CaDAnCE: A Criticality-Aware Deployment and Configuration Engine

Predictable deployment and configuration (D&C) of components in response to dynamic environmental changes or system mode changes is essential for ensuring open distributed real-time and embedded (DRE) system real-time QoS. This paper provides three contributions to research on the predictability of D&C for component-based open DRE systems. First, we describe how the dependency relationships among different components and their criticality levels can cause deployment order inversion of tasks, which impedes deployment predictability. Second, we describe how to minimize D&C latency of mission-critical tasks with a multi-graph dependency tracing and graph recomposition algorithm called CaDAnCE. Third, we empirically evaluate the effectiveness of CaDAnCE on a representative open DRE system case study based on NASA Earth Science Enterprise's Magnetospheric Multi-Scale (MMS) mission system. Our results show that CaDAnCE avoids deployment order inversion while incurring negligible (<1%) performance overhead, thereby significantly improving D&C predictability.