Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICCD.2017.86 | 2017 IEEE International Conference on Computer Design (ICCD) |
Keywords | Field | DocType |
Multicore architectures,task mapping,real-time and energy constraints,QoS,MILP,Benders decomposition | Computer science,Scheduling (computing),Parallel computing,Task mapping,Imprecise computation,Quality of service,Integer programming,Multi-core processor,Wireless sensor network,Computational complexity theory,Distributed computing | Conference |
ISSN | ISBN | Citations |
1063-6404 | 978-1-5386-2255-1 | 1 |
PageRank | References | Authors |
0.35 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Mo | 1 | 29 | 7.35 |
| Angeliki Kritikakou | 2 | 66 | 12.85 |
| Olivier Sentieys | 3 | 597 | 73.35 |