Linked models@runtime to ease the administration of pervasive applications

International audiencePervasive applications are often executed in fluctuating conditions and need frequent adaptations to meet requirements. Autonomic computing techniques are frequently used to automate adaptations to changing execution conditions. However, some administration tasks still have to be performed by human administrators. Such tasks are very complex because of a lack of understanding of the system current state. In this paper, we propose to build and link models at runtime of supervised applications in order to ease the administrators' job. Our approach is illustrated on a health application called actimetrics, developed with Orange Labs