QoS control framework for content satisfaction in ubiquitous multimedia computing

Law, K.L.E.; So, Sunny

doi:10.1007/s12652-011-0077-8

Cited by 3 publications

(2 citation statements)

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“…Since the file sharing applications consume large amounts of bandwidth, delay and jitter often happen to the Internet, which are the main factors of influencing the quality of service (QoS) (Law and So 2012). When deploying network traffic shaping and policing, ''elephant'' flows of file sharing applications must be correctly identified.…”

Section: Introductionmentioning

confidence: 99%

SmoteAdaNL: a learning method for network traffic classification

Liu

Wang

Tao

2015

J Ambient Intell Human Comput

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Machine learning based network traffic classification is a critical technique for network management, and has attracted much attention. Recently, most of the researchers focus on achieving high flow classification accuracy (FCA). However the amount of ''mice'' flows is more than that of ''elephant'' flows in the Internet, these classifiers hence are more suitable for ''mice'' flows, but have low byte classification accuracy (BCA). To address this issue, the notion of byte misclassification is firstly explored. According to the exploration that most misclassified bytes belong to the minority class, a novel method of network traffic classification is proposed by combining the data re-sampling and ensemble learning algorithms. To enhance the classification accuracy of the minority class, the data re-sampling algorithm is employed to increase the number of minority class flows. The data re-sampling however will change the data distribution and degrade the generalization of a classifier. A boosting-style ensemble learning algorithm with the consideration of ensemble diversity hence is employed to improve the generalization. The experiments conducted on the real-world traffic datasets show that the proposed method achieves over 90 % BCA and 96 % FCA on average, and improves about 7.15 % BCA by comparing with the existing methods.

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Section: Introductionmentioning

confidence: 99%

SmoteAdaNL: a learning method for network traffic classification

Liu

Wang

Tao

2015

J Ambient Intell Human Comput

View full text Add to dashboard Cite

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“…This approach could deal with conflict during allocation process of competitive resources effectively and significant narrow the gap between each user's experience. Finally, simulation results are analyzed to show our mechanism performs well in process of device set selection according to contexts of both network environment and QoS indicators of service [8][9][10]. After that, the selected device set will compose a CP (capable piconet) [11] to support the requested ubiquitous service.…”

Section: Introductionmentioning

confidence: 99%