Distributed real-time embedded (DRE) systems often operate in open environments where operating conditions, input workload, and resource availability cannot be accurately characterized a priori. Some DRE systems, such as NASA's Magnetospheric Multi-Scale (MMS) mission, perform sequences of heterogeneous data collection, manipulation, and coordination tasks to meet specified objectives/goals. These systems are also required to operate with a high degree of local autonomy and adaptivity as new data is acquired and analyzed, and as environmental conditions change. Key challenges in managing open DRE systems include effective planning and online management of system resources to accommodate for changing mission goals, environmental conditions, resource needs, and resource availability. This paper explores the benefits of an integrated planning and adaptive resource management architecture that combines decision-theoretic planning with adaptive resource management to control and ensure efficient functioning of open DRE systems.