A Dynamic Multi-Objective Evolutionary Algorithm for Nontrivial Upper Bounds of Real-Time Tasks in Embedded System Design
Sponsor
The authors wish to thank Natural Science Foundation of China under Grant No. 61662054, 61262082, Natural Science Foundation of Inner Mongolia under Grand No.2015MS0608, Inner Mongolia Science and Technology Innovation Team of Cloud Computing and Software Engineering and Inner Mongolia Application Technology Research and Development Funding Project “Mutual Creation Service Platform Research and Development Based on Service Optimizing and Operation Integrating”, Inner Mongolia Engineering Lab of Cloud Computing and Service Software and Inner Mongolia Engineering Lab of Big Data Analysis Technology.
Published In
2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)
Document Type
Citation
Publication Date
2018
Abstract
In the real-time embedded system, how to schedule more real-time tasks has been a difficult point. The nontrivial upper bound of execution time of the real-time task is the key. The analysis of hit-miss behavior of the instruction cache is a problem for the estimation of the nontrivial upper bound, especially more difficult after using shared set-associative instruction caches. And the computation of results for the dynamic problem is a challenge due to the shortcomings of the classic method ILP. In this paper, we present a model for the prediction of nontrivial upper bound, prove that the prediction of the hit-miss number of the shared set-associative instruction cache is a dynamic multi-objective optimization problem, and design a dynamic multi-objective optimization algorithm for the estimation of solution. Simulation experiments demonstrate the effectiveness of our approach for computing the nontrivial upper bound.
Locate the Document
DOI
10.1109/SmartWorld.2018.00082
Persistent Identifier
https://archives.pdx.edu/ds/psu/28121
Citation Details
Xing, H., Zhou, J., Song, X., & Qi, R. (2018, October). A Dynamic Multi-Objective Evolutionary Algorithm for Nontrivial Upper Bounds of Real-Time Tasks in Embedded System Design. In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 277-286). IEEE.
Description
©2018 IEEE