Giovanni Russello

Using Aspect-GAMMA in the design of embedded systems

This paper proposes a design framework that takes advantage of the aspect-orientation paradigm. The proposed framework is based on the multi-set transformation language called GAMMA, used for the functional aspect, together with a set of modelling notations for other aspects of system design, namely coordination, timing and distribution.

Dynamically adapting tuple replication for managing availability in a shared data space

With its decoupling of processes in space and time, the shared data space model has proven to be a well-suited solution for developing distributed component-based systems. However, as in many distributed applications, functional and extra-functional aspects are still interwoven in components. In this paper, we address how shared data spaces can support separation of concerns. In particular, we present a solution that allows developers to merely specify performance and availability requirements for data tuples, while the underlying middleware evaluates various distribution and replication policies in order to select the one that meets these requirements best. Moreover, in our approach, the middleware continuously monitors the behavior of application and system components, and switches to different policies if this would lead to better results. We describe our approach, along with the design of a prototype implementation and its quantitative evaluation.

Dynamic Adaptation of Data Distribution Policies in a Shared Data Space System

Increasing demands for interconnectivity, adaptivity and flexibility are leading to distributed component-based systems (DCBS) where components may dynamically join and leave a system at run-time.

Exploiting Differentiated Tuple Distribution in Shared Data Spaces

The shared data space model has proven to be an effective paradigm for building distributed applications. However, building an efficient distributed implementation remains a challenge. A plethora of different implementations exists. Each of them has a specific policy for distributing data across nodes. Often, these policies are tailored to a specific application domain. Thus, those systems may often perform poorly with applications extraneous to their domain. In this paper, we propose that implementations of a distributed shared data space system should provide mechanisms for tailoring data distribution policies. Through this flexibility the shared data space system can cope with a wide spectrum of application classes. The need for this flexibility is illustrated by experiments which show that there is no single distribution policy that works well in all cases.

An experimental evaluation of self-managing availability in shared data spaces

With its decoupling of processes in space and time, the shared data space model has proven to be a well-suited solution for developing distributed component-based systems. However, as in many distributed applications, functional and extra-functional aspects are still interwoven in components. In this paper, we address how shared data spaces can support the separation of concerns. In particular, we present a solution that allows developers to merely specify availability requirements for data tuples, while the underlying middleware evaluates various distribution and replication policies in order to select the one that meets these requirements best. Moreover, in our approach, the middleware continuously monitors the behavior of application and system components, and switches to different policies if this would lead to better results. We describe our approach, along with the design of a prototype implementation and its quantitative evaluation.