scholarly journals Netbench – large-scale network device testing with real-life traffic patterns

2019 ◽  
Vol 214 ◽  
pp. 08005
Author(s):  
Stefan Nicolae Stancu ◽  
Adam Lukasz Krajewski ◽  
Mattia Cadeddu ◽  
Marta Antosik ◽  
Bernd Panzer-Steinde
Keyword(s):  
Large Scale ◽  
Network Performance ◽  
Real Life ◽  
Traffic Patterns ◽  
Correct Operation ◽  
Testing Framework ◽  
Network Testing ◽  
Large Scale Network ◽  
Scale Network ◽  
Large Scale Tests

Network performance is key to the correct operation of any modern data centre infrastructure or data acquisition (DAQ) system. Hence, it is crucial to ensure the devices employed in the network are carefully selected to meet the required needs. Specialized commercial testers implement standardized tests [1, 2], which benchmark the performance of network devices under reproducible, yet artificial conditions. Netbench is a network-testing framework, relying on commodity servers and NICs, that enables the evaluation of network devices performance for handling traffic-patterns that closely resemble real-life usage, at a reasonably affordable price. We will present the architecture of the Netbench framework, its capabilities and how they complement the use of specialized commercial testers (e.g. competing TCP flows that create temporary congestion provide a good benchmark of buffering capabilities in real-life scenarios). Last but not least, we will describe how CERN used Netbench for performing large scale tests with partial-mesh and full-mesh TCP flows [3], an essential validation point during its most recent high-end routers call for tender.

scholarly journals Self-regulation of stress-related large-scale brain network balance using real-time fMRI Neurofeedback

2021 ◽  
Author(s):  
Florian Krause ◽  
Nikolaos Kogias ◽  
Martin Krentz ◽  
Michael Luehrs ◽  
Rainer Goebel ◽  
...  
Keyword(s):  
Real Time ◽  
Executive Control ◽  
Large Scale ◽  
Acute Stress ◽  
Self Regulation ◽  
Real Life ◽  
Brain Network ◽  
Large Scale Network ◽  
Novel Approach ◽  
Scale Network

It has recently been shown that acute stress affects the allocation of neural resources between large-scale brain networks, and the balance between the executive control network and the salience network in particular. Maladaptation of this dynamic resource reallocation process is thought to play a major role in stress-related psychopathology, suggesting that stress resilience may be determined by the retained ability to adaptively reallocate neural resources between these two networks. Actively training this ability could hence be a potentially promising way to increase resilience in individuals at risk for developing stress-related symptomatology. Using real-time functional Magnetic Resonance Imaging, the current study investigated whether individuals can learn to self-regulate stress-related large-scale network balance. Participants were engaged in a bidirectional and implicit real-time fMRI neurofeedback paradigm in which they were intermittently provided with a visual representation of the difference signal between the average activation of the salience and executive control networks, and tasked with attempting to self-regulate this signal. Our results show that, given feedback about their performance over three training sessions, participants were able to (1) learn strategies to differentially control the balance between SN and ECN activation on demand, as well as (2) successfully transfer this newly learned skill to a situation where they (a) did not receive any feedback anymore, and (b) were exposed to an acute stressor in form of the prospect of a mild electric stimulation. The current study hence constitutes an important first successful demonstration of neurofeedback training based on stress-related large-scale network balance - a novel approach that has the potential to train control over the central response to stressors in real-life and could build the foundation for future clinical interventions that aim at increasing resilience.

A Proposal of SDN Based Disaster-Aware Smart Routing for Highly-Available Information Storage Systems and Its Evaluation

2017 ◽  
Vol 9 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Satoru Izumi ◽  
Misumi Hata ◽  
Hiroyuki Takahira ◽  
Mustafa Soylu ◽  
Asato Edo ◽  
...  
Keyword(s):  
Data Transmission ◽  
Large Scale ◽  
Network Performance ◽  
Storage Systems ◽  
Real Space ◽  
Information Storage ◽  
Large Scale Network ◽  
Disaster Situation ◽  
Scale Network ◽  
Available Information

In this paper, the authors propose a SDN based disaster-aware smart routing scheme for highly-available information storage systems. The authors' proposed scheme is based on the concept of Symbiotic Computing to recognize disaster status in Real Space, and provides appropriate routes form Digital Space dynamically. This realizes effective data transmission considering disaster situation and its time variation. In this paper, the authors design and implement their proposed scheme with dynamic multipath routing and parallel data transmission to enhance the network performance. They also evaluate its effectiveness through large-scale network environments.

scholarly journals Modeling and Quantifying Interaction of Information and Capacity in Public Transport Disruptions

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Mahsa Rahimi Siegrist ◽  
Francesco Corman
Keyword(s):  
Public Transport ◽  
Time Management ◽  
Large Scale ◽  
Information Dissemination ◽  
Real Life ◽  
Negative Effects ◽  
Public Transport System ◽  
Large Scale Network ◽  
Scale Network ◽  
The Impact

Disruption in public transport networks has adverse implications for both passengers and service managers. To evaluate the effects of disruptions on passengers’ behaviour, various methods, simulation modules, and mathematical models are widely used. However, such methods included many assumptions for the sake of simplicity. We here use multiagent microsimulation modules to simulate complex real-life scenarios. Aspects that were never explicitly modelled together are the capacity of the network and the effect of disruption to on-board passengers, who might need to alight the disrupted services. In addition, our simulation and developed module provide a framework that can be applied for both transport planning and real-time management of disruption for the large-scale network. We formalize the agent-based assignment problem in capacitated transit networks for disrupted situations, where some information is available about the disruption. We extend a microsimulation environment to quantify precisely the impact and the number of agents directly and indirectly affected by the disruption, respectively, those passengers who cannot perform their trip because of disrupted services (directly affected passengers), and those passengers whose services are not disrupted but experience additional crowding effects (indirectly affected passengers). The outcomes are discussed both from passengers’ perspective and for extracting more general planning and policy recommendations. The modeling and solution approaches are applied to the multimodal public transport system of Zürich, Switzerland. Our results show that different information dissemination strategies have a large impact on direct and indirect effects. By earlier information dissemination, the direct effects get milder but larger in space, and indirect negative effects arise. The scenarios with the least information instead are very strongly affecting few passengers, while the less negative indirect effect for the rest of the network.

Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Large Scale Network ◽  
Brand Advertising ◽  
Scale Network
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