Context-Sensitive Resource Discovery

“…While it has been helpful to illustrate our model and to experiment with it, the language used does not represent the focus of our research. Other works have concentrated on defining precise ontologies to encode resource descriptors (e.g., [10]), and to capture the preferences of a user into computer models (e.g., [27]). A common assumption to all these works is that a universally accepted ontology is being used; however, we believe that such an ontology will never exist.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the descriptor contains information that can be used to assess the quality of the resource itself; this includes, for example, estimates of local resources (e.g., battery and memory) consumption. If we assume these estimates to vary in a range [0,10], then the descriptor in Figure 1 says that the component consumes much more energy than memory. Note that these values do not aim to be precise estimates of actual consumptions; rather, they aim to enable comparisons of resources of the same type (e.g., services, components, etc.).…”

Section: Q-cad Resources Descriptors and Bindingmentioning

confidence: 99%

“…Under the tag <MAXIMISE>, the application lists the non-functional parameters it is interested in, together with a weight that expresses their relative importance. Let us assume that these weights vary in a range [0,10]; the example indicates that the application is willing to select a component that maximises battery and memory saving; also, saving energy is twice as important as saving memory. The maximise part of the utility function is executed on a resource descriptor, as a summation of products (i.e., normalised estimates multiplied by weights, as found in the resource descriptor and utility function respectively); it returns a single value that can be used to compare the quality of different resources.…”

Section: Utility Functionsmentioning

confidence: 99%

See 1 more Smart Citation

Q-CAD: QoS and context aware discovery framework for mobile systems

Capra

Zachariadis

Mascolo

ICPS '05. Proceedings. International Conference on Pervasive Services, 2005.

View full text Add to dashboard Cite

Pervasive computing environments are populated by a large number of heterogeneous and dynamic resources, encompassing devices, services and information sources. This number is set to radically increase in the future; as a result, a mobile device will be able to contact a large number of service providers and sensors that will enable it to perform any task at hand. Moreover, interaction with these services and sensors will be made possible by means of various components, some located on the mobile device, some available for download from remote hosts. We refer to services, sensors and components as resources. In this paper we present Q-CAD, a resource discovery framework that enables pervasive computing applications to discover and select the resource(s) best satisfying the user needs, taking the current execution context and quality-of-service (QoS) requirements into account. The available resources are screened, so that only those suitable to the current execution context of the application will be considered; the shortlisted resources are then evaluated against the QoS needs of the application, and a binding is established to the best available. The paper illustrates how we encode context and QoS information, gives details of the Q-CAD model and of its mapping onto a component-based architecture, and it finally reports on the implementation and on the experimental results.

show abstract

“…Since our name service may return multiple components matching a query, the requesting component may use a selection function to select appropriate components. When data sources come and go, as is typical in a pervasive-computing environment, the results of the query change occasionally and the selection function is re-evaluated, permitting quick adaptation for the dependent [1].…”

Section: Component Registrationmentioning

confidence: 99%