An adaptive statistical sampling technique for computer network traffic

Dogman, A.; Saatchi, Reza; Al-Khayatt, Samir

doi:10.1109/csndsp16145.2010.5580380

Cited by 8 publications

(9 citation statements)

References 5 publications

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“…Adaptive sampling techniques are generally developed for a specific estimation parameter, such as packet loss [4] [5] or delay and jitter [6] [7]. A larger group of proposals are aimed at traffic characterization and SLA monitoring [8] [9] [10] [7]. Common adaptive techniques are usually based on Fuzzy Logic or Linear Prediction.…”

Section: Related Workmentioning

confidence: 99%

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A multiadaptive sampling technique for cost-effective network measurements

2013

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The deployment of efficient measurement solutions to assist network management tasks without interfering with normal network operation assumes a prominent role in today's high-speed networks attending to the huge amounts of traffic involved. From a myriad of proposals for traffic measurement, sampling techniques are particularly relevant contributing effectively for this purpose as only a subset of the overall traffic volume is handled for processing, preserving ideally the correct estimation of network statistical behavior. In this context, this paper proposes MuST-a multiadaptive sampling technique based on linear prediction, aiming at reducing significantly the measurement overhead and still assuring that traffic samples reflect the statistical characteristics of the global network traffic under analysis. Conversely to current sampling techniques, MuST is a multi and self-adaptive technique as both the sample size and interval between samples are self-adjustable parameters according to the ongoing network activity and the accuracy of prediction achieved. The tests carried out demonstrate that the proposed sampling technique is able to achieve accurate network estimations with reduced overhead, using

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Section: Related Workmentioning

confidence: 99%

“…M total is the average throughput of total traffic; M estimated is the average throughput of the sampled traffic [7].…”

Section: Correlationmentioning

confidence: 99%

A multiadaptive sampling technique for cost-effective network measurements

2013

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“…Traffic sampling techniques presently support a wide range of network tasks. As illustrated in Figure , examples of these tasks include the following: Network management – this involves short‐term, medium‐term, and long‐term planning and management of network operation, maintenance, and provisioning of network services ; Traffic engineering – this encompasses optimization of network performance, traffic characterization, traffic modeling, and control ; Performance evaluation – this involves evaluating the performance of management tools and protocols, fault tolerance, and network reliability ; Network security – this includes the detection of intrusion and anomalies, identification of botnets, and distributed denial of service attacks ; Service level agreement compliance – this implies the use of auditing tools for measuring and reporting service levels ; QoS control ‐ this involves measuring parameters such as packet delay and delay variation and loss . …”

Section: Related Workmentioning

confidence: 99%

“…This may involve a simple deterministic function as discussed in , in which a systematic selection scheme is used (presented in Section 4.2.1), where the sample size and the interval between samples are set at the beginning of the sampling process and remain invariant until the end. In the adaptive approach presented in , the interval between samples is decided dynamically based on the variance of an observed reference parameter while the sample size remains invariant.…”

Section: Taxonomy Proposalmentioning

confidence: 99%

Inside packet sampling techniques: exploring modularity to enhance network measurements

Silva

Carvalho

Lima

2016

Int J Communication

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Summary Traffic sampling is viewed as a prominent strategy contributing to lightweight and scalable network measurements. Although multiple sampling techniques have been proposed and used to assist network engineering tasks, these techniques tend to address a single measurement purpose, without detailing the network overhead and computational costs involved. The lack of a modular approach when defining the components of traffic sampling techniques also makes difficult their analysis. Providing a modular view of sampling techniques and classifying their characteristics is, therefore, an important step to enlarge the sampling scope, improve the efficiency of measurement systems, and sustain forthcoming research in the area. Thus, this paper defines a taxonomy of traffic sampling techniques resorting to a comprehensive analysis of the inner components of existing proposals. After identifying granularity, selection scheme, and selection trigger as the main components differentiating sampling proposals, the study goes deeper on characterizing these components, including insights into their computational weight. Following this taxonomy, a general‐purpose architecture is established to sustain the development of flexible sampling‐based measurement systems. Traveling inside packet sampling techniques, this paper contributes to a clearer positioning and comparison of existing proposals, providing a road map to assist further research and deployments in the area. Copyright © 2016 John Wiley & Sons, Ltd.

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“…Currently, traffic sampling techniques sustain a broad range of network tasks including: network management involving short, medium and long term planning and management of network operation, maintenance and provisioning of network services [2] [3]; traffic engineering involving performance optimization, traffic characterization, traffic modeling and control [4] [5]; performance evaluation of protocols and management tools, network reliability and fault tolerance [6] [7]; network security, including anomalies and intrusion detection, botnet and DDoS (Distributed Denial of Service) identification [8]; SLA (Service Level Agreement) compliance, where auditing tools might resort to network sampling for measuring and reporting service levels [9]; QoS control, an area widely assisted by sampling, for measuring parameters such as delay, jitter and packet loss [10] [11].…”

Section: Related Workmentioning

confidence: 99%

A modular sampling framework for flexible traffic analysis

Silva

Carvalho

Lima

2015

2015 23rd International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

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The paradigm of having everyone and everything connected in an ubiquitous way poses huge challenges to today's networks due to the massive traffic volumes involved. To turn treatable all network tasks requiring traffic analysis, sampling the traffic has become mandatory triggering substantial research in the area. Aiming at fostering the deployment and tuning of new sampling techniques, this paper presents a flexible sampling framework developed following a multilayer design in order to easily set up the characteristics of a sampling technique according to the measurement task to be assisted. The framework implementation relies on a comprehensive sampling taxonomy which identifies the granularity, selection scheme and selection trigger as the inner characteristics distinguishing current sampling proposals. As proof of concept of the versatility of this framework in testing the suitability of distinct sampling schemes, this work provides a comparative performance evaluation of classical and recent sampling techniques regarding the estimation accuracy, the volume of data involved in the sampling process and the computational weight in terms of CPU and memory usage.

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An adaptive statistical sampling technique for computer network traffic

Cited by 8 publications

References 5 publications

A multiadaptive sampling technique for cost-effective network measurements

A multiadaptive sampling technique for cost-effective network measurements

Inside packet sampling techniques: exploring modularity to enhance network measurements

A modular sampling framework for flexible traffic analysis

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