Distributed System Contract Monitoring

The use of behavioural contracts, to specify, regulate and verify systems, is particularly relevant to runtime monitoring of distributed systems. System distribution poses major challenges to contract monitoring, from monitoring-induced information leaks to computation load balancing, communication overheads and fault-tolerance. We present mDPi, a location-aware process calculus, for reasoning about monitoring of distributed systems. We define a family of Labelled Transition Systems for this calculus, which allow formal reasoning about different monitoring strategies at different levels of abstractions. We also illustrate the expressivity of the calculus by showing how contracts in a simple contract language can be synthesised into different mDPi monitors.Comment: In Proceedings FLACOS 2011, arXiv:1109.239

A Calculus for Orchestration of Web Services

Service-oriented computing, an emerging paradigm for distributed computing based on the use of services, is calling for the development of tools and techniques to build safe and trustworthy systems, and to analyse their behaviour. Therefore, many researchers have proposed to use process calculi, a cornerstone of current foundational research on specification and analysis of concurrent, reactive, and distributed systems. In this paper, we follow this approach and introduce CWS, a process calculus expressly designed for specifying and combining service-oriented applications, while modelling their dynamic behaviour. We show that CWS can model all the phases of the life cycle of service-oriented applications, such as publication, discovery, negotiation, orchestration, deployment, reconfiguration and execution. We illustrate the specification style that CWS supports by means of a large case study from the automotive domain and a number of more specific examples drawn from it

Static Analysis of Functional Programs

In this paper, the static analysis of programs in the functional programming language Miranda* is described based on two graph models. A new control-flow graph model of Miranda definitions is presented, and a model with four classes of callgraphs. Standard software metrics are applicable to these models. A Miranda front end for Prometrix, ¿, a tool for the automated analysis of flowgraphs and callgraphs, has been developed. This front end produces the flowgraph and callgraph representations of Miranda programs. Some features of the metric analyser are illustrated with an example program. The tool provides a promising access to standard metrics on functional programs

Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare

For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. The proposed approach allows for mixed time-series -- containing both pattern and non-pattern data -- such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. We present the early results of our approach in the context of monitoring the health status of a person at home. The purpose is to build a behavioral profile of a person by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors installed in the home

Abridged Petri Nets

A new graphical framework, Abridged Petri Nets (APNs) is introduced for bottom-up modeling of complex stochastic systems. APNs are similar to Stochastic Petri Nets (SPNs) in as much as they both rely on component-based representation of system state space, in contrast to Markov chains that explicitly model the states of an entire system. In both frameworks, so-called tokens (denoted as small circles) represent individual entities comprising the system; however, SPN graphs contain two distinct types of nodes (called places and transitions) with transitions serving the purpose of routing tokens among places. As a result, a pair of place nodes in SPNs can be linked to each other only via a transient stop, a transition node. In contrast, APN graphs link place nodes directly by arcs (transitions), similar to state space diagrams for Markov chains, and separate transition nodes are not needed. Tokens in APN are distinct and have labels that can assume both discrete values ("colors") and continuous values ("ages"), both of which can change during simulation. Component interactions are modeled in APNs using triggers, which are either inhibitors or enablers (the inhibitors' opposites). Hierarchical construction of APNs rely on using stacks (layers) of submodels with automatically matching color policies. As a result, APNs provide at least the same modeling power as SPNs, but, as demonstrated by means of several examples, the resulting models are often more compact and transparent, therefore facilitating more efficient performance evaluation of complex systems.Comment: 17 figure