Jacopo Mauro

AIOCJ: A Choreographic Framework for Safe Adaptive Distributed Applications

We present AIOCJ, a framework for programming distributed adaptive applications. Applications are programmed using AIOC, a choreographic language suited for expressing patterns of interaction from a global point of view. AIOC allows the programmer to specify which parts of the application can be adapted. Adaptation takes place at runtime by means of rules, which can change during the execution to tackle possibly unforeseen adaptation needs. AIOCJ relies on a solid theory that ensures applications to be deadlock-free by construction also after adaptation. We describe the architecture of AIOCJ, the design of the AIOC language, and an empirical validation of the framework.

Aeolus: A component model for the cloud

Abstract We introduce the Aeolus component model, which is specifically designed to capture realistic scenarios arising when configuring and deploying distributed applications in the so-called cloud environments, where interconnected components can be deployed on clusters of heterogeneous virtual machines, which can be in turn created, destroyed, and connected on-the-fly. The full Aeolus model is able to describe several component characteristics such as dependencies, conflicts, non-functional requirements (replication requests and load limits), as well as the fact that component interfaces to the world might vary depending on the internal component state. When the number of components needed to build an application grows, it becomes important to be able to automate activities such as deployment and reconfiguration. This corresponds, at the level of the model, to the ability to decide whether a desired target system configuration is reachable, which we call the achievability problem, and producing a path to reach it. In this work we show that the achievability problem is undecidable for the full Aeolus model, a strong limiting result for automated configuration in the cloud. We also show that the problem becomes decidable, but Ackermann-hard, as soon as one drops non-functional requirements. Finally, we provide a polynomial time algorithm for the further restriction of the model where support for inter-component conflicts is also removed.

SUNNY: a Lazy Portfolio Approach for Constraint Solving

Within the context of constraint solving, a portfolio approach allows one to exploit the synergy between different solvers in order to create a globally better solver. In this paper we present SUNNY: a simple and flexible algorithm that takes advantage of a portfolio of constraint solvers in order to compute — without learning an explicit model — a schedule of them for solving a given Constraint Satisfaction Problem (CSP). Motivated by the performance reached by SUNNY vs. different simulations of other state of the art approaches, we developed sunny-csp , an effective portfolio solver that exploits the underlying SUNNY algorithm in order to solve a given CSP. Empirical tests conducted on exhaustive benchmarks of MiniZinc models show that the actual performance of sunny-csp conforms to the predictions. This is encouraging both for improving the power of CSP portfolio solvers and for trying to export them to fields such as Answer Set Programming and Constraint Logic Programming.

An Empirical Evaluation of Portfolios Approaches for Solving CSPs

Recent research in areas such as SAT solving and Integer Linear Programming has shown that the performances of a single arbitrarily efficient solver can be significantly outperformed by a portfolio of possibly slower on-average solvers. We report an empirical evaluation and comparison of portfolio approaches applied to Constraint Satisfaction Problems (CSPs). We compared models developed on top of off-the-shelf machine learning algorithms with respect to approaches used in the SAT field and adapted for CSPs, considering different portfolio sizes and using as evaluation metrics the number of solved problems and the time taken to solve them. Results indicate that the best SAT approaches have top performances also in the CSP field and are slightly more competitive than simple models built on top of classification algorithms.

Dynamic Choreographies Safe Runtime Updates of Distributed Applications

Programming distributed applications free from communication deadlocks and races is complex. Preserving these properties when applications are updated at runtime is even harder. We present DIOC, a language for programming distributed applications that are free from deadlocks and races by construction. A DIOC program describes a whole distributed application as a unique entity (choreography). DIOC allows the programmer to specify which parts of the application can be updated. At runtime, these parts may be replaced by new DIOC fragments from outside the application. DIOC programs are compiled, generating code for each site, in a lower-level language called DPOC. We formalise both DIOC and DPOC semantics as labelled transition systems and prove the correctness of the compilation as a trace equivalence result. As corollaries, DPOC applications are free from communication deadlocks and races, even in presence of runtime updates.

A Planning Tool Supporting the Deployment of Cloud Applications

Cloud computing offers the possibility to build sophisticated software systems on virtualized infrastructures at a fraction of the cost necessary just a few years ago. Nevertheless, the deployment of such complex systems is a serious issue due to the large number of involved software packages and services, and to their elaborated interdependencies. In this paper we address the challenge of automatizing this complex deployment process. We first formalize it as a planning problem and observe that standard planning tools can effectively solve it only on small and trivial instances. For this reason, we propose an ad hoc planning technique which we validate by means of a prototype implementation able to effectively solve this deployment problem also on instances of realistic size.

An enhanced features extractor for a portfolio of constraint solvers

Recent research has shown that a single arbitrarily efficient solver can be significantly outperformed by a portfolio of possibly slower on-average solvers. The solver selection is usually done by means of (un)supervised learning techniques which exploit features extracted from the problem specification. In this paper we present an useful and flexible framework that is able to extract an extensive set of features from a Constraint (Satisfaction/Optimization) Problem defined in possibly different modeling languages: MiniZinc, FlatZinc or XCSP.

Towards Global and Local Types for Adaptation

Choreographies allow designers to specify the protocols followed by participants of a distributed interaction. In this context, adaptation may be necessary to respond to external requests or to better suit a changing environment (a self-update). Adapting the behavior of a participant requires to update in a coordinated way possibly all the participants interacting with him. We propose a language able to describe a choreography together with its adaptation strategies, and we discuss the main issues that have to be solved to enable adaptation on a participant code dealing with many interleaved protocols.

Aeolus: Mastering the Complexity of Cloud Application Deployment

Cloud computing offers the possibility to build sophisticated software systems on virtualized infrastructures at a fraction of the cost necessary just few years ago, but deploying/maintaining/reconfiguring such software systems is a serious challenge. The main objective of the Aeolus project, an initiative funded by ANR (the French “Agence Nationale de la Recherche”), is to tackle the scientific problems that need to be solved in order to ease the problem of efficient and cost-effective deployment and administration of the complex distributed architectures which are at the heart of cloud applications.

SUNNY-CP: a sequential CP portfolio solver

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 work we present sunny-cp: a CP portfolio for solving both CSPs and COPs that turned out to be competitive also in the MiniZinc Challenge, the reference competition for CP solvers.