Bandwidth estimation for video streaming under percentile delay, jitter, and packet loss rate constraints using traces

Anjum, Bushra; Perros, Harry G.

doi:10.1016/j.comcom.2014.08.018

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…• Fuzzy inference system: FIS was used to determine updated (isi) based on the current (isi) and the three traffic difference (td) values of delay, jitter and percentage packet loss ratio. The traffic parameters delay, jitter and packet loss ratio were considered as the independent variables representing p values in regression equation (4). The sampling section was divided to sub-sections (s 1 , s 2 ,...s n ).…”

Section: Description Of Adaptive Sampling Methodsmentioning

confidence: 99%

“…The simulation ran for 800 seconds. The linear regression model shown in equation (4) was used to model traffic parameters for pre-and postsections of the inter-sampling interval (isi). The traffic parameter differences were measured using equation (6) to determine the magnitude of the traffic changes.…”

Section: Relative Error= Absolute (mentioning

confidence: 99%

“…Sampling is important in determining quality of service (QoS) for networks. The purpose of QoS is to either provide priority to certain time-sensitive applications (like video conferencing) or to guarantee an agreed level of service through resource (such as bandwidth) reservation [4] [5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Sampling Technique for Computer Network Traffic Parameters Using a Combination of Fuzzy System and Regression Model

Salama

Saatchi

Burke

2017

2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI)

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In order to evaluate the effectiveness of wired and wireless networks for multimedia communication, suitable mechanisms to analyse their traffic are needed. Sampling is one such mechanism that allows a subset of packets that accurately represents the overall traffic to be formed thus reducing the processing resources and time. In adaptive sampling, unlike fixed rate sampling, the sample rate changes in accordance with transmission rate or traffic behavior and thus can be more optimal. In this study an adaptive sampling technique that combines regression modelling and a fuzzy inference system has been developed. It adjusts the sampling according to the variations in the traffic characteristics. The method's operation was assessed using a computer network simulated in the NS-2 package. The adaptive sampling evaluated against a number of non-adaptive sampling gave an improved performance.

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Section: Description Of Adaptive Sampling Methodsmentioning

confidence: 99%

Section: Relative Error= Absolute (mentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Sampling Technique for Computer Network Traffic Parameters Using a Combination of Fuzzy System and Regression Model

Salama

Saatchi

Burke

2017

2017 Fourth International Conference on Mathematics and Computers in Sciences and in Industry (MCSI)

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“…More recently this problem has been recognized in data centers (Guo et al 2014). The effect of variability was also studied in Anjum and Perros (2015) with application to video-streaming. Most of the previous literature, however, focused only on large-timescale variability, where Markov-modulating models represent the random fluctuations of the environment.…”

Section: Introductionmentioning

confidence: 99%

Modelling large timescale and small timescale service variability

et al. 2019

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The performance of service units may depend on various randomly changing environmental effects. It is quite often the case that these effects vary on different timescales. In this paper, we consider small and large scale (short and long term) service variability, where the short term variability affects the instantaneous service speed of the service unit and a modulating background Markov chain characterizes the long term effect. The main modelling challenge in this work is that the considered small and long term variation results in randomness along different axes: short term variability along the time axis and long term variability along the work axis. We present a simulation approach and an explicit analytic formula for the service time distribution in the double transform domain that allows for the efficient computation of service time moments. Finally, we compare the simulation results with analytic ones.

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“…For broadband wired links, which usually transport traffic with a certain degree of aggregation, the batch extension of MAP rather than MAP itself is normally proposed as the appropriate model [30]. Nonetheless, for wireless links or when traffic comes from a single source, the MAP has proven to be sufficiently general [31,32]. Therefore, in our case we can safely restrict ourselves to the MAP.…”

Section: Characteristics Of the Channelmentioning

confidence: 99%

Discrete time analysis of cognitive radio networks with imperfect sensing and saturated source of secondary users

Alfa

Pla

Martínez-Bauset

et al. 2016

Computer Communications

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Sensing is one of the most challenging issues in cognitive radio networks. Selection of sensing parameters raises several tradeoffs between spectral efficiency, energy efficiency and interference caused to primary users (PUs). In this paper we provide representative mathematical models that can be used to analyze sensing strategies under a wide range of conditions. The activity of PUs in a licensed channel is modeled as a sequence of busy and idle periods, which are represented as alternating Markov phase renewal processes. The representation of the secondary users (SUs) behavior is also largely general: the duration of transmissions, sensing periods and the intervals between consecutive sensing periods are modeled by phase type distributions, which constitute a very versatile class of distributions. Expressions for several key performance measures in cognitive radio networks are obtained from the analysis of the model. Most notably, we derive the distribution of the length of an effective white space; the distributions of the waiting times until the SU transmits a given amount of data, through several transmission epochs or uninterruptedly; and the goodput when an interrupted SU transmission has to be restarted from the beginning due to the presence of a PU.

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Bandwidth estimation for video streaming under percentile delay, jitter, and packet loss rate constraints using traces

Cited by 10 publications

References 10 publications

Adaptive Sampling Technique for Computer Network Traffic Parameters Using a Combination of Fuzzy System and Regression Model

Adaptive Sampling Technique for Computer Network Traffic Parameters Using a Combination of Fuzzy System and Regression Model

Modelling large timescale and small timescale service variability

Discrete time analysis of cognitive radio networks with imperfect sensing and saturated source of secondary users

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