Verkkojärjestelmien seurantatyökalujen kehittäminen

With increasing amounts of software services provided to users and the more demanding requirements needed from them, monitoring of services is becoming increasingly important. Web service monitoring is the process of confirming system functionality by studying its various attributes, such as availability, reliability, and performance. Monitoring the services helps the software developers, maintainers and owners as they allow for increased reliability, robustness and possibly performance analysis. This thesis focuses on web service monitoring and the tools that it is done with. Specific goals are to learn about the different categories that monitoring services can take and to showcase a custom web service monitoring tool and its further development. The subject is important to the case company LogiNets, which has specific monitoring requirements that need to be fulfilled. These goals were achieved by researching literature on different types of monitoring tools for a literature review and then doing a case study of monitoring tool development. The case study was done about adding a new functionality to LogiNets’s indoor web service monitoring tool called Agent. The literature review was successful in identifying different categories of monitoring tools both by their location relative to the monitored service as well as by the quality of service requirements they fulfill. The review did not, however, discover significant research about existing commercial monitoring tools, and thus provided little help in the case study. The case study was more successful, with the new functionality added and similar extensions planned for the future

Adaptive monitoring: A systematic mapping

Context: Adaptive monitoring is a method used in a variety of domains for responding to changing conditions. It has been applied in different ways, from monitoring systems’ customization to re-composition, in different application domains. However, to the best of our knowledge, there are no studies analyzing how adaptive monitoring differs or resembles among the existing approaches. Objective: To characterize the current state of the art on adaptive monitoring, specifically to: (a) identify the main concepts in the adaptive monitoring topic; (b) determine the demographic characteristics of the studies published in this topic; (c) identify how adaptive monitoring is conducted and evaluated by the different approaches; (d) identify patterns in the approaches supporting adaptive monitoring. Method: We have conducted a systematic mapping study of adaptive monitoring approaches following recommended practices. We have applied automatic search and snowballing sampling on different sources and used rigorous selection criteria to retrieve the final set of papers. Moreover, we have used an existing qualitative analysis method for extracting relevant data from studies. Finally, we have applied data mining techniques for identifying patterns in the solutions. Results: We have evaluated 110 studies organized in 81 approaches that support adaptive monitoring. By analyzing them, we have: (1) surveyed related terms and definitions of adaptive monitoring and proposed a generic one; (2) visualized studies’ demographic data and arranged the studies into approaches; (3) characterized the main approaches’ contributions; (4) determined how approaches conduct the adaptation process and evaluate their solutions. Conclusions This cross-domain overview of the current state of the art on adaptive monitoring may be a solid and comprehensive baseline for researchers and practitioners in the field. Especially, it may help in identifying opportunities of research; for instance, the need of proposing generic and flexible software engineering solutions for supporting adaptive monitoring in a variety of systems.Peer ReviewedPostprint (author's final draft

Sistemas organizativos para la asignación dinámica de recursos computacionales en entornos distribuidos

[ES]Cloud Computing, el conocido paradigma computacional, está emergiendo en los últimos años con gran fuerza. Este paradigma incluye un novedoso modelo de comercialización basado en el pago por uso que ha cambiado radicalmente el modelo de negocio en Internet, lo que ha permitido que las empresas y usuarios individuales puedan alquilar los recursos computacionales que necesitan en cada momento. Este nuevo modelo computacional también ha derivado en que el modelo de producción de estos recursos computacionales evolucione hasta una aproximación cercana al modelo de producción just-in-time, en el que sólo se consumen los recursos necesarios para la producción de los servicios en función de la demanda existente en cada momento, hablándose dentro de este ámbito de elasticidad en los servicios ofertados. Para que esto sea posible, no cabe duda, que una gran cantidad de tecnologías subyacentes han tenido que madurar para dar como resultado un nicho tecnológico con la capacidad para variar los recursos asociados a cada servicio en función de la demanda. Sin embargo, pese a los indudables avances que se han producido a nivel tecnológico, todavía hoy existe una gran capacidad de mejora de estos sistemas. En este sentido, en el marco de esta tesis doctoral se propone el uso de los sistemas multiagente y, especialmente, aquellos basados en modelos organizativos para el control y monitorización de un sistema Cloud Computing. Gracias a esta aproximación, una de las primeras en este campo de investigación, será posible incluir en las plataformas Cloud de nueva generación características derivadas de la Inteligencia Artificial, como son la autonomía, la proactividad y, también, la capacidad de aprendizaje. Para ello se propone un modelo único en su concepción, que permite dotar a la organización de agentes inteligentes con capacidades auto-adaptativas en tiempo de ejecución para entornos abiertos, altamente dinámicos en los que, además, existe un cierto grado de incertidumbre. Así gracias a este modelo, el sistema es capaz de variar los recursos computacionales asociados a cada servicio producido en función de la demanda existe por parte de los usuarios, mediante la auto-adaptación dinámica del propio sistema en su conjunto