Abstract

Automated deployment of software components into hardware resources is a highly constrained optimisation problem. Hardware memory limits which components can be deployed into the particular hardware unit. Interacting software components have to be deployed either into the same hardware unit, or connected units. Safety concerns could restrict the deployment of two software components into the same unit. All these constraints hinder the search for high quality solutions that optimise quality attributes, such as reliability and communication overhead. When the optimisation problem is multi-objective, as it is the case when considering reliability and communication overhead, existing methods often fail to produce feasible results. Moreover, this problem can be modelled by bipartite graphs with complicating constraints, but known methods do not scale well under the additional restrictions. In this paper, we develop a novel multi-objective Beam search and ant colony optimisation (Beam-ACO) hybrid method, which uses problem specific bounds derived from communication, co-localisation and memory constraints, to guide the search towards feasibility. We conduct an experimental evaluation on a range of component deployment problem instances with varying levels of difficulty. We find that Beam-ACO guided by the co-localisation constraint is most effective in finding high quality feasible solutions.

Highlights

  • Software component deployment is a relevant optimisation problem in many domains

  • We investigate the efficacy of Beam Ant Colony System (BACS) on this multi-objective version of the problem and determine which problem characteristics lead to the most effective BACS implementation

  • The emphasis is first on feasibility since if feasible solutions are not found the high values of reliability and communication overhead do not mean much, as the solution can not be implemented in a real scenario

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Summary

Introduction

Software component deployment is a relevant optimisation problem in many domains. A growing number of functionalities have to be implemented as software programs and deployed to the hardware infrastructure of a car. The number of potential assignments between software components and hardware units is restricted by a number of hard constraints. Some software requires access to sensors which mandates its positioning on a host residing on the same bus, others cannot be located on the same hardware for safety reasons. Component deployment optimisation has to fulfill multiple goals, such as reliability, cost, safety, and performance, which can be conflicting, the problem is modeled as a multi-objective optimisation problem. Previous approaches to multi-objective models of the automotive component deployment problem comprise bi- and tri-objective formulations. Multi Objective Genetic Algorithm (MOGA) [1], Non-dominated Sorting Genetic Algorithm II (NSGA-II) [2], and Population-Ant

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