Safe Configurations of Replica Voting Processes in Fault-Resilient Data Collection Services

Voting among replicated sensor devices achieves a timely delivery of correct data to the end-user in a hostile environment. Enforcement of this safety prescription by the voting system depends on the hostility of environment, system parameters & resources (network bandwidth and device replication), and input data characteristics. How severely the faulty devices induce data corruptions and timeliness errors impacts the quality of information (QoI) in data delivery. We consider situations where the network bandwidth varies dynamically, device replication faces operational constraints, and environment parameters change unpredictably. An adaptation management module H exercises control of the voting system based on application context and external threats. H determines the safe configurations of voting system: i.e., the device replication and system resource allocation, to sustain an acceptable QoI.

[1]  Alejandro P. Buchmann,et al.  Managing Expectations: Runtime Negotiation of Information Quality Requirements in Event-Based Systems , 2014, ICSOC.

[2]  David H. Ackley,et al.  Building diverse computer systems , 1997, Proceedings. The Sixth Workshop on Hot Topics in Operating Systems (Cat. No.97TB100133).

[3]  John Keeney,et al.  Chisel: a policy-driven, context-aware, dynamic adaptation framework , 2003, Proceedings POLICY 2003. IEEE 4th International Workshop on Policies for Distributed Systems and Networks.

[4]  Yuhui Shi,et al.  chapter two – Computational intelligence , 2007 .

[5]  Marcus Brunner,et al.  Probabilistic decentralized network management , 2009, 2009 IFIP/IEEE International Symposium on Integrated Network Management.

[6]  Klara Nahrstedt,et al.  A control-based middleware framework for quality-of-service adaptations , 1999, IEEE J. Sel. Areas Commun..

[7]  Carlo Ghezzi,et al.  Autotuning control structures for reliability-driven dynamic binding , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[8]  Priya Narasimhan,et al.  Experiences with a CANoe-based fault injection framework for AUTOSAR , 2010, 2010 IEEE/IFIP International Conference on Dependable Systems & Networks (DSN).

[9]  Matti A. Hiltunen,et al.  Cholla: A Framework for Composing and Coordinating Adaptations in Networked Systems , 2009, IEEE Transactions on Computers.

[10]  Chan-Gun Lee,et al.  Incorporating Resource Safety Verification to Executable Model-based Development for Embedded Systems , 2008, 2008 IEEE Real-Time and Embedded Technology and Applications Symposium.

[11]  Jiang Wu,et al.  Engineering of replica voting protocols for energy-efficiency in data delivery , 2006, 2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks(WoWMoM'06).

[12]  Carl E. Landwehr,et al.  Basic concepts and taxonomy of dependable and secure computing , 2004, IEEE Transactions on Dependable and Secure Computing.

[13]  Kaliappa Nadar Ravindran,et al.  Replica Voting: a Distributed Middleware Service for Real-time Dependable Systems , 2006, 2006 1st International Conference on Communication Systems Software & Middleware.

[14]  Arnd Poetzsch-Heffter,et al.  Slicing for model reduction in adaptive embedded systems development , 2008, SEAMS '08.

[15]  A. Polychronopoulos,et al.  Multiple sensor collision avoidance system for automotive applications using an IMM approach for obstacle tracking , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[16]  Nancy G. Leveson.  Software Challenges in Achieving Space Safety , 2009 .