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eal-time systems with hard timing constrains require known upper bounds on each task’s worst-case execution time (WCET) to determine if all deadlines can be met. One challenge in predictable execution is that Dynamic Random Access Memory (DRAM) cells must be refreshed periodically to maintain data validity, yet memory remains blocked during refresh, which results in overly pessimistic WCET bounds. This work contributes “Colored Refresh” to hide DRAM refresh overhead while preserving real-time schedulability for cyclic executives, which are widely used in highly critical systems. Colored Refresh partitions DRAM memory at rank granularity such that refreshes rotate round-robin from rank to rank. Real-time tasks are assigned different ranks via colored memory allocation. By cooperatively scheduling real-time tasks and refresh operations, memory requests no longer suffer from refresh interference. This reduces memory access latencies for tasks irrespective of DRAM density and size. Hence, Colored Refresh reduces a task’s WCET and makes its execution more predictable. 

Bounding each task’s worst-case execution time (WCET) accurately is essential for real-time systems to determine if all deadlines can be met. Yet, access latencies to Dynamic Random Access Memory (DRAM) vary significantly due to DRAM refresh, which blocks access to memory cells. Variations further increase as DRAM density grows. This work contributes the “Colored Refresh Server” (CRS), a uniprocessor scheduling paradigm that partitions DRAM in two distinctly colored groups such that refreshes of one color occur in parallel to the execution of real-time tasks of the other color. By executing tasks in phase with periodic DRAM refreshes with opposing colors, memory requests no longer suffer from refresh interference. Experimental results confirm that refresh overhead 

Past work has investigated intrusion detection mechanisms for real-time control devices. This work contributes a novel framework of separating security monitoring and detection from real-time control, where the former is performed on Cloud edge devices while the latter is run on embedded devices attached to the system that is controlled. We contribute a security monitoring system that validates worst-case timing bounds of the target controller and also validates its control outputs by comparing it against model-based predictions, which are derived from machine learning.