Building Self-Configuring Data Centers with Cross Layer Coevolution

Abstract—This paper describes a biologically-inspired architecture, called SymbioticSphere, which allows data centers to autonomously adapt to dynamic environmental changes. SymbioticSphere follows biological principles such as decentralization, evolution and symbiosis to design application services and middleware platforms in a data center. Each service and platform is designed as a biological entity, and implements biological behaviors such as energy exchange, migration, reproduction and death. Each service/platform also possesses behavior policies, as genes, each of which defines when to and how to invoke a particular behavior. This paper presents a set of behaviors for services and platforms, and describes how services and platforms act and interact with each other. Simulation results show that services and platforms autonomously adapt to dynamic network conditions (e.g., user location, network traffic and resource availability) by evolving their behavior policies across generations. Simulation results also show that services and platforms coevolve to improve their adaptability by adjusting their behavior policies cooperatively. Index Terms—Autonomic self-configuring network systems, Biologically-inspired networking, evolvable network systems I

Un sistema multi-agente para la auto-configuración de las operaciones de red en la subcapa MAC del modelo OSI

En el presente trabajo se propone una arquitectura auto-configurable empleando un sistema multi-agente para el diagnóstico y optimización de una red de telecomunicaciones por medio de la implementación de un sistema de recolección de información en estaciones, servidores y equipos activos de la red, la información capturada de se analiza, estandariza, filtra y procesa para el logro del objetivo seleccionado. Como soporte, se cuenta con un modelo de datos, el cual define y permite guardar la información necesaria para la reconfiguración de la red utilizando un modelo de optimización mediante el cual se realizan la reconfiguración de equipos y estaciones de red. El proceso de optimización es el mecanismo para el logro del objetivo que nos lleva a la toma de decisión. El sistema multi-agente interviene en el proceso de resolver muchos problemas de manera simultánea y desplegar la solución que consta de tareas aplicadas de una manera distribuida. Los resultados indican la factibilidad de una alternativa y permiten lograr la capacidad de ejecutar una solución automática de problemas. Una vez los agentes son comunicados de una tarea actúan sin la necesidad de un control centralizado.Abstract: In this work, a self-configuration computer network framework over a multi agent system for diagnostic, optimization and management was developed. The system follows several steps such as collecting information from workstations, servers and other active equipment in network. This information is tuned through parsing, standardization, and thus a data model takes scheme which who allows to keep the relevant information for the decision process. An optimization model is a key mechanism for achieving decision process. The multi agent system involves a process of solving simultaneously several problems and uses its distributed capacity for deploying a solutions. As a conclusion, it was possible to reach the goal, providing and deploying a self-configuration system on a network in a distributed way, when each agent does a job, it does not need a central control. When a computer network come into a partition state (two o many separate pieces), each piece must resolve their interconnection problems such as well-functioning, stability or self-diagnostic.Maestrí