Attila Fekete

Public-key encryption with chaos

We propose public-key encryption algorithms based on chaotic maps, which are generalization of well-known and commercially used algorithms: Rivest-Shamir-Adleman (RSA), ElGamal, and Rabin. For the case of generalized RSA algorithm we discuss in detail its software implementation and properties. We show that our algorithm is as secure as RSA algorithm.

Traffic Dynamics in Scale-Free Networks

We study traffic dynamics in growing scale-free networks. Both the scale-free structure of the network and the adaptive nature of the dynamics which controls traffic in the network are considered in the model. The model is investigated with computer simulations and analytically for the case of a scale-free tree. For the scale-free tree, an exact formula and its power law approximation of the complementary cumulative distribution function of link load (edge betweenness) is presented. We examine whether the scaling properties of the network affect the performance of the transport mechanism and estimate the average number of competing transport mechanisms at bottlenecks. We find that bottlenecks tend to appear on the periphery of the network as the performance increases. Various bandwidth allocation strategies are compared. We show that the best performance is achieved when capacity is distributed proportionally to the expected load of links. We demonstrate that it is necessary to study both the topology and the dynamics of the transport mechanism to understand the whole system.

Shortest path discovery of complex networks

In this Rapid Communication we present an analytic study of sampled networks in the case of some important shortest-path sampling models. We present analytic formulas for the probability of edge discovery in the case of an evolving and a static network model. We also show that the number of discovered edges in a finite network scales much more slowly than predicted by earlier mean-field models. Finally, we calculate the degree distribution of sampled networks and we demonstrate that they are analogous to a destroyed network obtained by randomly removing edges from the original network.

ETOMIC Advanced Network Monitoring System for Future Internet Experimentation

ETOMIC is a network traffic measurement platform with high precision GPS-synchronized monitoring nodes. The infrastructure is publicly available to the network research community, supporting advanced experimental techniques by providing high precision hardware equipments and a Central Management System. Researchers can deploy their own active measurement codes to perform experiments on the public Internet. Recently, the functionalities of the original system were significantly extended and new generation measurement nodes were deployed. The system now also includes well structured data repositories to archive and share raw and evaluated data. These features make ETOMIC as one of the experimental facilities that support the design, development and validation of novel experimental techniques for the future Internet. In this paper we focus on the improved capabilities of the management system, the recent extensions of the node architecture and the accompanying database solutions.

Self-similarity in bottleneck buffers

We analyze a model of a heavily utilized bottleneck buffer in this paper. We show that the nature of traffic leaving the buffer is determined by the buffer length/number of TCP ratio. If this ratio is above a critical value, TCP can stay in the congestion avoidance phase and it can generate periodic traffic after a transient period. If the ratio is below a critical value then the congestion window dynamics becomes chaotic or stochastic. We demonstrate that the traffic shows self-similar properties in this chaotic phase, which is determined by the exponential backoff mechanism.

NOVI tools and algorithms for federating virtualized infrastructures

The EC FP7/FIRE STREP project NOVI - Network Innovation over Virtualized Infrastructures - explores efficient approaches to compose virtualized e-Infrastructures towards a holistic Future Internet (FI) cloud service. Resources belonging to various levels, i.e. networking, storage and processing are in principle managed by separate yet inter-working providers. In this ecosystem NOVI aspires to develop and validate methods, information systems and algorithms that will provide users with isolated slices, baskets of resources and services drawn from federated infrastructures. Experimental research accomplished thus far concludes the first phase of NOVI, with early prototypes of semantic-aware advanced control & management plane components being deployed and tested. The NOVI testing environment is based on combining PlanetLab and FEDERICA, two dissimilar virtualized experimental infrastructures with attributes widely anticipated in a FI cloud. This federated testbed is stitched at the data plane via the NSwitch, a distributed virtual switch developed within NOVI.

Shortest-path sampling of dense homogeneous networks

The study of complex networks is usually based on samples of the network. We analyze edge and node samples of dense homogeneous networks, obtained by shortest-path exploration of the network between a set of agents, distributed uniformly among the nodes. We characterize the density of the network by a novel metric based on the scaling of the average degree with the size of the network and present the edge and node sampling probability as a function of the agent density for the densest network scenarios.

On buffer limited congestion window dynamics and packet loss

The central result of this paper is an analytic formula describing the packet loss probability in a buffer as a function of the length of the buffer and the probability of external packet loss. This formula makes it possible to calculate the total loss along a multi-buffer, multi-link route. Also, new types of congestion window distributions are discovered when the packet loss in the buffer is large. These are different from the usual Gaussian type single humped distributions and can help to develop a qualitative classification of window distributions.

Effect of network structure on phase transitions in queuing networks

Recently, De Martino et al. [J. Stat. Mech. (2009) P08023; Phys. Rev. E 79, 015101 (2009)] have presented a general framework for the study of transportation phenomena on random networks with annealed disorder. One of their most significant achievements was a deeper understanding of the phase transition from the uncongested to the congested phase at a critical traffic load on uncorrelated networks. In this paper, we also study phase transition in transportation networks using a discrete time random walk model. Our aim is to establish a direct connection between the structure of an uncorrelated random graph with quenched disorder and the value of the critical traffic load. We show that if the network is dense, the quenched and annealed formulas for the critical loading probability coincide. For sparse graphs, higher-order corrections, related to the local structure of the network, appear.

Formation of Self-similar Traffic at Bottleneck Buffers of Computer Networks

This paper analyzes how self-similarity is created in bottleneck buffers of computer networks. We argue that, in absence of heavy tailed section length or file size distributions, the sources which create self similarity in the network are certain buffers which are either too short or shared by too many TCP sessions. We analyse how congestion and long-range dependent traffic is generated in such a buffer shared by parallel TCP flows. We find that a single parameter, the \(\textit{buffer length/number of TCPs}\) ratio, determines the nature of the traffic leaving the buffer. If this ratio is above ≈ 3.0, TCPs stay in the congestion avoidance phase, if it is below then the congestion window dynamics becomes chaotic or stochastic. We show that in this phase the main properties of the traffic are determined by the exponential backoff mechanism. Emergence of chaotic TCP dynamics and long range dependence seem to be intimately related.