Characterizing Per-Application Network Traffic Using Entropy

Petkov, Vladislav; Rajagopal, Ram

doi:10.1109/mascots.2011.65

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Using entropy to characterize per-application network traffic, Petkov et al [31] estimated the entropy of difference streaming scenarios and divided them into categories of entropy measurement from the lowest to the highest. The entropy rate of a sequence is a measure of how predictable a sequence is based on past observations.…”

Section: Related Workmentioning

confidence: 99%

A Fuzzy-Based Adaptive Streaming Algorithm for Reducing Entropy Rate of DASH Bitrate Fluctuation to Improve Mobile Quality of Service

Van

Park

Nam

et al. 2017

Entropy

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Dynamic adaptive streaming over Hypertext Transfer Protocol (HTTP) is an advanced technology in video streaming to deal with the uncertainty of network states. However, this technology has one drawback as the network states frequently and continuously change. The quality of a video streaming fluctuates along with the network changes, and it might reduce the quality of service. In recent years, many researchers have proposed several adaptive streaming algorithms to reduce such changes. However, these algorithms only consider the current state of a network. Thus, these algorithms might result in inaccurate estimates of a video quality in the near term. Therefore, in this paper, we propose a method using fuzzy logic and a mathematics moving average technique, in order to reduce mobile video quality fluctuation in Dynamic Adaptive Streaming over HTTP (DASH). First, we calculate the moving average of the bandwidth and buffer values for a given period. On the basis of differences between real and average values, we propose a fuzzy logic system to deduce the value of the video quality representation for the next request. In addition, we use the entropy rate of a bandwidth measurement sequence to measure the predictable/stabilization of our method. The experiment results show that our proposed method reduces video quality fluctuation as well as improves 40% of bandwidth utilization compared to existing methods.

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Section: Related Workmentioning

confidence: 99%

A Fuzzy-Based Adaptive Streaming Algorithm for Reducing Entropy Rate of DASH Bitrate Fluctuation to Improve Mobile Quality of Service

Van

Park

Nam

et al. 2017

Entropy

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“…In their papers, the complexity of wired and wireless traffic is analyzed and compared with fractal analysis results. Unlike the work of Riihijarvi et al's, Petkov et al [16] focused on the per-application flow traffic. The plug-in packet timing entropy (PPTEn) is used as an estimator to investigate the complexity of per-flow network traffic.…”

Section: Introductionmentioning

confidence: 98%

Predictability of Aggregated Traffic of Gateways in Wireless Mesh Network with AODV and DSDV Routing Protocols and RWP Mobility Model

Xiang

Chen

2014

Wireless Pers Commun

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When evaluating the performance of routing protocols in wireless mesh network (WMN), we need deeper analysis from the aspect of network traffic complexity to show how traffic characteristics are influenced by routing protocols and node mobility. The predictability of network traffic can be used as one metric of complexity and can be analyzed by multiscale entropy (MSE) method. With 20 different random waypoint (RWP) mobility scenarios and with destination sequenced distance vector (DSDV), a typical proactive protocol, and Ad hoc on-demand distance vector (AODV), a typical reactive protocol, the predictabilities of aggregated traffic of gateway in WMN are analyzed using MSE method to show how different routing protocols bear different mobility scenarios. The MSE results show that the aggregated traffic of gateway with DSDV is more difficult to be predicted than that with AODV for different mobility scenarios. The maxspeed parameter of RWP dominates the traffic predictability for AODV. Both of the pause time and the maxspeed parameters, have great influence on the traffic predictability for DSDV. The reasons lie in the behaviors of routing protocols, i.e., AODV has up-to-date paths while DSDV does not.

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