.012010
Characterizing Interactive Behavior in a Large-Scale Operational IPTV Environment · INFOCOM 2010

Methods — techniques the papers use, named apart from their topics

semi-markov model · 0.2parametric modeling · 0.2
YearPublicationVenuePosition
2010 Characterizing Interactive Behavior in a Large-Scale Operational IPTV Environment
abstract
We investigate the user viewing activity for broadcast TV, pre-recorded content using Digital Video Recording (DVR) and video on demand (VoD) in an IP-based content distribution environment. Advanced stream control functions (play, pause, skip, rewind, etc.) provide users with a high level of interactivity, but place demands on the distribution infrastructure (servers, network, home-network) that can be difficult to manage at large scale. To support system design as well as network capacity planning, it is necessary to have a good model of user interaction. Using traces from a well-provisioned operational environment with a large user population, we first characterize interactivity for broadcast TV, DVR and VoD. We then develop parametric models of individual users stream control operations for VoD. Our analysis shows that interactive behavior is adequately characterized by two semi-Markov models, one for weekdays and another for weekends. We propose a parametric model for the underlying sojourn time distributions and show that it results in a superior fit compared to well known distributions (generalized Pareto and Weibull). In order to validate that our models faithfully capture user behavior, we compare the workload that a VoD server experiences in response to actual traces and synthetic data generated from our proposed models.
Vijay Gopalakrishnan, Rittwik Jana, Ralph Knag, K. K. Ramakrishnan, Deborah F. Swayne, Vinay A. Vaishampayan
INFOCOM3