Constraint Programming and Ant Colony System for the Component Deployment Problem
Author(s) -
Dhananjay Thiruvady,
Irene Moser,
Aldeida Aleti,
Asef Nazari
Publication year - 2014
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2014.05.178
Subject(s) - computer science , software deployment , software , automotive industry , component based software engineering , component (thermodynamics) , constraint programming , software system , embedded system , distributed computing , mathematical optimization , operating system , physics , mathematics , stochastic programming , engineering , thermodynamics , aerospace engineering
Contemporary motor vehicles have increasing numbers of automated functions to augment the safety and comfort of a car. The automotive industry has to incorporate increasing numbers of processing units in the structure of cars to run the software that provides these functionalities. The software components often need access to sensors or mechanical devices which they are designed to operate. The result is a network of hardware units which can accommodate a limited number of software programs, each of which has to be assigned to a hardware unit. A prime goal of this deployment problem is to find software-to-hardware assignments that maximise the reliability of the system. In doing so, the assignments have to observe a number of constraints to be viable. This includes limited memory of a hardware unit, collocation of software components on the same hardware units, and communication between software components. Since the problem consists of many constraints with a significantly large search space, we investigate an ACO and constraint programming (CP) hybrid for this problem. We find that despite the large number of constraints, ACO on its own is the most effective method providing good solutions by also exploring infeasible regions
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