Scalable performance-energy trade-off exploration of embedded real-time systems on multiprocessor platforms

Conventional task scheduling on real-time systems with multiple processors is notorious for its computational intractability. This problem becomes even harder when designers also have to consider other constraints such as energy consumptions. Such a multi-objective trade-off exploration is a crucial step to generating cost-efficient real-time embedded systems. Although previous task schedulers have attempted to provide fast heuristics for design space exploration, they cannot handle large systems efficiently. As today's embedded systems become increasingly larger we need a scalable scheduler to handle this complexity. This paper presents a hierarchical scheduler that combines the graph partition and the task interleaving to tackle the tradeoff exploration problem in a scalable way. Our scheduler can employ the existing flattened scheduler and significantly accelerate the design space explorations for large tasks. The speed-up of up to 2 orders of magnitude has been obtained for large task models compared to the conventional flattened scheduler.