Jesús Acosta-Elias

A demand based algorithm for rapid updating of replicas

In many Internet scale replicated system, not all replicas can be dealt with in the same way, since some will be in greater demand than others. In the case of weak consistency algorithms, we have observed that updating first replicas having most demand, a greater number of clients would gain access to updated content in a shorter period of time. In this work we have investigated the benefits that can be obtained by prioritizing replicas with greater demand, and considerable improvements have been achieved. In zones of higher demand, the consistent state is reached up to six times quicker than with a normal weak consistency algorithm, without incurring the additional costs of the strong consistency.

A growth model for directed complex networks with power-law shape in the out-degree distribution

Many growth models have been published to model the behavior of real complex networks. These models are able to reproduce several of the topological properties of such networks. However, in most of these growth models, the number of outgoing links (i.e., out-degree) of nodes added to the network is constant, that is all nodes in the network are born with the same number of outgoing links. In other models, the resultant out-degree distribution decays as a poisson or an exponential distribution. However, it has been found that in real complex networks, the out-degree distribution decays as a power-law. In order to obtain out-degree distribution with power-law behavior some models have been proposed. This work introduces a new model that allows to obtain out-degree distributions that decay as a power-law with an exponent in the range from 0 to 1.

The Effects of Network Topology on Epidemic Algorithms

Epidemic algorithms can propagate information in a large scale network, that changes arbitrarily, in a self-organizing way. This type of spreading process allows rapid dissemination of information to all network nodes. However, the dynamics of epidemic algorithms can be strongly influenced by the network topology. In this paper, numerical simulations are used to illustrate such influences. We address networks with simple topologies for simplicity and in order to isolate other effects that occur in more complex networks.

On a growth model for complex networks capable of producing power-law out-degree distributions with wide range exponents

The out-degree distribution is one of the most reported topological properties to characterize real complex networks. This property describes the probability that a node in the network has a particular number of outgoing links. It has been found that in many real complex networks the out-degree has a behavior similar to a power-law distribution, therefore some network growth models have been proposed to approximate this behavior. This paper introduces a new growth model that allows to produce out-degree distributions that decay as a power-law with an exponent in the range from 1 to ∞.

Topology-Sensitive epidemic algorithm for information spreading in large-scale systems

Epidemic algorithms are an emerging technique that has recently gained popularity as a potentially effective solution for disseminating information in large-scale network systems. For some application scenarios, efficient and reliable data dissemination to all or a group of nodes in the network is necessary to provide with the communication services within the system. These studies may have a large impact in communication networks where epidemic-like protocols become a practice for message delivery, collaborative peer-to-peer applications, distributed database systems, routing in Mobile Ad Hoc networks, etc. In this paper we present, through various simulations, that an epidemic spreading process can be highly influenced by the network topology. We also provide a comparative performance analysis of some global parameters performance such as network diameter and degree of connectivity. Based on this analysis, we propose a new epidemic strategy that takes into account the topological structure in the network. The results show that the proposed epidemic algorithm outperform a classical timestamped anti-entropy epidemic algorithm in terms of the number of sessions required to reach a consistent state in the network system.

Performance evaluation of a self-organized hierarchical topology for update of replicas in a large distributed system

In this paper we evaluate our own weak consistency algorithm, which is called the ”Fast Consistency Algorithm”, and whose main aim is optimizing the propagation of changes introducing a preference for nodes and zones of the network which have greatest demand. Weak consistency algorithms allow us to propagate changes in a large, arbitrary changing storage network in a self-organizing way. These algorithms generate very little traffic overhead; they have low latency and are scalable, in addition to being fault tolerant. The algorithm has been simulated over ns-2, and measured its performance for complex spatial distributions of demand, including Internet like self-similar fractal distributions of demand. The impulse response of the algorithm has been characterized. We conclude that considering application parameters such as demand in the event or change propagation mechanism to: 1) prioritize probabilistic interactions with neighbors with higher demand, and 2) including little changes on the logical topology (leader interconnection in hierarchical topology ), gives a surprising improvement in the speed of change propagation perceived by most users. In other words, it satisfies the greatest demand in the shortest amount of time.

Performance Evaluation of the Fast Consistency Algorithms in Large Decentralized Systems

Weak consistency algorithms allow us to propagate changes in a large, arbitrary changing storage network in a self-organizing way. These algorithms generate very little traffic overhead. In this paper we evaluate our own weak consistency algorithm, which is called the ”Fast Consistency Algorithm”, and whose main aim is optimizing the propagation of changes introducing a preference for nodes and zones of the network which have greatest demand. We conclude that considering application parameters such as demand in the event or change propagation mechanism to: 1) prioritize probabilistic interactions with neighbors with higher demand, and 2) including little changes on the logical topology, gives a surprising improvement in the speed of change propagation perceived by most users.