Comparative study of various traffic generator tools

Srivastava, Shalvi; Anmulwar, Sweta; Sapkal, A. M.; Batra, Tarun; Gupta, Anil Kumar; Kumar, Vinodh

doi:10.1109/raecs.2014.6799557

Cited by 24 publications

(9 citation statements)

References 2 publications

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“…Several tools have been developed and are available to flexibly measure different network performance parameters [70][71][72][73]. However, periodically probing the performance between each pair of platforms is a resource intensive task and poorly scalable [74].…”

Section: Net Performance Prediction Methodologymentioning

confidence: 99%

Flexible Framework for Real-Time Embedded Systems Based on Mobile Cloud Computing Paradigm

Mora

Gil

Lopez

et al. 2015

Mobile Information Systems

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The development of applications as well as the services for mobile systems faces a varied range of devices with very heterogeneous capabilities whose response times are difficult to predict. The research described in this work aims to respond to this issue by developing a computational model that formalizes the problem and that defines adjusting computing methods. The described proposal combines imprecise computing strategies with cloud computing paradigms in order to provide flexible implementation frameworks for embedded or mobile devices. As a result, the imprecise computation scheduling method on the workload of the embedded system is the solution to move computing to the cloud according to the priority and response time of the tasks to be executed and hereby be able to meet productivity and quality of desired services. A technique to estimate network delays and to schedule more accurately tasks is illustrated in this paper. An application example in which this technique is experimented in running contexts with heterogeneous work loading for checking the validity of the proposed model is described.

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Section: Net Performance Prediction Methodologymentioning

confidence: 99%

Flexible Framework for Real-Time Embedded Systems Based on Mobile Cloud Computing Paradigm

Mora

Gil

Lopez

et al. 2015

Mobile Information Systems

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“…To evaluate the performance in non-ideal scenarios, where other processes on the end-device cause high CPU load or the network is working at full capacity, we used third-party tools to generate extensive network traffic and CPU usage. For network traffic, we used the Ostinato 11 traffic generator, which can create sequential and interleaved streams of different protocols at different rates to generate artificial network load [14]. This results in a full capacity utilization of the network stack, but the test should still succeed.…”

Section: Testing Setupmentioning

confidence: 99%

OPC UA versus ROS, DDS, and MQTT: Performance Evaluation of Industry 4.0 Protocols

Profanter

Tekat

Dorofeev

et al. 2019

2019 IEEE International Conference on Industrial Technology (ICIT)

156

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Ethernet-based protocols are getting more and more important for Industry 4.0 and the Internet of Things. In this paper, we compare the features, package overhead, and performance of some of the most important protocols in this area. First, we present a general feature comparison of OPC UA, ROS, DDS, and MQTT, followed by a more detailed wire protocol evaluation, which gives an overview over the protocol overhead for establishing a connection and sending data. In the performance tests we evaluate open-source implementations of these protocols by measuring the round trip time of messages in different system states: idle, high CPU load, and high network load. The performance analysis concludes with a test measuring the round trip time for 500 nodes on the same host.

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“…In [ 11 ], the authors presented an IP traffic generator based on a specific hardware architecture that generates network traffic to perform tests in the development process of network elements. A comparative study of various traffic-generator tools was performed in [ 12 ], comparing the performance of several network-evaluation tools, namely, PackETH, Ostinato, and D-ITG. The performance of such tools was evaluated in terms of bandwidth utilization.…”

Section: Introductionmentioning

confidence: 99%

Flow-Data Gathering Using NetFlow Sensors for Fitting Malicious-Traffic Detection Models

Campazas-Vega

Crespo-Martínez

Guerrero‐Higueras

et al. 2020

Sensors

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Advanced persistent threats (APTs) are a growing concern in cybersecurity. Many companies and governments have reported incidents related to these threats. Throughout the life cycle of an APT, one of the most commonly used techniques for gaining access is network attacks. Tools based on machine learning are effective in detecting these attacks. However, researchers usually have problems with finding suitable datasets for fitting their models. The problem is even harder when flow data are required. In this paper, we describe a framework to gather flow datasets using a NetFlow sensor. We also present the Docker-based framework for gathering netflow data (DOROTHEA), a Docker-based solution implementing the above framework. This tool aims to easily generate taggable network traffic to build suitable datasets for fitting classification models. In order to demonstrate that datasets gathered with DOROTHEA can be used for fitting classification models for malicious-traffic detection, several models were built using the model evaluator (MoEv), a general-purpose tool for training machine-learning algorithms. After carrying out the experiments, four models obtained detection rates higher than 93%, thus demonstrating the validity of the datasets gathered with the tool.

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Comparative study of various traffic generator tools

Cited by 24 publications

References 2 publications

Flexible Framework for Real-Time Embedded Systems Based on Mobile Cloud Computing Paradigm

Flexible Framework for Real-Time Embedded Systems Based on Mobile Cloud Computing Paradigm

OPC UA versus ROS, DDS, and MQTT: Performance Evaluation of Industry 4.0 Protocols

Flow-Data Gathering Using NetFlow Sensors for Fitting Malicious-Traffic Detection Models

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