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- Title
UMOTS: an uncertainty-aware multi-objective genetic algorithm-based static task scheduling for heterogeneous embedded systems.
- Authors
Raji, Mohsen; Nikseresht, Mohaddaseh
- Abstract
Increasing manufacturing process variations due to aggressive technology scaling in addition to heterogeneity in design components are expected to cause serious challenges for future embedded system design steps including task scheduling. Process variation effects along with increased complexity in embedded applications result in design uncertainties, which in turn, reduce the accuracy and efficiency of traditional design approaches with deterministic values for the design component parameters. In this paper, a multi-objective task scheduling framework is proposed for embedded systems considering uncertainties in both hardware and software component parameters. The tasks which are modeled as a task graph are scheduled on a specific hardware platform consisting of processors and communication parts. Uncertainty is considered in both software (task parameters) and hardware (processor and communication parameters) of the embedded system. UMOTS takes advantages of a Monte-Carlo-based approach within a multi-objective genetic algorithm to handle the uncertainties in model parameters. The proposed approach finds the Pareto frontier, which is robust against uncertainties, in the objective space formed by performance, energy consumption, and reliability. The efficiency of UMOTS is investigated in the experimental results using real-application task graphs. In terms of Scheduling Length Ratio (SLR) and speedup, UMOTS provides 27.8% and 28.6% performance improvements in comparison to HSHD, one state-of-the-art task scheduling algorithm. Additionally, UMOTS, which is based on a multi-objective genetic optimization algorithms, finds robust Pareto frontier with 1%, 5% and 10% uncertainty in design indicators with respect to design limitations.
- Subjects
TELECOMMUNICATION equipment; GENETIC algorithms; SCHEDULING; SYSTEMS design
- Publication
Journal of Supercomputing, 2022, Vol 78, Issue 1, p279
- ISSN
0920-8542
- Publication type
Article
- DOI
10.1007/s11227-021-03887-1