Framework for Intelligent Message Routing Policy Adaptation

Monitoring message workflow transmission is a very challenging problem, especially in pervasive environments, mainly because of the wide range of unexpected events (e.g. Human and material resources unavailability) and context changes (e.g. Source and target localizations) that may occur at run-time. In this paper, we propose an information system and services orchestration framework enabling intelligent message routing policy adaptation. Our objective is to build a reliable routing strategy that can autonomously and intelligently adapt its own behavior and decisions according to source and target context changes as well as to controlled message status (e.g. Exceeded deadlines for message reception). We present a solution that emphasizes some cutting-edge characteristics that we believe are crucial for enhancing the quality of message communication, such as intelligence, controllability, scalability, adaptivity and personalization. The routing decisions can be adapted at different levels of decision-making such as message itinerary, delay for message treatment, etc., by means of advanced AI methods that we detail for some of the most sensitive self-adaptive services.

[1]  Habib Youssef,et al.  Autonomic architecture for wireless routing protocols , 2010, ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010.

[2]  WenAn Tan,et al.  SLA detective control model for workflow composition of cloud services , 2013, Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[3]  Peter Paluch,et al.  Towards the autonomic network management and context base routing , 2014, 2014 ELEKTRO.

[4]  David Sinreich,et al.  An architectural blueprint for autonomic computing , 2006 .

[5]  Xin Yao,et al.  Architecting Self-Aware Software Systems , 2014, 2014 IEEE/IFIP Conference on Software Architecture.

[6]  Luiz Fernando Bittencourt,et al.  Execution of service workflows in grid environments , 2009, 2009 5th International Conference on Testbeds and Research Infrastructures for the Development of Networks & Communities and Workshops.

[7]  Kun Yang,et al.  Dynamic algorithms for autonomic pervasive services in mobile wireless environments , 2010, Int. J. Auton. Comput..

[8]  Zoubin Ghahramani,et al.  Learning Dynamic Bayesian Networks , 1997, Summer School on Neural Networks.

[9]  T. A. Gonsalves,et al.  Employing Bayesian Belief Networks for energy efficient Network Management , 2010, 2010 National Conference On Communications (NCC).

[10]  S. Gregori,et al.  An adaptive QoS and energy-aware routing algorithm for wireless sensor networks , 2008, 2008 International Conference on Information and Automation.