6 research outputs found
GoalD: A Goal-Driven Deployment Framework for Dynamic and Heterogeneous Computing Environments
Context: Emerging paradigms like Internet of Things and
Smart Cities utilize advanced sensing and communication infrastructures, where heterogeneity is an inherited feature. Applications targeting
such environments require adaptability and context-sensitivity to uncertain availability and failures in resources and their ad-hoc networks. Such
heterogeneity is often hard to predict, making the deployment process a
challenging task.
Objective: This paper proposes GoalD as a goal-driven framework to
support autonomous deployment of heterogeneous computational resources
to fulfill requirements, seen as goals, and their correlated components on
one hand, and the variability space of the hosting computing and sensing
environment on the other hand.
Method: GoalD comprises an offline and an online stage to fulfill autonomous deployment by leveraging the use of goals. Deployment configuration strategies arise from the variability structure of the Contextual
Goal Model as an underlying structure to guide autonomous planning
by selecting available as well as suitable resources at runtime.
Results: We evaluate GoalD on an existing exemplar from the selfadaptive systems community – the Tele Assistance Service provided by
Weyns and Calinescu [1]. Furthermore, we evaluate the scalability of
GoalD on a repository consisting of 430,500 artifacts. The evaluation
results demonstrate the usefulness and scalability of GoalD in planning
the deployment of a system with thousands of components in a few milliseconds