MAURO , Jacopo

In previous works we have proved that component reconfig-uration in the presence of conflicts among components is non-primitive recursive, while it becomes poly-time if there are no conflicts and under the assumption that there are no components in the initial configuration. The case with non-empty initial configurations was left as an open...

The Aeolus component problem of automatic deployment of complex distributed component systems. In the general setting, the task of checking if a distributed application can be deployed is an undecidable problem. However, the current undecidability proof in Aeolus assumes the possibility to perform in a synchronized way atomic configuration...

In previous works we have proved that component reconfiguration in the presence of conflicts among components is non-primitive recursive, while it becomes poly-time if there are no conflicts and under the assumption that there are no components in the initial configuration. The case with non-empty initial configurations was left as an open...

In the context of Constraint Programming, a portfolio approach exploits the complementary strengths of a portfolio of different constraint solvers. The goal is to predict and run the best solver(s) of the portfolio for solving a new, unseen problem. In this work we reproduce, simulate, and evaluate the performance of different portfolio...

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...

The Constraint Programming (CP) paradigm allows to model and solve Constraint Satisfaction / Optimization Problems (CSPs / COPs). A CP Portfolio Solver is a particular constraint solver that takes advantage of a portfolio of different CP solvers in order to solve a given problem by properly exploiting Algorithm Selection techniques. In this...

In Constraint Programming (CP) a portfolio solver combines a variety of different constraint solvers for solving a given problem. This fairly recent approach enables to significantly boost the performance of single solvers, especially when multicore architectures are exploited. In this work we give a brief overview of the portfolio solver...

The Resource-Constrained Project Scheduling Problem (RCPSP) is a well-known scheduling problem aimed at minimizing the makespan of a project subject to temporal and resource constraints. Constraint Programming allows to model and solve RCPSPs in a natural and efficient way, especially when Lazy Clause Generation (LCG) techniques are employed....

In Constraint Programming (CP) a portfolio solver combines a variety of different constraint solvers for solving a given problem. This fairly recent approach enables to significantly boost the performance of single solvers, especially when multicore architectures are exploited. In this work we give a brief overview of the portfolio solver...

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...

The SUNNY algorithm is a portfolio technique originally tailored for Constraint Satisfaction Problems (CSPs). SUNNY allows to select a set of solvers to be run on a given CSP, and was proven to be effective in the MiniZinc Challenge, i.e., the yearly international competition for CP solvers. In 2015, SUNNY was compared with other solver...

Within the Constraint Satisfaction Problems (CSP) context, a methodology that has proven to be particularly performant consists of using a portfolio of different constraint solvers. Nevertheless, comparatively few studies and investigations have been done in the world of Constraint Optimization Problems (COP). In this work, we provide a...

In distributed systems like those based on cloud or service-oriented frameworks, applications are typically assembled by deploying and connecting a large number of heterogeneous software components, spanning from fine-grained packages to coarse-grained complex services. Automation techniques and tools have been proposed to ease the deployment...

We investigate the problem of modeling the optimal and automatic deployment of cloud applications. We follow an approach based on three main pillars: (i) the specification of the computing resources needed by software components and those provided by the executing environment (e.g. virtual machines or containers), (ii) the declarative...

This tutorial describes AIOCJ, which stands for Adaptive Interaction Oriented Choreographies in Jolie, a choreographic language for programming microservice-based applications which can be updated at runtime. The compilation of a single AIOCJ program generates the whole set of distributed microservices that compose the application. Adaptation...

In Choreographic Programming, a choreography specifies in a single artefact the expected behaviour of all the participants in a distributed system. The choreography is used to synthesise correct-by-construction programs for each participant. In previous work, we defined Dynamic Choreographies to support the update of distributed systems at...

Traditional development methodologies that separate software design from application deployment have been replaced by approaches such as continuous delivery or DevOps, according to which deployment issues should be taken into account already at the early stages of development. This calls for the definition of new modeling and specification...

DevOps advocates the usage of Virtualization Technologies (VT), such as Virtual Machines and Containers. However, it is complex to predict how the usage of a given VT will impact on the performance of an application. In this paper, we present a collection of reference benchmarks that developers can use to orient when looking for the...

Serverless computing is a Cloud development paradigm where developers write and compose stateless functions, abstracting from their deployment and scaling. In this paper, we address the problem of functionexecution scheduling, i.e., how to schedule the execution of Serverless functions to optimise their performance against some user-defined...

Humans are social animals and usually organize activities in groups. However, they are often willing to split temporarily a bigger group in subgroups to enhance their preferences. In this work we present NightSplitter, an on-line tool that is able to plan movie and dinner activities for a group of users, possibly splitting them in subgroups to...

Following previous work on the automated deployment of componentbased applications, we present a formal model specifically tailored for reasoning on the deployment of microservice architectures. The first result that we present is a formal proof of decidability of the problem of synthesizing optimal deployment plans for microservice...