COWS: A Timed Service-Oriented Calculus

COWS (Calculus for Orchestration of Web Services) is a foundational language for Service Oriented Computing that combines in an original way a number of ingredients borrowed from well-known process calculi, e.g. asynchronous communication, polyadic synchronization, pattern matching, protection, delimited receiving and killing activities, while resulting different from any of them. In this paper, we extend COWS with timed orchestration constructs, this way we obtain a language capable of completely formalizing the semantics of WS-BPEL, the ‘de facto’ standard language for orchestration of web services. We present the semantics of the extended language and illustrate its peculiarities and expressiveness by means of several examples

A WSDL-Based Type System for WS-BPEL

We tackle the problem of providing rigorous formal foundations to current software engineering technologies for web services. We focus on two of the most used XML-based languages for web services: WSDL and WS-BPEL. To this aim, first we select an expressive subset of WS-BPEL, with special concern for modeling the interactions among web service instances in a network context, and define its operational semantics. We call ws-calculus the resulting formalism. Then, we put forward a rigorous typing discipline that formalizes the relationship existing between ws-calculus terms and the associated WSDL documents and supports verification of their compliance. We prove that the type system and the operational semantics of ws-calculus are ‘sound’ and apply our approach to an example application involving three interacting web services

Specifying and analysing reputation systems with coordination languages

Reputation systems are nowadays widely used to support decision making in networked systems. Parties in such systems rate each other and use shared ratings to compute reputation scores that drive their interactions. The existence of reputation systems with remarkable differences calls for formal approaches to their analysis. We present a verification methodology for reputation systems that is based on the use of the coordination language Klaim and related analysis tools. First, we define a parametric Klaim specification of a reputation system that can be instantiated with different reputation models. Then, we consider stochastic specification obtained by considering actions with random (exponentially distributed) duration. The resulting specification enables quantitative analysis of properties of the considered system. Feasibility and effectiveness of our proposal is demonstrated by reporting on the analysis of two reputation models

A formalized framework for mobile cloud computing

Mobile Cloud Computing (MCC) is an emerging paradigm to transparently provide support for demanding tasks on resource-constrained mobile devices by relying on the integration with remote cloud services. Research in this field is tackling the multiple conceptual and technical challenges (e.g., how and when to offload) that are hindering the full realization of MCC. The Networked Autonomic Machine (NAM) framework is a tool that supports and facilitates the design networks of hardware and software autonomic entities, providing or consuming services or resources. Such a framework can be applied, in particular, to MCC scenarios. In this paper, we focus on NAM’s features related to the key aspects of MCC, in particular those concerning code mobility capabilities and autonomic offloading strategies. Our first contribution is the definition of a set of high-level actions supporting MCC. The second contribution is the proposal of a formal semantics for those actions, which provides the core NAM features with a precise formal characterization. Thus, the third contribution is the further development of the NAM conceptual framework and, in particular, the partial re-engineering of the related Java middleware. We show the effectiveness of the revised middleware by discussing the implementation of a Global Ambient Intelligence case study

Focus of Attention Improves Information Transfer in Visual Features

Unsupervised learning from continuous visual streams is a challenging problem that cannot be naturally and efficiently managed in the classic batch-mode setting of computation. The information stream must be carefully processed accordingly to an appropriate spatio-temporal distribution of the visual data, while most approaches of learning commonly assume uniform probability density. In this paper we focus on unsupervised learning for transferring visual information in a truly online setting by using a computational model that is inspired to the principle of least action in physics. The maximization of the mutual information is carried out by a temporal process which yields online estimation of the entropy terms. The model, which is based on second-order differential equations, maximizes the information transfer from the input to a discrete space of symbols related to the visual features of the input, whose computation is supported by hidden neurons. In order to better structure the input probability distribution, we use a human-like focus of attention model that, coherently with the information maximization model, is also based on second-order differential equations. We provide experimental results to support the theory by showing that the spatio-temporal filtering induced by the focus of attention allows the system to globally transfer more information from the input stream over the focused areas and, in some contexts, over the whole frames with respect to the unfiltered case that yields uniform probability distributions

Reputation-based Cooperation in the Clouds

The popularity of the cloud computing paradigm is opening new opportunities for collaborative computing. In this paper we tackle a fundamental problem in open-ended cloud-based distributed comput- ing platforms, i.e., the quest for potential collaborators. We assume that cloud participants are willing to share their computational resources for shared distributed computing problems, but they are not willing to dis- closure the details of their resources. Lacking such information, we advo- cate to rely on reputation scores obtained by evaluating the interactions among participants. More specifically, we propose a methodology to as- sess, at design time, the impact of different (reputation-based) collabo- rator selection strategies on the system performance. The evaluation is performed through statistical analysis on a volunteer cloud simulator

A model checking approach for verifying COWS specifications

We introduce a logical verification framework for checking functional properties of service-oriented applications formally specified using the service specification language COWS. The properties are described by means of SocL, a logic specifically designed to capture peculiar aspects of services. Service behaviours are abstracted in terms of Doubly Labelled Transition Systems, which are used as the interpretation domain for SocL formulae. We also illustrate the SocL model checker at work on a bank service scenario specified in COWS

A Flexible Approach to Multi-party Business Process Execution on Blockchain

In modern business scenarios, more and more organisations have to deal with the critical requirements of trustworthiness and flexibility, when collaborating in multi-party business processes. This calls for new kinds of systems able to manage collaborative processes in untrusted and dynamic environments. Concerning the collaborative perspective, the Business Process Management discipline has provided effective and standardised solutions for a long time, now. Regarding the trustworthiness perspective, blockchain is advocated as one of the most prominent technologies to guarantee trust in a multi-party setting. However, while the immutability of blockchain provides transparent and secure proof of past business interactions, it hinders the flexibility of the business process execution, as the business logic regulating the process execution is immutably stored in the blockchain. On the other hand, flexibility is a property that is becoming crucial in such a setting due to the high dynamism of the business scenarios. In fact, it permits to modify a process at run-time to deal with internal or external changes. In this paper, we face this issue by proposing an architecture for the flexible blockchain-based execution of multi-party business processes. In our approach, business processes are modelled by BPMN choreography diagrams translated into code, whose execution state is then stored in the blockchain. Flexibility is achieved by decoupling the business process’s logic from its execution state, thus allowing run-time changes to the process execution without losing the fundamental properties of trust provided by the blockchain. To show the effectiveness of our approach, we provide a prototypical implementation, called FlexChain, and we use it on a case study from the healthcare application domain. The results obtained by the analysis of cost for the reported case study show the feasibility of the approach. In particular, major costs to sustain relate to one-time operations, such as the deployment and the run-time update of the model, while the most frequent actions are quite efficient

Local Propagation in Constraint-based Neural Network

In this paper we study a constraint-based representation of neural network architectures. We cast the learning problem in the Lagrangian framework and we investigate a simple optimization procedure that is well suited to fulfil the so-called architectural constraints, learning from the available supervisions. The computational structure of the proposed Local Propagation (LP) algorithm is based on the search for saddle points in the adjoint space composed of weights, neural outputs, and Lagrange multipliers. All the updates of the model variables are locally performed, so that LP is fully parallelizable over the neural units, circumventing the classic problem of gradient vanishing in deep networks. The implementation of popular neural models is described in the context of LP, together with those conditions that trace a natural connection with Backpropagation. We also investigate the setting in which we tolerate bounded violations of the architectural constraints, and we provide experimental evidence that LP is a feasible approach to train shallow and deep networks, opening the road to further investigations on more complex architectures, easily describable by constraints

Neural Time-Reversed Generalized Riccati Equation

Optimal control deals with optimization problems in which variables steer a dynamical system, and its outcome contributes to the objective function. Two classical approaches to solving these problems are Dynamic Programming and the Pontryagin Maximum Principle. In both approaches, Hamiltonian equations offer an interpretation of optimality through auxiliary variables known as costates. However, Hamiltonian equations are rarely used due to their reliance on forward-backward algorithms across the entire temporal domain. This paper introduces a novel neural-based approach to optimal control, with the aim of working forward-in-time. Neural networks are employed not only for implementing state dynamics but also for estimating costate variables. The parameters of the latter network are determined at each time step using a newly introduced local policy referred to as the time-reversed generalized Riccati equation. This policy is inspired by a result discussed in the Linear Quadratic (LQ) problem, which we conjecture stabilizes state dynamics. We support this conjecture by discussing experimental results from a range of optimal control case studies