Information Technology Reference
In-Depth Information
8
Conclusions
This paper introduces RVPF, a versatile method for runtime verification with state es-
timation in which the balance among runtime overhead, memory usage, and prediction
accuracy can be controlled by varying the number of particles RVPF uses for state es-
timation. Our benchmarking results confirm RVPF's flexibility and its superiority over
RVSE and AP-RVSE in terms of state-estimation accuracy.
Although RVPF cannot match the speed of AP-RVSE, its relatively low memory
footprint gives it an advantage in the context of embedded systems, where memory
resources are limited. Our results also show that RVPF can be configured to outperform
RVSE without significantly impacting the accuracy of state estimation.
As future work, we are developing a version of RVPF where the number of particles
used for state estimation can vary at runtime. This would allow for dynamic control of
the tradeoff involving estimation accuracy, memory consumption, and speed.
Acknowledgements.
We thank Justin Seyster for introducing the concept of peek
events and developing the micro-benchmarking facility on which our experimental re-
sults are based.
References
1. Bartocci, E., Grosu, R., Karmarkar, A., Smolka, S.A., Stoller, S.D., Zadok, E., Seyster, J.:
Adaptive runtime verification. In: Qadeer, S., Tasiran, S. (eds.) RV 2012. LNCS, vol. 7687,
pp. 168-182. Springer, Heidelberg (2013)
2. Blom, H.A.P., Bloem, E.: Particle filtering for stochastic hybrid systems. In: Proceedings of
43rd IEEE Conference on Decision and Control, CDC 2004, vol. 3, pp. 3221-3226 (2004)
3. Doucet, A.: Monte Carlo Methods for Bayesian Estimation of Hidden Markov Models. Ap-
plication to Radiation Signals. Ph.D. Thesis (1997)
4. Doucet, A.: On sequential simulation-based methods for bayesian filtering. Technical Report
CUED-F-ENG-TR310, University of Cambridge, Department of Engineering (1998)
5. Huang, X., Seyster, J., Callanan, S., Dixit, K., Grosu, R., Smolka, S.A., Stoller, S.D., Zadok,
E.: Software monitoring with controllable overhead. STTT 14(3), 327-347 (2012)
6. Koutsoukos, X.D., Kurien, J., Zhao, F.: Estimation of distributed hybrid systems using par-
ticle filtering methods. In: Maler, O., Pnueli, A. (eds.) HSCC 2003. LNCS, vol. 2623,
pp. 298-313. Springer, Heidelberg (2003)
7. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice-Hall
(2010)
8. Sistla, A.P., Zefran, M., Feng, Y.: Runtime monitoring of stochastic cyber-physical systems
with hybrid state. In: Khurshid, S., Sen, K. (eds.) RV 2011. LNCS, vol. 7186, pp. 276-293.
Springer, Heidelberg (2012)
9. Sistla, A.P., Zefran, M., Feng, Y.: Monitorability of stochastic dynamical systems. In:
Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 720-736. Springer,
Heidelberg (2011)
10. Stoller, S., Bartocci, E., Seyster, J., Grosu, R., Havelund, K., Smolka, S., Zadok, E.: Runtime
verification with state estimation. In: Khurshid, S., Sen, K. (eds.) RV 2011. LNCS, vol. 7186,
pp. 193-207. Springer, Heidelberg (2012)
