Adaptive resource allocation for embedded parallel applications

Parallel and distributed computer architectures are increasingly being considered for application in a wide variety of computationally intensive embedded systems. Many such applications impose highly dynamic demands for resources (processors, memory, and communication network), because their computations are data-dependent, or because the applications must constantly interact with a rapidly changing physical environment, or because the applications themselves are adaptive. This paper presents a set of dynamic resource allocation techniques aimed at maintaining high levels of application performance in the presence of varying resource demands. It focuses on a class of applications structured as multiple pipelines of data-parallel stages, as this structure is common to many sensor-based applications. We discuss the issues involved in resource management for such applications, and present preliminary results from our implementations on Intel Paragon. Our approach uses feedback control - a real-rime monitoring system is used to detect significant performance shortfalls, and resources are reallocated among the application components in an attempt to improve performance. The main contribution of this work is that it combines real-time monitoring of an application's performance with dynamic resource allocation, and focuses on practical implementations rather than simulation and analysis.