A mixed integer linear programming model for reliability optimisation in the component deployment problem

Component deployment is a combinatorial optimisation problem in software engineering that aims at finding the best allocation of software components to hardware resources in order to optimise quality attributes, such as reliability. The problem is often constrained because of the limited hardware re...

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Bibliographic Details
Published in:The Journal of the Operational Research Society Vol. 67; no. 8; pp. 1050 - 1060
Main Authors: Nazari, Asef, Thiruvady, Dhananjay, Aleti, Aldeida, Moser, Irene
Format: Journal Article
Language:English
Published: London Taylor & Francis 01.08.2016
Palgrave Macmillan
Palgrave Macmillan UK
Taylor & Francis Ltd
Subjects:
Approximation
Business and Management
Combinatorial analysis
Component reliability
General Paper
General Papers
Hardware
Heuristic methods
Integer programming
Linear programming
Management
Mixed integer
Operations Research/Decision Theory
Optimization
Quality management
reliability
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Software engineering
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linear programming
integer programming
reliability
ISSN:0160-5682, 1476-9360
Online Access:Get full text
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Summary:Component deployment is a combinatorial optimisation problem in software engineering that aims at finding the best allocation of software components to hardware resources in order to optimise quality attributes, such as reliability. The problem is often constrained because of the limited hardware resources, and the communication network, which may connect only certain resources. Owing to the non-linear nature of the reliability function, current optimisation methods have focused mainly on heuristic or metaheuristic algorithms. These are approximate methods, which find near-optimal solutions in a reasonable amount of time. In this paper, we present a mixed integer linear programming (MILP) formulation of the component deployment problem. We design a set of experiments where we compare the MILP solver to methods previously used to solve this problem. Results show that the MILP solver is efficient in finding feasible solutions even where other methods fail, or prove infeasibility where feasible solutions do not exist.
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ISSN:0160-5682
1476-9360
DOI:10.1057/jors.2015.119