Gaussian semi-Markov model based on real video multimedia traffic

Fowler, Scott; Sarfraz, Jalal; Abbas, Muhammad Muddassir; Angelakis, Vangelis

doi:10.1109/icc.2015.7249437

Cited by 3 publications

(2 citation statements)

References 12 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They evaluated the probability that channels being busy, also developed the expected waiting times and the expected number of channel switches. Fowler et all in [19] described the behavior of the LTE video call (ex. Skype video call) and video streaming traffic in heterogeneous real environment using gaussian mixture model.…”

Section: Related Workmentioning

confidence: 99%

“…they showed that the bursty traffic introduced in [23] is the best distribution to model various application types due to its various parameters. The model can represent the bursty traffic (with the idle and active periods) as in Figure 2 and also produces the self-similarity property which presents many modes of use (continuous flow, Bulk arrival, Poisson arrival etc) [17,19]. λ p := the maximum rate that a device can send during an active period.…”

Section: Lte Network System Modelmentioning

confidence: 99%

See 1 more Smart Citation

Influence of various application types on the performance of LTE mobile networks

Agamy

Mohamed

2020

IJECE

View full text Add to dashboard Cite

Modern mobile internet networks are becoming heavier and denser. Also it is not regularly planned, and becoming more heterogeneous. The explosive growth in the usage of smartphones poses numerous challenges for LTE cellular networks design and implementation. The performance of LTE networks with bursty and self-similar trafﬁc has become a major challenge. Accurate modeling of the data generated by each connected wireless device is important for properly investigating the performance of LTE networks. This paper presents a mathematical model for LTE networks using queuing theory considering the inﬂuence of various application types. Using sporadic source trafﬁc feeding to the queue of the evolved node B and with the exponential service time assumption, we construct a queuing model to estimate the performance of LTE networks. We use the performance model presented in this paper to study the inﬂuence of various application categories on the performance of LTE cellular networks. Also we validate our model with simulation using NS3 simulator with different scenarios.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Lte Network System Modelmentioning

confidence: 99%

Influence of various application types on the performance of LTE mobile networks

Agamy

Mohamed

2020

IJECE

View full text Add to dashboard Cite

show abstract

Malware Detection via Machine Learning and Recognition of Non Stationary Tasks

Alagrash

Badih

Rrushi

2020

2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf

View full text Add to dashboard Cite

Intelligence of Smart Systems: Model, Bounds, and Algorithms

Huang

2017

IEEE/ACM Trans. Networking

View full text Add to dashboard Cite

We present a general framework for understanding system intelligence, i.e., the level of system smartness perceived by users, and propose a novel metric for measuring intelligence levels of dynamical human-in-the-loop systems, defined to be the maximum average reward obtained by proactively serving user demands, subject to a resource constraint. Our metric captures two important elements of smartness, i.e., being able to know what users want and pre-serve them, and achieving good resource management while doing so. We provide an explicit characterization of the system intelligence, and show that it is jointly determined by user demand volume (opportunity to impress), demand correlation (user predictability), and system resource and action costs (flexibility to pre-serve).We then propose an online learning-aided control algorithm called Learning-aided Budget-limited Intelligent System Control (LBISC). We show that LBISC achieves an intelligence level that is within O(N (T ) − 1 2 + ) of the highest level, where N (T ) represents the number of data samples collected within a learning period T and is proportional to the user population size in the system, while guaranteeing an O(max(N (T ) − 1 2 / , log(1/ ) 2 )) average resource deficit. Moreover, we show that LBISC possesses an O(max(N (T ) − 1 2 / , log(1/ ) 2 ) + T ) convergence time, which is much smaller compared to the Θ(1/ ) time required for nonlearning based algorithms. The analysis of LBISC rigorously quantifies the impacts of data and user population (captured by N (T )), learning (captured by our learning method), and control (captured by LBISC) on achievable system intelligence, and provides novel insight and guideline into designing future smart systems.

show abstract

Gaussian semi-Markov model based on real video multimedia traffic

Cited by 3 publications

References 12 publications

Influence of various application types on the performance of LTE mobile networks

Influence of various application types on the performance of LTE mobile networks

Malware Detection via Machine Learning and Recognition of Non Stationary Tasks

Intelligence of Smart Systems: Model, Bounds, and Algorithms

Contact Info

Product

Resources

About