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