Online Traffic Measurement and Analysis in Big Data: Comparative Research Review

Ibrahim, Lena T.; Hassan, Rosilah; Ahmad, Kamsuriah; Asat, Asrul Nizam; Omar, Halizah

doi:10.3844/ajassp.2016.420.431

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…Third, it can handle traffic measurement and analysis as a single packet. And finally, it can update traffic measurement functions such as (scaling network usage levels and providing different data segments) [4].…”

Section: A Hadoop Overviewmentioning

confidence: 99%

“…The section lists the diagram from seven-famed Internet movement estimation with examination devices in interpreting focuses and develop the plan of these tools used. As delineated in figure 3, the main traffic measurement and analysis tools are the following: Hadoop /Map Reduce, Tcpdump, Coral Reef, Snort, Tstat, Net Flow, and L7 Filter [4], [15]. 1) Hadoop/MapReduce: It is a software framework that is commonly utilized for a simple and easy application like writing.…”

Section: F Items Of Internet Traffic Measured and Analyzedmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of How Scalable Features in Hadoop / MapReduce by Internet Traffic Management

Jacob¹,

Tharwat²,

Fudzee³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Internet traffic monitoring is to measure and analyze the network bottlenecks to manage the online data are transferring processes efficiently. Various tools have been developed by using internet traffic measurement and internet traffic analysis tools, such as Hadoop. Activity measurement and adaptive examination represent the dynamics of information exchange. On the other hand, information exchange and dynamics measure movement in light of the system assets that can be accessed depending on the characteristics of the exchanged information. The main aim of this work is to apply scalable features of internet traffic measurement and analysis using Hadoop to understand the effects of these features on the speed of transferring data. This gives a new vision or opportunity to dynamically adapting the most suitable traffic measurement and analysis feature according to network capabilities and environment. This research employs Hadoop/Map Reduce as scalable internet traffic measurement and analysis tools. The simulation was conducted by using five personal computers; one as a server and four virtual computers as network nodes. Each computer has 2GB memory and 100GB storage. Five types of data segmentation are utilized 10 MB, 40MB, 64MB, 200MB, and500MB. The speed of the network is calculating in a megabit per second (Mbs) based upon the network speed on the number of allocated PCs (100 Mbs/4). The simulation is conducted to test the data transfer time based on various selections of network capabilities such as transferring extensive data through a network of medium and heavy usage.

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Section: A Hadoop Overviewmentioning

confidence: 99%

Section: F Items Of Internet Traffic Measured and Analyzedmentioning

confidence: 99%

Analysis of How Scalable Features in Hadoop / MapReduce by Internet Traffic Management

Jacob¹,

Tharwat²,

Fudzee³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Te Internet trafc measurement and analysis minimizes or avoids trafc congestion challenges [49], but prior to data transmission, the performance of the classifer must be verifed with various metrics. We present the results of the four deep learning models on three dataset that we have adapted.…”

Section: Experimentation and Evaluationmentioning

confidence: 99%

Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory, and Autoencoder

Wassie Geremew,

Ding

2023

Journal of Computer Networks and Communications

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Currently, the widespread of real-time applications such as VoIP and videos-based applications require more data rates and reduced latency to ensure better quality of service (QoS). A well-designed traffic classification mechanism plays a major role for good QoS provision and network security verification. Port-based approaches and deep packet inspection (DPI) techniques have been used to classify and analyze network traffic flows. However, none of these methods can cope with the rapid growth of network traffic due to the increasing number of Internet users and the growth of real-time applications. As a result, these methods lead to network congestion, resulting in packet loss, delay, and inadequate QoS delivery. Recently, a deep learning approach has been explored to address the time-consumption and impracticality gaps of the abovementioned methods and maintain existing and future traffics of real-time applications. The aim of this research is then to design a dynamic traffic classifier that can detect elephant flows to prevent network congestion. Thus, we are motivated to provide efficient bandwidth and fast transmission requirements to many Internet users using SDN capability and the potential of deep learning. Specifically, DNN, CNN, LSTM, and Deep autoencoder are used to build elephant detection models that achieve an average accuracy of 99.12%, 98.17%, and 98.78%, respectively. Deep autoencoder is also one of the promising algorithms that do not require human class labeler. It achieves an accuracy of 97.95% with a loss of 0.13. Since the loss value is closer to zero, the performance of the model is good. Therefore, the study has a great importance to Internet service providers, Internet subscribers, as well as for future researchers in this area.

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Application of the Methods for Monitoring of IP Networks for Studying Power Electronic Devices

Nedyalkov

Georgiev

2021

IEEE EUROCON 2021 - 19th International Conference on Smart Technologies

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Online Traffic Measurement and Analysis in Big Data: Comparative Research Review

Cited by 5 publications

References 32 publications

Analysis of How Scalable Features in Hadoop / MapReduce by Internet Traffic Management

Analysis of How Scalable Features in Hadoop / MapReduce by Internet Traffic Management

Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory, and Autoencoder

Application of the Methods for Monitoring of IP Networks for Studying Power Electronic Devices

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