Preparing databases for network traffic monitoring

Vokorokos, Liberios; Pekár, Adrián; Ádám, Norbert

doi:10.1109/sami.2012.6208950

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…The actual version of the evaluating application uses the MongoDB. As a connector between the evaluating application and the web interface the Redis database service [15] was used. To improve the speed and performance, a listener was also implemented which is listening on a pre-defined port to receive only messages designated for the concrete evaluating modules.…”

Section: Design Of the Proposed Solutionmentioning

confidence: 99%

Reducing Time-Related Demands of Flow-Based Network Monitoring Tools Emerging during Data Processing

Pekár

Chovanec

2016

IJSPS

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This paper deals with the time-related demands of flow-based network monitoring tools emerging during data analysis and visualization. Nowadays, most of the monitoring tools are based on flow measurement. Despite its popularity it is still surrounded by several issues, especially in case of data analysis and visualization. As networks are continuously growing in size, connected users and the volume of transmitted traffic, monitoring tools generate more and more measurement data. In consequence, processing the queries by the storage systems and the subsequent visualization of the results by the analyzing applications represent an excessive response time of the flow-based monitoring tools, thus their operation and management are becoming complex. In this paper we provide a solution for mitigating the time-related demands of flow-based monitoring tools emerging during data analysis and visualization. 

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Section: Design Of the Proposed Solutionmentioning

confidence: 99%

Reducing Time-Related Demands of Flow-Based Network Monitoring Tools Emerging during Data Processing

Pekár

Chovanec

2016

IJSPS

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“…There are several related research studies that may be referenced to achieve multidimensional traffic analysis for comprehensive and detailed understanding of long‐term traffic data. Wang et al .…”

Section: Requirement Analysis and Related Workmentioning

confidence: 99%

“…Third is the lack of an effective method to extract useful information from large volumes of traffic data that are accumulated over a long period of time. There are several related research studies [19][20][21][22][23][24] that may be referenced to achieve multidimensional traffic analysis for comprehensive and detailed understanding of long-term traffic data. Wang et al [19] proposed a data-warehousing and data-mining approach to discover comprehensive and detailed information in BitTorrent P2P application.…”

Section: Related Work For Multidimensional Long-term Traffic Analysismentioning

confidence: 99%

“…Data warehouses and OLAP tools are based on a multidimensional data model. This model views data in the form of a data cube [23,24,[26][27][28][29]. A data cube allows data to be modeled and viewed in multiple dimensions.…”

Section: Multidimensional Data Cubementioning

confidence: 99%

“…The flow information contains IP address, port number, timing information such as start and end time of the flow, the number of packets and bytes in both directions, and application name. We used the data warehouse for the effective storage of long‐term massive traffic data, where the flow data are stored in the form of a round robin database to solve the increasing storage size as time goes on . We used the OLAP operation to retrieve information from the data warehouse.…”

Section: Netcube: a Comprehensive Traffic Analysis Modelmentioning

confidence: 99%

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NetCube: a comprehensive network traffic analysis model based on multidimensional OLAP data cube

Park

et al. 2012

Int J Network Mgmt

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SUMMARY Network traffic monitoring and analysis are essential for effective network operation and resource management. In particular, multidimensional analysis for long‐term traffic data is necessary for comprehensive understanding of the traffic trend and effective quality‐of‐service provision considering the extremely dynamic behavior of the current Internet, where various types of traffic occur from high‐speed network links and greatly increasing number of applications. However, only limited analysis results are provided, as the existing network traffic analysis tools and systems are developed and deployed focusing on their own specialized analysis purposes. Consequently, it is difficult to understand the network comprehensively and deeply, which increases the necessity for multilateral analysis of long‐term traffic data. In this paper, we propose a novel traffic analysis model for large volumes of Internet traffic accumulated over a long period of time. The NetCube, the proposed network traffic analysis model using online analytical processing (OLAP) on a multidimensional data cube, provides an easy and fast way to construct a multidimensional traffic analysis system for comprehensive and detailed analysis of long‐term traffic data by utilizing simple OLAP operations and powerful data‐mining techniques on various abstraction levels of traffic data to complete the analysis purpose. We validate the feasibility and applicability of the proposed NetCube traffic analysis model by implementing a traffic analysis system and applying it to our campus network. Copyright © 2012 John Wiley & Sons, Ltd.

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