In modern communication systems, it is necessary to develop new approaches to optimize them. To achieve this objective, new paradigms are necessary. The present work proposes an autonomic communication system, which uses a system of decision-making based on a Bayesian network, and a knowledge base based on an ontology, to take decisions of reconfiguration in the system. The main goal of the autonomic communication system is to adapt the communications platform to the characteristics of the context, in order to improve its performance. The ontology provides the knowledge about the performance factors and their relationships in the communication networks, to configure the structure of the Bayesian network. The Bayesian network is the stochastic reasoning mechanism used by the decision-making system. We present several tests on the NS2 simulator. In the tests carried out, we observe an improvement between 85% and 96% in the system performance, for scenarios where our approach of autonomic decision reconfigures the communication system.
AguilarJ., Definition of an energy function for the random neural to solve optimization problems, Neural Networks1 (1998), 731–737.
2.
AguilarJ.SubiasA.Travé-MassuyésL. and ZouaouiK., Situation Assessment in Autonomous Systems,Proceedings of the 4th Global Information Infrastructure and Networking Symposium, IEEE Communication Society, 2012.
3.
AguilarJ.JerezM.ExpositoE. and VillemurT., CARMiCLOC: Context awareness middleware in cloud computing, Proc Latin American Computing Conference (CLEI) (2015), 1–10.
4.
AguilarJ.BuendiaO.MorenoK. and MosqueraD., Autonomous cycle of data analysis tasks for learning processes, in: Technologies and Innovation, Communications Computer and Information Science SeriesValencia-GarcíaR. et al., eds, Springer 658 (2016), pp. 187–202.
5.
AguilarJ.TorresJ. and AguilarK., Autonomic decision making based on bayesian networks and ontologies, Proc IEEE International Joint Conference on Neural Networks (2016), 3825–3832.
6.
BencomoN. and BelaggounA., Supporting decision-making for self-adaptive systems: From goal models to dynamic decision networks, Lecture Notes in Computer Science7830 (2013), 221–236.
7.
Ben-GalI.F.FaltinF. and KenettR., Bayesian Networks, Encyclopedia of Statistics in Quality & Reliability, Wiley & Sons, 2007.
CrowleyM., Evaluating influence diagrams where we’ve been and where we’re going, British Columbia: University of British Columbia, 2004.
10.
DobsonS.MassacciF. and DenazisS., A survey of autonomic communications, ACM Transactions on Autonomous and Adaptive Systems1(2) (2006), 223–259.
11.
ExpositoE., Méthodologie modèles et paradigmes pour la conception d’une couche transport de nouvelle génération, Toulouse: LAAS-CNRS, 2010.
12.
ExpositoE.LamolleM. and Gómez-MontalvoJ., Introducing an ontology driven architecture for distributed, Proc 2010 Second International Conference on Information, Process, and Knowledge Management (2010), 7–12.
13.
GhahramaniZ., The Handbook of Brain Theory and Neural Networks, in: Graphical models: parameter learningArbidM., eds, MIT Press, 2003, pp. 486–490.
14.
HelspeE.M. and van der GaagL.C., Building Bayesian networks through ontologies, Proc 15th European Conference on Artificial Intelligence (2002), 680–684.
15.
HudecM. and FenzS., Ontology-based generation of Bayesian networks, Proc International Conference on Complex, Intelligent and Software Intensive Systems (2009), 712–717.
16.
IBM, An architectural blueprint for autonomic computing, New Tork: IBM. IETF, Internet Engineering Task Force, https://tools.ietf.org/html/rfc1122 (2005).
17.
KephartJ. and ChessD., The vision of autonomic computing, IEEE Computer Society3 (2003), 41–50.
18.
KjaerulffU.B. and MadsenA.L., Bayesian Networks and Influence Diagrams, Aalborg Denmark: Springer, 2005.
19.
NorsysS.C., Netica API Programmer’s Library Reference Manual Version 4.18, Vancouver, BC, Canada: Norsys Software Corp, 2010.
20.
QiJ.ZhangS.CaoL.SunY. and TanL., Cognitive networks autonomic decision-making approach based on influence diagram, Journal of Networks7(5) (2012), 876–883.
21.
RongP.ZhongliD. and PengY., A bayesian network approach to ontology mapping, Baltimore, Maryland, USA, 2006.
22.
RongP.ZhongliD. and PengY., BayesOWL: Uncertainty modeling in semantic web ontologies, Soft Computing in Ontologies and Semantic Web Studies in Fuzziness and Soft Computing204 (2006b), 3–29.
23.
DobsonS.DenazisS.FernándezA.GaïtiD.GelenbeE.MassacciF.NixonP.SaffreF.SchmidtN. and ZambonelliF., A survey of autonomic communications, ACM Transactions on Autonomous and Adaptive Systems1(2) (2006), 223–259.
TenenbaumJ.GriffithsT. and KempC., Theory-based Bayesian models of inductive learning and reasoning, TRENDS in Cognitive Sciences10(7) (2006), 309–318.
26.
VanN., PhD Thesis: Une approche pour la composition autonome de services de communication orientés QoS. Application aux protocoles de transport configurables, Toulouse: Mathématiques Informatique Télécommunications de Toulouse (MITT), 2009.
27.
VizcarrondoJ.AguilarJ.ExpositoE. and SubiasA., ARMISCOM: Autonomic reflective middleware for management service composition, Proc Global Information Infrastructure and Networking Symposium (GIIS) (2012), 1–8.
28.
VizcarrondoJ.AguilarJ.ExpositoE. and SubiasA., MAPE-K as a service-oriented architecture, IEEE Latin America Transactions15 (2017), 1163–1175.
29.
ZaragozaJ.H.SucarE. and MoralesE.F., A two-step method to learn multidimensional Bayesian network classifiers based on mutual information measures, Proc Twenty-Fourth International Florida Artificial Intelligence Research Society Conference (2011), 644–649.
30.
Gómez-MontalvoJ., MODA: A multimedia ontology-driven architecture for autonomic quality of service management in home networks, PhD Thesis, LAAS-CNRS, 2012.
31.
HowardR.A. and MathesonJ.E., Influence diagrams, in: Readings on the Principles and Applications of Decision AnalysisHowardR.A. and MathesonJ.E., eds, Vol. II, Strategic Decisions Group, Menlo Park, California, 1981, pp. 719–762.
