The GreatSPN tool

Baarir, S.; Beccuti, Marco; Cerotti, Davide; Pierro, Massimiliano De; Donatelli, Susanna; Franceschinis, Giuliana

doi:10.1145/1530873.1530876

Cited by 109 publications

(10 citation statements)

References 16 publications

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“…Subnet composition through superposition of shared places is automated by the ALGE-BRA module of GREATSPN introduced by Baarir et al (2009). The initial marking of M corresponds to the initial marking of its components, defined, as for the fixed parts of the model, in previous sections; all start places of adaptation procedures hold a neutral token.…”

Section: Module Compositionmentioning

confidence: 99%

Formal specification and verification of decentralized self-adaptive systems using symmetric nets

Camilli

Capra

2021

Discrete Event Dyn Syst

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Engineering distributed self-adaptive systems is challenging due to multiple interacting components, some of which monitor and possibly modify the behavior of managed components that operate in highly dynamic settings. Formalizing such systems having a decentralized adaptation control has been recognized as a hard task. In this article, we introduce a formal framework based on Symmetric Nets (a well-established subclass of Colored Petri nets) for modeling and analyzing distributed self-adaptive discrete-event systems. Even though Petri Nets represent a sound and expressive formal model of concurrency and distribution, they cannot specify in a natural way structural changes enacted by adaptation procedures. We overcome this limitation by means of a two-layer modeling approach that enables clear separation of concerns and allows multiple decentralized adaptation procedures to be specified, validated, and verified against formal requirements. Validation and verification techniques are supported by powerful off-the-shelf tools tailored to Symmetric Nets. A self-healing manufacturing system case study is used to show applicability, advantages, and shortcomings of the approach. In particular, complexity issues are thoroughly discussed and mitigated by adopting complementary approaches based on interleaving reduction and behavioral symmetries exploitation.

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Section: Module Compositionmentioning

confidence: 99%

Formal specification and verification of decentralized self-adaptive systems using symmetric nets

Camilli

Capra

2021

Discrete Event Dyn Syst

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“…Efficient methods have been proposed to perform SN state-space based analysis [7], or structural analysis [4], [3]. Many of these algorithms have been implemented in GreatSPN [1], whereas the most recent developments on structural analysis have been implemented in SNexpression (www.di.unito.it/~depierro/SNex).…”

Section: Symmetric Netsmentioning

confidence: 99%

“…In this paper we consider the relationship between GTS and PN from a new perspective: we provide a formalization of Graph Transformation Systems (GTS) based on Symmetric Nets (SN) 1 [6], a type of Coloured Petri nets [12], [11] featuring a particular syntax that outlines model symmetries and is exploited both in state-space based and structural analysis. The idea is simple: each rule (derivation) of a GTS corresponds to a SN transition which is properly connected to a couple of SN places whose marking encodes a graph.…”

Section: Introductionmentioning

confidence: 99%

An Operational Semantics of Graph Transformation Systems Using Symmetric Nets

Capra

2019

Electron. Proc. Theor. Comput. Sci.

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Graph transformation systems (GTS) have been successfully proposed as a general, theoretically sound model for concurrency. Petri nets (PN), on the other side, are a central and intuitive formalism for concurrent or distributed systems, well supported by a number of analysis techniques/tools. Some PN classes have been shown to be instances of GTS. In this paper, we change perspective presenting an operational semantics of GTS in terms of Symmetric Nets, a well-known class of Coloured Petri nets featuring a structured syntax that outlines model symmetries. Some practical exploitations of the proposed operational semantics are discussed. In particular, a recently developed structural calculus for SN is used to validate graph rewriting rules in a symbolic way.An Operational Semantics of GTS the GreatSPN package [1]; an operational interleaving semantics for GTS is provided in a natural way building the state-transition system of a SN; a compact state-transition system -called symbolic reachability graph [7], in which states (markings) representing isomorphic graphs are folded, can be directly derived once an initial symbolic graph encoding is set; some recent advances in SN (symbolic) structural analysis [4], [3], implemented in the SNExpression tool (www.di.unito.it/~depierro/SNex) may be exploited to check some conditions ensuring rule well-definiteness, validate rules, and verify their potential concurrency; in particular, a fully automated calculus of symbolic structural relations in SN models may be profitably used. All these concepts are instantiated on a few, though significant, examples of graph rewriting rules, and a simple GTS. All the examples used in the paper are available in GreatSPN format at https://github.com/lgcapra/GTS-SN.The GTS formalization based on SN may be considered as an alternative to classical approaches, in particular the algebraic ones based on single and double pushout. The strengths of this new proposal are a more intuitive definition of derivations, and the availability of well established tools for the editing/validation/analysis of models. The relationship between SN rules and single/double pushout derivations, however, is not treated in this paper, and deserves further investigations.The balance of the paper is as follows: Section 2 introduces SN and related background notions; Section 3 presents the encoding of a GTS as a SN, and its operational semantics; symbolic structural conditions for rule well-definiteness are also set up; Section 4 shows an application of SN structural calculus for verifying rule concurrency in a GTS; finally, Section 5 contains the conclusion and describes ongoing work

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“…This model has been proposed to evaluate the execution time of a MR job via simulation. The GreatSPN 2.0 [38] tool was used to evaluate the SWN model with the same values of accuracy and confidence interval as the SAN model. The results obtained from the experiments, proposed SAN model, and the SWN model in [13] are shown in Table 3 for MR applications.…”

Section: Composite Dag Modelmentioning

confidence: 99%

Analytical composite performance models for Big Data applications

Karimian-Aliabadi

Ardagna

Entezari‐Maleki

et al. 2019

Journal of Network and Computer Applications

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In the era of Big Data, whose digital industry is facing the massive growth of data size and development of data intensive software, more and more companies are moving to use new frameworks and paradigms capable of handling data at scale. The outstanding MapReduce (MR) paradigm and its implementation framework, Hadoop are among the most referred ones, and basis for later and more advanced frameworks like Tez and Spark. Accurate prediction of the execution time of a Big Data application helps improving design time decisions, reduces over allocation charges, and assists budget management. In this regard, we propose analytical models based on the Stochastic Activity Networks (SANs) to accurately model the execution of MR, Tez and Spark applications in Hadoop environments governed by the YARN Capacity scheduler. We evaluate the accuracy of the proposed models over the TPC-DS industry benchmark across different configurations. Results obtained by numerically solving analytical SAN models show an average error of 6% in estimating the execution time of an application compared to the data gathered from experiments and moreover the model evaluation time is lower than simulation time of state of the art solutions.

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The GreatSPN tool

Cited by 109 publications

References 16 publications

Formal specification and verification of decentralized self-adaptive systems using symmetric nets

Formal specification and verification of decentralized self-adaptive systems using symmetric nets

An Operational Semantics of Graph Transformation Systems Using Symmetric Nets

Analytical composite performance models for Big Data applications

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