Paper Info
Title
A Dynamic Multi-Objective Evolutionary Algorithm for Nontrivial Upper Bounds of Real-Time Tasks in Embedded System Design
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.
Year
DOI
Venue
2018
10.1109/SmartWorld.2018.00082
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)
Keywords
Field
DocType
multi-knapsack,nontrivial upper bound,dynamic multi-objective optimization,shared instruction cache behavior,timing analysis
Evolutionary algorithm,Upper and lower bounds,Computer science,Cache,Embedded system design,Parallel computing,Static timing analysis,Optimization problem,Dynamic problem,Computation,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-5386-9381-0
0
0.34
References 
Authors
13
4
Name
Order
Citations
PageRank
Hai-feng Xing100.34
Jiantao Zhou223.41
Xiaoyu Song331846.99
Rui-dong Qi400.34