Mapping series-parallel streaming applications on hierarchical platforms with reliability and energy constraints

• Published in: Journal of parallel and distributed computing Vol. 163; pp. 45 - 61
• Main Authors: Gou, Changjiang, Benoit, Anne, Chen, Mingsong, Marchal, Loris, Wei, Tongquan
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.05.2022; Elsevier
• Subjects: Computer Science; Distributed, Parallel, and Cluster Computing; Energy; Hierarchical platforms; Reliability; Series-parallel streaming applications; Task mapping; Task mapping; Series-parallel streaming applications; Energy; Hierarchical platforms; Reliability; building block; dependence graph; Dynamic voltage and frequency scaling
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2022.01.016

Streaming applications come from various application fields such as physics, where data is continuously generated and must be processed on the fly. Typical streaming applications have a series-parallel dependence graph, and they are processed on a hierarchical failure-prone platform, as for instance in miniaturized satellites. The goal is to minimize the energy consumed when processing each data set, while ensuring real-time constraints in terms of processing time. Dynamic voltage and frequency scaling (DVFS) is used to reduce the energy consumption, and we ensure a reliable execution by either executing a task at maximum speed, or by triplicating it, so that the time to execute a data set without failure is bounded. We propose a structure rule to partition the series-parallel applications and map the application onto the platform, and we prove that the optimization problem is NP-complete. We design a dynamic-programming algorithm for the special case of linear chains, which is optimal for a special class of schedules. Furthermore, this algorithm provides an interesting heuristic and a building block for designing heuristics for the general case. The heuristics are compared to a baseline solution, where each task is executed at maximum speed. Simulations on realistic settings demonstrate the good performance of the proposed heuristics; in particular, significant energy savings can be obtained. •Minimizing the energy consumption of series-parallel streaming applications.•Reliable execution with maximum speed execution or triplication.•Design of a dynamic programming algorithm and clever mapping heuristics.•Good performance of heuristics compared to baseline on realistic settings.