Understanding information centric layer of adaptive collaborative caching framework in mobile disconnection-prone networks

Smart networks and services leverage in-network caching to improve transmission efficiency and support large amount of content sharing, decrease high operating costs and handle disconnections. In this paper, we investigate the complex challenges related to content popularity weighting process in collaborative caching algorithm in heterogeneous mobile disconnection prone environments. We describe a reputation-based popularity weighting mechanism built in information-centric layer of our adaptive collaborative caching framework CafRepCache which considers a realistic case where caching points gathering content popularity observed by nodes differentiates between them according to node's reputation and network's connectivity. We extensively evaluate CafRepCache with competitive protocols across three heterogeneous real-world mobility, connectivity traces and use YouTube dataset for different workload and content popularity patterns. We show that our collaborative caching mechanism CafRepCache balances the trade-off that achieves higher cache hit ratio, efficiency and success ratios while keeping lower delays, packet loss and caching footprint compared to competing protocols across three traces in the face of dynamic mobility of publishers and subscribers

Understanding information centric layer of adaptive collaborative caching framework in mobile disconnection-prone networks

Smart networks and services leverage in-network caching to improve transmission efficiency and support large amount of content sharing, decrease high operating costs and handle disconnections. In this paper, we investigate the complex challenges related to content popularity weighting process in collaborative caching algorithm in heterogeneous mobile disconnection prone environments. We describe a reputation-based popularity weighting mechanism built in information-centric layer of our adaptive collaborative caching framework CafRepCache which considers a realistic case where caching points gathering content popularity observed by nodes differentiates between them according to node's reputation and network's connectivity. We extensively evaluate CafRepCache with competitive protocols across three heterogeneous real-world mobility, connectivity traces and use YouTube dataset for different workload and content popularity patterns. We show that our collaborative caching mechanism CafRepCache balances the trade-off that achieves higher cache hit ratio, efficiency and success ratios while keeping lower delays, packet loss and caching footprint compared to competing protocols across three traces in the face of dynamic mobility of publishers and subscribers

Understanding information centric layer of adaptive collaborative caching framework in mobile disconnection-prone networks

Smart networks and services leverage in-network caching to improve transmission efficiency and support large amount of content sharing, decrease high operating costs and handle disconnections. In this paper, we investigate the complex challenges related to content popularity weighting process in collaborative caching algorithm in heterogeneous mobile disconnection prone environments. We describe a reputation-based popularity weighting mechanism built in information-centric layer of our adaptive collaborative caching framework CafRepCache which considers a realistic case where caching points gathering content popularity observed by nodes differentiates between them according to node's reputation and network's connectivity. We extensively evaluate CafRepCache with competitive protocols across three heterogeneous real-world mobility, connectivity traces and use YouTube dataset for different workload and content popularity patterns. We show that our collaborative caching mechanism CafRepCache balances the trade-off that achieves higher cache hit ratio, efficiency and success ratios while keeping lower delays, packet loss and caching footprint compared to competing protocols across three traces in the face of dynamic mobility of publishers and subscribers

Modélisation multi-agents pour systèmes émergents et auto-organisés

Dans ce travail, une architecture multi-agents pour systèmes émergents et auto-organisés (MASOES) est définie. Cette architecture permet la possibilité de modéliser une système émergent et auto-organisés à travers une société d'agents (homogène ou hétérogène), qui travaillent de manière décentralisée, avec différents types de comportement: réactive, imitative et cognitive. En outre, ils sont capables de modifier dynamiquement leur comportement en fonction de leur état émotionnel, de sorte que les agents peuvent s'adapter dynamiquement à leur environnement, en favorisant l'émergence de structures. Pour cela, un modèle à deux dimensions affectives avec des émotions positives et négatives est proposé. L'importance de ce modèle affectif, c'est qu'il y a pas des modèles émotionnels pour étudier et comprendre comment modéliser et simuler émergentes et auto-organisés des processus dans un environnement multi-agent et aussi, son utilité pour étudier certains aspects de l'interaction sociale multi-agent (influence des émotions dans les comportements individuels et collectifs des agents).Leer fonéticamente D'autre part, une méthodologie pour faire la modélisation avec MASOES est spécifiée, elle explique comment décrire les éléments, relations et mécanismes au niveau individuel et collectif de la société d'agents, qui favorisent l'analyse de phénomène auto-organisatif et émergent sans modéliser le système mathématiquement. Il est également proposé une méthode de vérification pour MASOES basée sur le paradigme de la sagesse des foules et de cartes cognitives floues (CCFs), pour testé les spécifications de design et les critères de vérification établis, tels que: la densité, la diversité, l'indépendance, l'émotivité, l'auto-organisation et émergence, entre autres. Il montre également l'applicabilité de MASOES par des études de cas diverses dans différents contextes comme : Wikipedia, développement de logiciel gratuit et comportement collectif des piétons par le modèle de forces sociales. Finalement, les deux modèles proposés dans MASOES: le modèle multi-agent initiale et le modèle avec CCFs basé sur ce modèle multi-agent initiale se complètent mutuellement. Cela signifie qu'il est possible de tester le modèle multi-agent à travers le méta-modèle basé sur las CCFs. En outre, il représente une nouvelle alternative pour étudier, tester, vérifier ou valider l'auto-organisation et émergence dans les systèmes complexes et de tester le modèle multi-agent, car il est difficile de faire des tests dans ces systèmes pour le niveau d'incertitude et de complexité qu'ils traitent.In this work a multi-agent architecture for self-organizing and emergent systems (MASOES) is defined. This architecture allows the possibility of modeling a self-organizing and emergent system through a society of agents (homogenous or heterogeneous), who work in a decentralized way, with different types of behavior: reactive, imitative or cognitive. Also they are able to dynamically change their behavior according to their emotional state, so that the agents can adapt dynamically to their environment, favoring the emergence of structures. For it, a two-dimensional affective model with positive and negative emotions is proposed. The importance of this affective model is that there are not emotional models for studying and understanding how to model and simulate emergent and self-organizing processes in a multi-agent environment and also, its usefulness to study some aspects of social interaction multi-agent (e.g. the influence of emotions in individual and collective behavior of agents). On the other hand, a methodology for modeling with MASOES is specified, it explains how to describe the elements, relations and mechanisms at individual and collective level of the society of agents, that favor the analysis of the self-organizing and emergent phenomenon without modeling the system mathematically. It is also proposed a verification method for MASOES based on the paradigm of wisdom of crowds and fuzzy cognitive maps (FCMs), for testing the design specifications and verification criteria established such as: density, diversity, independence, emotiveness, self-organization and emergence, among others. It also shows the applicability of MASOES for modeling diverse case studies (in a diversity of contexts) such as: Wikipedia, Free Software Development and collective behavior of pedestrians through the Social Force Model. Finally, the two models proposed in MASOES: the initial multi-agent model and the model with FCMs based on that initial multi-agent model complement each other. This means that it is possible to test the multi-agent model through the meta-model based on FCMs. Besides, it represents a novel alternative to study, test, verify or validate self-organization and emergence in complex systems and test the multi-agent model, since it is difficult to make tests in these systems directly, given the level of uncertainty and complexity they manage