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.