Service level agreements (SLAs), or contracts, have an important role in Web services. These contracts define the obligations and rights between the provider of a Web service and its client, with respect to the function and the Quality of Service (QoS). For composite services like orchestrations, such contracts are deduced by a process called QoS contract composition, based on contracts established between the orchestration and the called Web services. These contracts are typically stated in the form of hard guarantees (e.g., response time always less than 5 msec). Using hard bounds is not realistic, however, and more statistical approaches are needed. In this paper, we propose using soft probabilistic contracts instead, which consist of a probability distribution for the considered QoS parameter-in this paper, we focus on timing. We show how to compose such contracts to yield a global probabilistic contract for the orchestration. Our approach is implemented by the TOrQuE tool. Experiments on TOrQuE show that overly pessimistic contracts can be avoided and significant room for safe overbooking exists. An essential component of SLA management is then the continuous monitoring of the performance of called Web services to check for violations of the agreed SLA. We propose a statistical technique for runtime monitoring of soft contracts.

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In this paper, we consider the diagnosis of asynchronous discrete event systems. We follow a so-called true concurrency approach, in which no global state and no global time is available. Instead, we use only local states in combination with a partial order model of time. Our basic mathematical tool is that of net unfoldings originating from the Petri net research area. This study was motivated by the problem of event correlation in telecommunications network management.

Predicting biological systems’ behaviors requires taking into account many molecular and genetic elements for which limited information is available past a global knowledge of their pairwise interactions. Logical modeling, notably with Boolean Networks (BNs), is a well-established approach that enables reasoning on the qualitative dynamics of networks. Several dynamical interpretations of BNs have been proposed. The synchronous and (fully) asynchronous ones are the most prominent, where the value of either all or only one component can change at each step. Here we prove that, besides being costly to analyze, these usual interpretations can preclude the prediction of certain behaviors observed in quantitative systems. We introduce an execution paradigm, the Most Permissive Boolean Networks (MPBNs), which offers the formal guarantee not to miss any behavior achievable by a quantitative model following the same logic. Moreover, MPBNs significantly reduce the complexity of dynamical analysis, enabling to model genome-scale networks.

In this paper we study the diagnosis of distributed asynchronous systems with concurrency. Diagnosis is performed by a peer-to-peer distributed architecture of supervisors. Our approach relies on Petri net unfoldings and event structures, as means to manipulate trajectories of systems with concurrency. This article is an extended version of the paper with same title, which appeared as a plenary address in the Proceedings of CONCUR'2003.

Abstract-We consider asynchronous diagnosis in (safe) Petri net models of distributed systems, using the partial order semantics of occurrence net unfoldings. Both the observability and diagnosability properties will appear in two different forms, depending on the semantics chosen: strong observability and diagnosability are the classical notions from the state machine model and correspond to interleaving semantics in Petri nets. By contrast, the weak form is linked to characteristics of nonsequential processes, and requires an asynchronous progress assumption on those processes. We give algebraic characterizations for both types, and give verification methods. The study of weak diagnosability leads us to the analysis of a relation in occurrence nets, first presented in [15]: given the occurrence of some event a that reveals b, the occurrence of b is inevitable. Then b may already have occurred, be concurrent to, or even in the future of a. We show that the reveals-relation can be effectively computed recursively -for each pair, a suitable finite prefix of bounded depth is sufficient -, and show its use in asynchronous diagnosis. Based on this relation, a decomposition of the Petri net unfolding into facets is defined, yielding an abstraction technique that preserves and reflects maximal partially ordered runs.

Abstract. Attractors of network dynamics represent the long-term behaviours of the modelled system. Their characterization is therefore crucial for understanding the response and differentiation capabilities of a dynamical system. In the scope of qualitative models of interaction networks, the computation of attractors reachable from a given state of the network faces combinatorial issues due to the state space explosion. In this paper, we present a new algorithm that exploits the concurrency between transitions of parallel acting components in order to reduce the search space. The algorithm relies on Petri net unfoldings that can be used to compute a compact representation of the dynamics. We illustrate the applicability of the algorithm with Petri net models of cell signalling and regulation networks, Boolean and multi-valued. The proposed approach aims at being complementary to existing methods for deriving the attractors of Boolean models, while being generic since it applies to any safe Petri net.

We address the sequential reprogramming of gene regulatory networks modelled as Boolean networks. We develop an attractor-based sequential reprogramming method to compute all sequential reprogramming paths from a source attractor to a target attractor, where only attractors of the network are used as intermediates. Our method is more practical than existing reprogramming methods as it incorporates several practical constraints: (1) only biologically observable states, viz. attractors, can act as intermediates; (2) certain attractors, such as apoptosis, can be avoided as intermediates; (3) certain nodes can be avoided to perturb as they may be essential for cell survival or difficult to perturb with biomolecular techniques; and (4) given a threshold k, all sequential reprogramming paths with no more than k perturbations are computed. We compare our method with the minimal one-step reprogramming and the minimal sequential reprogramming on a variety of biological networks. The results show that our method can greatly reduce the number of perturbations compared to the one-step reprogramming, while having comparable results with the minimal sequential reprogramming. Moreover, our implementation is scalable for networks of more than 60 nodes.