This paper presents a dynamic multi-application scheduling approach for running and scheduling parallel adaptive applications in Networks of Workstations (NOWs) and Clusters of Processors (COPs). Parallel adaptive applications have the property of varying their parallelism degree dynamically following availability of resources and changes in the underlying environment state. In our model, each parallel adaptive application is controlled by its own scheduler responsible for optimizing resources which it uses. Multi-application scheduling consists of sharing resources among applications fairly and uses a combined (time-sharing and space-sharing) scheduling approach. The scheduling approach is characterized especially by dynamic arrivals of applications, and remapping of allocation in order to handle dynamic arrivals and departures of applications and underlying environment state changes. Therefore, resources are fairly shared among applications. The multi-application scheduler interacts with the application schedulers in order to optimize the scheduling approach. The application schedulers are responsible for applying the multi-application scheduler decisions and internally optimizing the resources exploited by the application. Beyond the adaptive aspect which allows parallel applications to exploit idle cycles in NOWs with respect to the personal character of workstations, the proposed model provides a multi-application scheduling support which globally ensures better resource utilization and fair resource sharing.