Abstract. Recent works on self-adaptivity use a middleware-based approach where the adaptation mechanisms and meta-level information are separated and externalized from the application code. Current solutions generally target individual life-cycle phases of an application in isolation, preventing easy integration of design-time and run-time adaptability. Integration is needed in order to support the introduction of new adaptive behavior during run-time. Self-adapting systems therefore need to support both planning, instantiation and maintenance of applications throughout their life-time.In this paper we propose middleware managed adaptation, in which services are specified by their behavior, and planned, instantiated and maintained by middleware services in such a way that the behavioral requirements are satisfied throughout the service life-time. Central to this approach is mirror-based reflection, which supports introspection and intercession on an application, or any service, through all the phases of its life-cycle, including pre-runtime. The mirror of a service may contain information about its implementation, including the developer's knowledge about how this implementation will perform in different contexts. By making this knowledge available to the middleware, we facilitate the implementation of a wide range of self-adaptive behaviors.
Self-adaptive systems often use a middleware-based approach where adaptation mechanisms and policies are separated and externalized from the application code. Such separation facilitates the independent analysis of application and adaptation. In the QuA middleware, we use mirror-based reflection and service planning to support the development and execution of self-adaptive systems. A mirror provides meta information about a service's behavior and implementation throughout all life-cycle phases, including its performance in different contexts. Service planning supports dynamic discovery, utility-based and context-aware evaluation, and selection of alternative implementations of a given service.Here we argue that the QuA middleware is also able to support certain forms of evolution of adaptive systems. Since in QuA new implementation alternatives or updated versions of software are automatically discovered and considered during service planning, evolution both during run time and load time is supported. Experimental results from evolving a state-of-the-art adaptive media streaming application using our middleware are also presented.
Distributed applications on a computational grid infrastructure require the provision of quality of service (QoS) to ensure application performance. However current grid solutions do not support QoS management, and are limited in performance optimization. In this paper we propose to use a platform managed QoS-aware service configuration approach that enables application developers to separate functional specification and QoS requirements from implementation decisions that depend on the deployment environment. The middleware platform is responsible for achieving application QoS goals at deployment time. This task of the middleware platform is called service planning. In order to show the benefit of the middleware framework, we present an analytical model of service planning for finding QoStradeoff points in making configuration decisions. This model is by itself an improvement of a common solution technique used for QoS management. We apply the model to a video-based object tracking grid application.
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