scholarly journals PeerFlow: Secure Load Balancing in Tor

2017 ◽  
Vol 2017 (2) ◽  
pp. 74-94 ◽  
Author(s):  
Aaron Johnson ◽  
Rob Jansen ◽  
Nicholas Hopper ◽  
Aaron Segal ◽  
Paul Syverson
Keyword(s):  
Load Balancing ◽  
Load Balance ◽  
Large Scale ◽  
Scanning System ◽  
Large Scale Network ◽  
Improve Accuracy ◽  
Scale Network ◽  
Network Simulations ◽  
Improved Design ◽  
Speed And Accuracy

Abstract We present PeerFlow, a system to securely load balance client traffic in Tor. Security in Tor requires that no adversary handle too much traffic. However, Tor relays are run by volunteers who cannot be trusted to report the relay bandwidths, which Tor clients use for load balancing. We show that existing methods to determine the bandwidths of Tor relays allow an adversary with little bandwidth to attack large amounts of client traffic. These methods include Tor’s current bandwidth-scanning system, TorFlow, and the peer-measurement system EigenSpeed. We present an improved design called PeerFlow that uses a peer-measurement process both to limit an adversary’s ability to increase his measured bandwidth and to improve accuracy. We show our system to be secure, fast, and efficient. We implement PeerFlow in Tor and demonstrate its speed and accuracy in large-scale network simulations.

scholarly journals Robust Modulation of Integrate-and-Fire Models

2018 ◽  
Vol 30 (4) ◽  
pp. 987-1011 ◽  
Author(s):  
Tomas Van Pottelbergh ◽  
Guillaume Drion ◽  
Rodolphe Sepulchre
Keyword(s):  
Large Scale ◽  
Neuronal Excitability ◽  
Population Studies ◽  
Fire Model ◽  
Neuronal Populations ◽  
Integrate And Fire ◽  
Large Scale Network ◽  
Channel Expression ◽  
Scale Network ◽  
Network Simulations

By controlling the state of neuronal populations, neuromodulators ultimately affect behavior. A key neuromodulation mechanism is the alteration of neuronal excitability via the modulation of ion channel expression. This type of neuromodulation is normally studied with conductance-based models, but those models are computationally challenging for large-scale network simulations needed in population studies. This article studies the modulation properties of the multiquadratic integrate-and-fire model, a generalization of the classical quadratic integrate-and-fire model. The model is shown to combine the computational economy of integrate-and-fire modeling and the physiological interpretability of conductance-based modeling. It is therefore a good candidate for affordable computational studies of neuromodulation in large networks.

MAHA: Migration-based Adaptive Heuristic Algorithm for Large-scale Network Simulations

2019 ◽  
Vol 23 (2) ◽  
pp. 1251-1266
Author(s):  
Muhammad Ibrahim ◽  
Muhammad Azhar Iqbal ◽  
Muhammad Aleem ◽  
Muhammad Arshad Islam ◽  
Nguyen-Son Vo
Keyword(s):  
Heuristic Algorithm ◽  
Large Scale ◽  
Large Scale Network ◽  
Scale Network ◽  
Network Simulations ◽  
Adaptive Heuristic

Modeling Neuronal Assemblies: Theory and Implementation

2001 ◽  
Vol 13 (9) ◽  
pp. 1923-1974 ◽  
Author(s):  
J. Eggert ◽  
J. L. van Hemmen
Keyword(s):  
Large Scale ◽  
Theoretical Background ◽  
Spiking Neurons ◽  
Spike Response ◽  
Macroscopic Models ◽  
Neuronal Assemblies ◽  
Large Scale Network ◽  
Graded Response ◽  
Scale Network ◽  
Network Simulations

Models that describe qualitatively and quantitatively the activity of entire groups of spiking neurons are becoming increasingly important for biologically realistic large-scale network simulations. At the systems and areas modeling level, it is necessary to switch the basic descriptional level from single spiking neurons to neuronal assemblies. In this article, we present and review work that allows a macroscopic description of the assembly activity. We show that such macroscopic models can be used to reproduce in a quantitatively exact manner the joint activity of groups of spike-response or integrate-and-fire neurons. We also show that integral as well as differential equation models of neuronal assemblies can be understood within a single framework, which allows a comparison with the commonly used assembly-averaged graded-response type of models. The presented framework thus enables the large-scale neural network modeler to implement networks using computational units beyond the single spiking neuron without losing much biological accuracy. This article explains the theoretical background as well as the capabilities and the implementation details of the assembly approach.

scholarly journals A macaque connectome for large-scale network simulations in TheVirtualBrain

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Kelly Shen ◽  
Gleb Bezgin ◽  
Michael Schirner ◽  
Petra Ritter ◽  
Stefan Everling ◽  
...  
Keyword(s):  
Large Scale ◽  
Large Scale Network ◽  
Scale Network ◽  
Network Simulations

Improvement on the Anycast Routing Algorithm Based on Genetic Algorithm

2011 ◽  
Vol 305 ◽  
pp. 389-393
Author(s):  
Min Shuo Li ◽  
Hua Wen Liu
Keyword(s):  
Genetic Algorithm ◽  
Load Balance ◽  
Large Scale ◽  
Simulation Experiment ◽  
Routing Algorithm ◽  
Large Scale Network ◽  
Anycast Routing ◽  
Scale Network ◽  
Performance Discrepancy ◽  
Improved Algorithm

Anycast routing can raise the QoS of networks and offers load-balance, it is the leading issue in the domain of computer, electric and communication. With the study of the anycast routing algorithm based on genetic algorithm, we put forth an improved anycast routing algorithm in the thesis. Through a lot simulation experiment on simulating platform, we compare and evaluate the performance discrepancy of the improved algorithm and the old one. The improved anycast-routing algorithm befits large-scale network, can satisfy the demand of the development of the net, It has more competent application merit.

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