Embedded Computing Design

In part 1 and part 2 of this series, we talked about two key real-time scheduling algorithms and how they fit into our real-time database goals. We also covered why scheduling alone isn’t enough, and how precise time measurement, predictable rollback, well-placed verification points, and a bit of real-world profiling are what actually make “on time” possible.

In Part 1 of this series, we talked about two key real-time scheduling algorithms — Earliest Deadline First (EDF) and Priority Inheritance (PI) — and how they tie into our real-time database management goals. Those algorithms decide when each transaction should run, but that’s only the beginning.

In hard real-time systems, deadlines aren’t suggestions, they draw the line between working and failing. Every task must finish exactly when it should; even a tiny slip can ripple through and throw everything else off balance.

Open-source and commercial database management systems (DBMSs) have been used in embedded systems for more than 20 years. For the most part, developers don't even debate whether to buy or build their own DBMS for embedded systems; that component is almost always bought.

The overall purpose of collecting data on the edge has shifted from purely device control and monitoring to improving various service capabilities through real-time analysis.

IoT devices span all aspects of modern life ? industrial, automotive, medical, and commercial applications that affect us all.