A Middleware framework for self-adaptive large scale distributed services
- Autores: Pablo Chacín Martínez
- Directores de la Tesis: Leandro Navarro Moldes (dir. tes.)
- Lectura: En la Universitat Politècnica de Catalunya (UPC) ( España ) en 2011
- Idioma: inglés
- Tribunal Calificador de la Tesis: Félix Freitag (presid.), Jordi Guitart (secret.), Jens Nimis (voc.), Luis Manuel Díaz de Cerio Ripalda (voc.), Oscar Ardaiz Villanueva (voc.)
- Materias:
- Enlaces
- Tesis en acceso abierto en: TDX
- Resumen
Modern service-oriented applications demand the ability to adapt to changing conditions and unexpected situations while maintaining a required QoS. Existing self-adaptation approaches seem inadequate to address this challenge because many of their assumptions are not met on the large-scale, highly dynamic infrastructures where these applications are generally deployed on. The main motivation of our research is to devise principles that guide the construction of large scale self-adaptive distributed services. We aim to provide sound modeling abstractions based on a clear conceptual background, and their realization as a middleware framework that supports the development of such services. Taking the inspiration from the concepts of decentralized markets in economics, we propose a solution based on three principles: emergent self-organization, utility driven behavior and model-less adaptation. Based on these principles, we designed Collectives, a middleware framework which provides a comprehensive solution for the diverse adaptation concerns that rise in the development of distributed systems. We tested the soundness and comprehensiveness of the Collectives framework by implementing eUDON, a middleware for self-adaptive web services, which we then evaluated extensively by means of a simulation model to analyze its adaptation capabilities in diverse settings. We found that eUDON exhibits the intended properties: it adapts to diverse conditions like peaks in the workload and massive failures, maintaining its QoS and using efficiently the available resources; it is highly scalable and robust; can be implemented on existing services in a non-intrusive way; and do not require any performance model of the services, their workload or the resources they use. We can conclude that our work proposes a solution for the requirements of self-adaptation in demanding usage scenarios without introducing additional complexity. In that sense, we believe we make a significant contribution towards the development of future generation service-oriented applications.
