Minimizing Energy Consumption for Real-Time Tasks on Heterogeneous Platforms Under Deadline and Reliability Constraints

Low energy consumption and high reliability are widely identified as increasingly relevant issues in real-time systems on heterogeneous platforms. In this paper, we propose a multi-criteria optimization strategy to minimize the expected energy consumption while enforcing the reliability threshold and meeting all task deadlines. The tasks arrive periodically. Each instance of a task is replicated to ensure a prescribed reliability threshold. The platform is composed of processors with different (and possibly unrelated) characteristics, including speed profile, energy cost and failure rate. We provide several mapping and scheduling heuristics to solve this challenging optimization problem. Specifically, a novel approach is designed to control (i) how many replicas to use for each task, (ii) on which processor to map each replica and (iii) when to schedule each replica for each task instance on its assigned processor. Different mappings achieve different levels of reliability and consume different amounts of energy. Scheduling matters because once a task replica is successful, the other replicas of that task instance are canceled, which calls for minimizing the amount of temporal overlap between any replica pair. The experiments are conducted for a comprehensive set of execution scenarios, with a wide range of processor speed profiles and failure rates. The comparison results reveal that our strategies perform better than the random baseline, with a gain in energy consumption of more than 40% for nearly all cases. The absolute performance of the heuristics is assessed by a comparison with a lower-bound; the best heuristics achieve an excellent performance. It saves only 2% less energy than the lower-bound.