Decomposed Task Mapping to Maximize QoS in Energy-Constrained Real-Time Multicores

Abstract : Multicore architectures are now widely used in energy-constrained real-time systems, such as energy-harvesting wireless sensor networks. To take advantage of these multicores, there is a strong need to balance system energy, performance and Quality-of-Service (QoS). The Imprecise Computation (IC) model splits a task into mandatory and optional parts allowing to tradeoff QoS. The problem of mapping, i.e. allocating and scheduling, IC-tasks to a set of processors to maximize system QoS under real-time and energy constraints can be formulated as a Mixed Integer Linear Programming (MILP) problem. However, state-of-the-art solving techniques either demand high complexity or can only achieve feasible (suboptimal) solutions. In this paper, we develop an effective decomposition-based approach to achieve an optimal solution while reducing computational complexity. It decomposes the original problem into two smaller easier-to-solve problems: a master problem for IC-tasks allocation and a slave problem for IC-tasks scheduling. We also provide comprehensive optimality analysis for the proposed method. Through the simulations, we validate and demonstrate the performance of the proposed method, resulting in an average 55% QoS improvement with regards to published techniques.
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Submitted on : Monday, November 13, 2017 - 1:31:32 PM
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Lei Mo, Angeliki Kritikakou, Olivier Sentieys. Decomposed Task Mapping to Maximize QoS in Energy-Constrained Real-Time Multicores. 35th IEEE International Conference on Computer Design (ICCD), Nov 2017, Boston, United States. pp.6. ⟨hal-01633782⟩

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