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State Space Construction and SteadyState Solution of GSPNs on a SharedMemory Multiprocessor
, 1997
"... A common approach for the quantitative analysis of a generalized stochastic Petri net (GSPN) is to generate its entire state space and then solve the corresponding continuoustime Markov chain (CTMC) numerically. This analysis often suffers from two major problems: the state space explosion and the ..."
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Cited by 23 (4 self)
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A common approach for the quantitative analysis of a generalized stochastic Petri net (GSPN) is to generate its entire state space and then solve the corresponding continuoustime Markov chain (CTMC) numerically. This analysis often suffers from two major problems: the state space explosion and the stiffness of the CTMC. In this paper we present parallel algorithms for sharedmemory machines that attempt to alleviate both of these difficulties: the large main memory capacity of a multiprocessor can be utilized and long computation times are reduced by efficient parallelization. The algorithms comprise both CTMC construction and numerical steadystate solution. We give experimental results obtained with a Convex SPP16 sharedmemory multiprocessor that show the behavior of the algorithms and the parallel speedups obtained.
Parallel SharedMemory StateSpace Exploration in Stochastic Modeling
 Solving Irregularly Structured Problems in Parallel
, 1997
"... Stochastic modeling forms the basis for analysis in many areas, including biological and economic systems, as well as the performance and reliability modeling of computers and communication networks. One common approach is the statespacebased technique, which, starting from a highlevel mod ..."
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Cited by 10 (2 self)
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Stochastic modeling forms the basis for analysis in many areas, including biological and economic systems, as well as the performance and reliability modeling of computers and communication networks. One common approach is the statespacebased technique, which, starting from a highlevel model, uses depthfirst search to generate both a description of every possible state of the model and the dynamics of the transitions between them. However, these state spaces, besides being very irregular in structure, are subject to a combinatorial explosion, and can thus become extremely large. In the interest therefore of utilizing both the large memory capacity and the greater computational performance of modern multiprocessors, we are interested in implementing parallel algorithms for the generation and solution of these problems. In this paper we describe the techniques we use to generate the state space of a stochastic Petrinet model using sharedmemory multiprocessors. We describe some of the problems encountered and our solutions, in particular the use of modified Btrees as a data structure for the parallel search process. We present results obtained from experiments on two different sharedmemory machines. 1
Rewriting logic and probabilities
 Rewriting Techniques and Applications (RTA), volume 2706 of L.N.C.S
, 2003
"... Abstract Rewriting Logic has shown to provide a general and elegant framework for unifying a wide variety of models, including concurrency models and deduction systems. In order to extend the modeling capabilities of rule based languages, it is natural to consider that the firing of rules can be sub ..."
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Cited by 10 (2 self)
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Abstract Rewriting Logic has shown to provide a general and elegant framework for unifying a wide variety of models, including concurrency models and deduction systems. In order to extend the modeling capabilities of rule based languages, it is natural to consider that the firing of rules can be subject to some probabilistic laws. Considering rewrite rules subject to probabilities leads to numerous questions about the underlying notions and results. In this paper, we discuss whether there exists a notion of probabilistic rewrite system with an associated notion of probabilistic rewriting logic. 1
Parallel Graph Generation Algorithms for Shared and Distributed Memory Machines
, 1997
"... In this paper we give an overview and a comparison of two parallel algorithms for the state space generation in stochastic modeling on common classes of multiprocessors. In this context state space generation simply means constructing a graph, which usually gets extremely large. On shared memory mac ..."
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Cited by 8 (1 self)
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In this paper we give an overview and a comparison of two parallel algorithms for the state space generation in stochastic modeling on common classes of multiprocessors. In this context state space generation simply means constructing a graph, which usually gets extremely large. On shared memory machines, the key problem for a parallelization is the implementation of a shared data structure which enables efficient concurrent access for retrieving the nodes of the graph. In our realization this search structure is a Btree with special locking mechanisms assigned, leading to significant speedups. For distributed memory machines a dynamically adaptive partitioning strategy to distribute the state space onto different processors together with load balancing mechanisms is implemented. Thus sequentially not manageable problem sizes can be solved by combining the main memories of clustered workstations.
Dependability and Performance Assessment of Dynamic CONNECTed Systems
, 2011
"... Abstract. In this chapter we present approaches for analysis and monitoring of dependability and performance of Connected systems, and their combined usage. These approaches need to account for dynamicity and evolvability of Connected systems. In particular, the chapter covers the quantitative asses ..."
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Cited by 6 (3 self)
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Abstract. In this chapter we present approaches for analysis and monitoring of dependability and performance of Connected systems, and their combined usage. These approaches need to account for dynamicity and evolvability of Connected systems. In particular, the chapter covers the quantitative assessment of dependability and performance properties through a stochastic modelbased approach: first an overview of dependabilityrelated measurements and stochastic modelbased approaches provides the necessary background. Then, our proposal in Connect of an automated and modular dependability analysis framework for dynamically Connected systems is described. This framework can be used offline for system design (specifically, in Connect, for Connector synthesis), and online, to continuously assess system behaviour and detect possible issues arising at runtime. For the latter purpose, a generic, flexible and modular monitoring infrastructure has been developed. Monitoring is at the core of the Connect vision, in order to ensure runtime observation of specified quantitative properties and possibly trigger adequate reactions. We focus here on the interaction chain between monitoring and analysis, to allow for online continuous validation of specified dependability and performance properties. Illustrative examples of applications of analysis and monitoring are provided with reference to the Connect Terrorist Alert scenario. 1
Examining Coincident Failures and UsageProfiles in Reliability Analysis of an Embedded Vehicle
 SubSystem, 9th Int’l Conf. on Analytical and Stochastic Modeling Techniques [ASMT 2002
, 2002
"... Structured models of systems allow us to determine their reliability, yet there are numerous challenges that need to be overcome to obtain meaningful results. This paper reports the results and approach used to model and analyze the Antilock Braking System of a passenger vehicle using Stochastic Pe ..."
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Cited by 5 (4 self)
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Structured models of systems allow us to determine their reliability, yet there are numerous challenges that need to be overcome to obtain meaningful results. This paper reports the results and approach used to model and analyze the Antilock Braking System of a passenger vehicle using Stochastic Petri Nets. Special emphasis is laid on modeling extrafunctional characteristics like coincident failures among components, severity of failure and usageprofiles of the system. Components generally interact with each other during operation, and a faulty component can affect the probability of failure of other components. The severity of a failure also has an impact on the operation of the system, as does the usage profile failures which occur during active use of the system are the only failures considered (i.e., in reliability calculations).
Probabilistic Petri Nets and Mazurkiewicz Equivalence
, 2003
"... We propose a novel notion of probabilistic net systems. We define their semantics in terms of probabilistic languages. We extend the notion of Mazurkiewicz equivalence to probabilistic languages. We show a preliminary set of properties. ..."
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Cited by 5 (1 self)
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We propose a novel notion of probabilistic net systems. We define their semantics in terms of probabilistic languages. We extend the notion of Mazurkiewicz equivalence to probabilistic languages. We show a preliminary set of properties.
Parallel Approaches to the Numerical Transient Analysis of Stochastic Reward Nets
 ICATPN’99, Springer LNCS Vol.1639
, 1997
"... This paper presents parallel approaches to the complete transientnumerical analysis of stochastic reward nets (SRNs) for both shared and distributedmemory machines. Parallelization concepts and implementation issues are discussed for the three main analysis steps that are (1) generation of the ..."
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Cited by 4 (0 self)
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This paper presents parallel approaches to the complete transientnumerical analysis of stochastic reward nets (SRNs) for both shared and distributedmemory machines. Parallelization concepts and implementation issues are discussed for the three main analysis steps that are (1) generation of the underlying continuoustime Markovchain (CTMC), (2) solving the CTMC numerically for the desired time points and (3) converting the results back to the net level byevaluating reward based result measure functions. The distributedmemory approach implements dynamic load balancing mechanisms in step (1) to guarantee an equal distribution of the state space onto the main memories of the clustered machines. The sharedmemory algorithms are based on elaborated synchronization mechanisms whichallow parallel read and write access to the global irregular data structure of the CTMC. Performance measurements on different architectures and a comparison of the approaches are given. All the algorithms are integrated in PPANDA which consequently is a parallel SRN modeling tool suitable for differentmultiprocessor platforms.
Fluid computation of the performance–energy tradeoff in large scale Markov models
"... Recent fluid analysis techniques allow fast and efficient calculation of complex reward metrics and passage time probabilities in systems with very large state space. We demonstrate how to incorporate these to look at the tradeoff between service level agreement (SLA) satisfaction and complex rewar ..."
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Cited by 3 (3 self)
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Recent fluid analysis techniques allow fast and efficient calculation of complex reward metrics and passage time probabilities in systems with very large state space. We demonstrate how to incorporate these to look at the tradeoff between service level agreement (SLA) satisfaction and complex reward optimisation. We show how the fluid analysis naturally leads to a constrained global optimisation problem with embedded differential equations. We illustrate this problem on an abstract model of a virtualised execution environment that accurately captures resource allocations.
Abstract interpretation for worst and average case analysis
 IN: PROGRAM ANALYSIS AND COMPILATION, THEORY AND PRACTICE. VOLUME 4444 OF LNCS
, 2007
"... We will review Wilhelm’s work on WCET for hard realtime applications and also recent work on analysis of softreal time systems Interpretation (PAI) as a quantitative variation of the classical approach; PAI aims to provide close approximations – this should be contrasted to the safe approximation ..."
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Cited by 3 (2 self)
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We will review Wilhelm’s work on WCET for hard realtime applications and also recent work on analysis of softreal time systems Interpretation (PAI) as a quantitative variation of the classical approach; PAI aims to provide close approximations – this should be contrasted to the safe approximations studied in the standard setting. We will discuss the relation between PAI and classical Abstract Interpretation as well as average case analysis.