Author name cluster

Roberto Amadini

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12 papers

2 author rows

LOPSTR Conference 2024 Conference Paper

Pick a Flavour: Towards Sustainable Deployment of Cloud-Edge Applications

Roberto Amadini
Simone Gazza
Jacopo Soldani
Monica Vitali
Antonio Brogi
Stefano Forti 0002
Saverio Giallorenzo
Pierluigi Plebani

Abstract Multi-component Cloud-Edge applications, which rely on the Internet of Things, call for suitable and sustainable management techniques. On one hand, they have to guarantee their hardware and Quality of Service requirements over large-scale Cloud-Edge infrastructures. On the other hand, there is an urge to reduce carbon emissions related to the software life cycle at all phases. To this end, this work-in-progress article introduces a novel constraint optimisation approach to adapt multi-flavoured applications and their placement, while determining optimal trade-offs between Quality of Service, operational costs, and carbon emissions. To showcase the practical feasibility of the proposed approach, we provide an open-source implementation and run it over a lifelike example.

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IJCAI Conference 2023 Conference Paper

A Regular Matching Constraint for String Variables

Roberto Amadini
Peter J. Stuckey

Using a regular language as a pattern for string matching is nowadays a common -and sometimes unsafe- operation, provided as a built-in feature by most programming languages. A proper constraint solver over string variables should support most of the operations over regular expressions and related constructs. However, state-of-the-art string solvers natively support only the membership relation of a string variable to a regular language. Here we take a step forward by defining a specialised propagator for the match operation, returning the leftmost position where a pattern can match a given string. Empirical evidences show the effectiveness of our approach, implemented within the constraint programming framework, and tested against state-of-the-art string solvers.

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JAIR Journal 2023 Journal Article

On the Evaluation of (Meta-)solver Approaches

Roberto Amadini
Maurizio Gabbrielli
Tong Liu
Jacopo Mauro

Meta-solver approaches exploit many individual solvers to potentially build a better solver. To assess the performance of meta-solvers, one can adopt the metrics typically used for individual solvers (e.g., runtime or solution quality) or employ more specific evaluation metrics (e.g., by measuring how close the meta-solver gets to its virtual best performance). In this paper, based on some recently published works, we provide an overview of different performance metrics for evaluating (meta-)solvers by exposing their strengths and weaknesses.

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IJCAI Conference 2022 Conference Paper

sunny-as2: Enhancing SUNNY for Algorithm Selection (Extended Abstract)

Tong Liu
Roberto Amadini
Maurizio Gabbrielli
Jacopo Mauro

SUNNY is a k-nearest neighbors based Algorithm Selection (AS) approach that schedules and runs a number of solvers for a given unforeseen problem. In this work we present sunny-as2, an enhancement of SUNNY for generic AS scenarios that advances the original approach with wrapper-based feature selection, neighborhood-size configuration and a greedy approach to speed-up the training phase. Empirical evidence shows that sunny-as2 is competitive w. r. t. state-of-the-art AS approaches.

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JAIR Journal 2021 Journal Article

sunny-as2: Enhancing SUNNY for Algorithm Selection

Tong Liu
Roberto Amadini
Maurizio Gabbrielli
Jacopo Mauro

SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY is based on the k-nearest neighbors algorithm and enables one to schedule, from a portfolio of solvers, a subset of solvers to be run on a given CP problem. This approach has proved to be effective for CP problems. In 2015, the ASlib benchmarks were released for comparing AS systems coming from disparate fields (e.g., ASP, QBF, and SAT) and SUNNY was extended to deal with generic AS problems. This led to the development of sunny-as, a prototypical algorithm selector based on SUNNY for ASlib scenarios. A major improvement of sunny-as, called sunny-as2, was then submitted to the Open Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the best approach for the runtime minimization of decision problems. In this work we present the technical advancements of sunny-as2, by detailing through several empirical evaluations and by providing new insights. Its current version, built on the top of the preliminary version submitted to OASC, is able to outperform sunny-as and other state-of-the-art AS methods, including those who did not attend the challenge.

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LOPSTR Conference 2020 Conference Paper

Algorithm Selection for Dynamic Symbolic Execution: A Preliminary Study

Roberto Amadini
Graeme Gange
Peter Schachte
Harald Søndergaard
Peter J. Stuckey

Abstract Given a portfolio of algorithms, the goal of Algorithm Selection ( AS ) is to select the best algorithm(s) for a new, unseen problem instance. Dynamic Symbolic Execution ( DSE ) brings together concrete and symbolic execution to maximise the program coverage. DSE uses a constraint solver to solve the path conditions and generate new inputs to explore. In this paper we join these lines of research by introducing a model that combines DSE and AS approaches. The proposed AS/DSE model is a generic and flexible framework enabling the DSE engine to solve the path conditions it collects with a portfolio of different solvers, by exploiting and extending the well-known AS techniques that have been developed over the last decade. In this way, one can increase the coverage and sometimes even outperform the aggregate coverage achievable by running simultaneously all the solvers of the portfolio.

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AIJ Journal 2020 Journal Article

Dashed strings for string constraint solving

Roberto Amadini
Graeme Gange
Peter J. Stuckey

String processing is ubiquitous across computer science, and arguably more so in web programming — where it is also a critical part of security issues such as injection attacks. In recent years, a number of string solvers have been developed to solve combinatorial problems involving string variables and constraints. We examine the dashed string approach to string constraint solving, which represents an unknown string as a sequence of blocks of characters with bounds on their cardinalities. The solving approach relies on propagation of information about the blocks of characters that arise from reasoning about the constraints in which they occur. This approach shows promising performance on many benchmarks involving constraints like string length, equality, concatenation, and regular expression membership. In this paper, we formally review the definition, the properties and the use of dashed strings for string constraint solving, and we provide an empirical validation that confirms the effectiveness of this approach.

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ECAI Conference 2020 Conference Paper

String Constraint Solving: Past, Present and Future

Roberto Amadini
Graeme Gange
Peter Schachte
Harald Søndergaard
Peter J. Stuckey

String constraint solving is an important emerging field, given the ubiquity of strings over different fields such as formal analysis, automated testing, database query processing, and cybersecurity. This paper highlights the current state-of-the-art for string constraint solving, and identifies future challenges in this field.

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AAAI Conference 2018 Conference Paper

Sweep-Based Propagation for String Constraint Solving

Roberto Amadini
Graeme Gange
Peter Stuckey

Solving constraints over strings is an emerging important ﬁeld. Recently, a Constraint Programming approach based on dashed strings has been proposed to enable a compact domain representation for potentially large bounded-length string variables. In this paper, we present a more efﬁcient algorithm for propagating equality (and related constraints) over dashed strings. We call this propagation sweep-based. Experimental evidences show that sweep-based propagation is able to signiﬁcantly outperform state-of-the-art approaches for string constraint solving.

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LOPSTR Conference 2016 Conference Paper

MiniZinc with Strings

Roberto Amadini
Pierre Flener
Justin Pearson
Joseph D. Scott
Peter J. Stuckey
Guido Tack

Abstract Strings are extensively used in modern programming languages and constraints over strings of unknown length occur in a wide range of real-world applications such as software analysis and verification, testing, model checking, and web security. Nevertheless, practically no constraint programming solver natively supports string constraints. We introduce string variables and a suitable set of string constraints as builtin features of the MiniZinc modelling language. Furthermore, we define an interpreter for converting a MiniZinc model with strings into a FlatZinc instance relying only on integer variables. This conversion is obtained via rewrite rules, and does not require any extension of the existing FlatZinc specification. This provides a user-friendly interface for modelling combinatorial problems with strings, and enables both string and non-string solvers to actually solve such problems.

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IJCAI Conference 2015 Conference Paper

A Multicore Tool for Constraint Solving

Roberto Amadini
Maurizio Gabbrielli
Jacopo Mauro

In Constraint Programming (CP), a portfolio solver uses a variety of different solvers for solving a given Constraint Satisfaction / Optimization Problem. In this paper we introduce sunny-cp2: the first parallel CP portfolio solver that enables a dynamic, cooperative, and simultaneous execution of its solvers in a multicore setting. It incorporates state-of-the-art solvers, providing also a usable and configurable framework. Empirical results are very promising. sunny-cp2 can even outperform the performance of the oracle solver which always selects the best solver of the portfolio for a given problem.

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LOPSTR Conference 2015 Conference Paper

Why CP Portfolio Solvers Are (under)Utilized? Issues and Challenges

Roberto Amadini
Maurizio Gabbrielli
Jacopo Mauro

Abstract It is well recognized that a single, arbitrarily efficient solver can be significantly outperformed by a portfolio solver exploiting a combination of possibly slower on-average different solvers. Despite the success of portfolio solvers within the context of solving competitions, they are rarely used in practice. In this paper we give an overview of the main limitations that hinder the practical adoption and development of portfolio solvers within the Constraint Programming (CP) paradigm, discussing also possible ways to overcome them and potential extensions outside the CP field.

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