Monitorability of Stochastic Dynamical Systems

Sistla, A. Prasad; Žefran, Miloš; Feng, Yunli

doi:10.1007/978-3-642-22110-1_58

Cited by 23 publications

(39 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A main aspect of our work is our approximation of the RVSE forward algorithm for state estimation, which pre-computes compound-state probability distributions and stores them in a graph. In the context of the runtime monitoring of HMMs, the authors of [10] propose a complementary method for accelerating the estimation of the current (hidden) state: Particle filters [4]. This sequential Monte-Carlo estimation method is particularly useful when the number of states of the HMM is very large, in particular, much larger than the number of particles (i.e., samples) necessary for obtaining a sufficiently accurate approximation.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Runtime Verification

Bartocci

Grosu

Karmarkar

et al. 2013

Runtime Verification

View full text Add to dashboard Cite

Abstract. We present Adaptive Runtime Verification (ARV), a new approach to runtime verification in which overhead control, runtime verification with state estimation, and predictive analysis are synergistically combined. Overhead control maintains the overhead of runtime verification at a specified target level, by enabling and disabling monitoring of events for each monitor instance as needed. In ARV, predictive analysis based on a probabilistic model of the monitored system is used to estimate how likely each monitor instance is to violate a given temporal property in the near future, and these criticality levels are fed to the overhead controllers, which allocate a larger fraction of the target overhead to monitor instances with higher criticality, thereby increasing the probability of violation detection. Since overhead control causes the monitor to miss events, we use Runtime Verification with State Estimation (RVSE) to estimate the probability that a property is satisfied by an incompletely monitored run. A key aspect of the ARV framework is a new algorithm for RVSE that performs the calculations in advance, dramatically reducing the runtime overhead of RVSE, at the cost of introducing some approximation error. We demonstrate the utility of ARV on a significant case study involving runtime monitoring of concurrency errors in the Linux kernel.

show abstract

Section: Related Workmentioning

confidence: 99%

Adaptive Runtime Verification

Bartocci

Grosu

Karmarkar

et al. 2013

Runtime Verification

View full text Add to dashboard Cite

show abstract

“…Particle filtering (PF) has recently been applied to hybrid systems for monitoring and diagnosis purposes, and in particular to estimate the hidden hybrid discrete-continuous state from a set of available measurements [6,2,8,9]. In [6], PF is applied to a class of distributed hybrid systems with autonomous transitions, non-linear system dynamics, and non-Gaussian noise.…”

Section: Related Workmentioning

confidence: 99%

“…In [2], the authors present a PF-based method for discrete-time stochastic hybrid systems, where each particle has two components: a Euclidean component representing the continuous state and a discrete component representing the mode. Their approach combines exact conditional mode probabilities, given the observations, with Sistla et al use PF to investigate the effectiveness of algorithms for monitorability and strong monitorability of partially observable stochastic systems [8,9]. Familiarity with PF is assumed and no further details, except for the number of particles used, are provided.…”

Section: Related Workmentioning

confidence: 99%

Runtime Verification with Particle Filtering

Kalajdzic

Bartocci

Smolka

et al. 2013

Runtime Verification

View full text Add to dashboard Cite

Abstract. We introduce Runtime Verification with Particle Filtering (RVPF), a powerful and versatile method for controlling the tradeoff between uncertainty and overhead in runtime verification. Overhead and accuracy are controlled by adjusting the frequency and duration of observation gaps, during which program events are not monitored, and by adjusting the number of particles used in the RVPF algorithm. We succinctly represent the program model, the program monitor, their interaction, and their observations as a dynamic Bayesian network (DBN). Our formulation of RVPF in terms of DBNs is essential for a proper formalization of peek events: low-cost observations of parts of the program state, which are performed probabilistically at the end of observation gaps. Peek events provide information that our algorithm uses to reduce the uncertainty in the monitor state after gaps.We estimate the internal state of the DBN using particle filtering (PF) with sequential importance resampling (SIR). PF uses a collection of conceptual particles (random samples) to estimate the probability distribution for the system's current state: the probability of a state is given by the sum of the importance weights of the particles in that state. After an observed event, each particle chooses a state transition to execute by sampling the DBN's joint transition probability distribution; particles are then redistributed among the states that best predicted the current observation. SIR exploits the DBN structure and the current observation to reduce the variance of the PF and increase its performance.We experimentally compare the overhead and accuracy of our RVPF algorithm with two previous approaches to runtime verification with state estimation: an exact algorithm based on the forward algorithm for HMMs, and an approximate version of that algorithm, which uses precomputation to reduce runtime overhead. Our results confim RVPF's versatility, showing how it can be used to control the tradeoff between execution time and memory usage while, at the same time, being the most accurate of the three algorithms.

show abstract

“…We show that the class of strongly monitorable systems introduced in [24] are exponentially converging monitorable. We also show that a subclass of well defined monitorable systems, called bounded uniform systems, are also exponentially converging monitorable.…”

Section: Introductionmentioning

confidence: 99%

“…We thus consider Hidden Markov Chains (HMC) to model such discrete state systems. In our earlier work [24,25], we addressed the problem of monitoring a system, modeled as a HMC H, when the correctness specification is given by a deterministic Streett automaton A on the computations of the system. There we considered accuracy measures of a monitor that capture its rates of false alarms and missed alarms.…”

Section: Introductionmentioning

confidence: 99%

Timely monitoring of partially observable stochastic systems

Sistla

Žefran

Feng

et al. 2014

Proceedings of the 17th International Conference on Hybrid Systems: Computation and Control

Self Cite

View full text Add to dashboard Cite

Ensuring the correct behavior of cyber physical systems at run time is of critical importance for their safe deployment.Any malfunctioning of such systems should be detected in a timely manner for further actions. This paper addresses the issue of how quickly a monitor raises an alarm after the occurrence of a failure in cyber physical systems. Towards this end, it introduces a class of systems called exponentially converging monitorable systems. The paper shows that failures in these systems can be detected fast by employing the traditional threshold monitors. It shows that the expected failure detection time for exponentially converging monitorable systems has logarithmic relationship with the inverse of the chosen threshold value. The paper identifies well defined natural classes of these systems. Experimental results are presented that confirm the theoretical results on the relationship between the failure detection time and the chosen threshold values.

show abstract

Monitorability of Stochastic Dynamical Systems

Cited by 23 publications

References 26 publications

Adaptive Runtime Verification

Adaptive Runtime Verification

Runtime Verification with Particle Filtering

Timely monitoring of partially observable stochastic systems

Contact Info

Product

Resources

About