Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Design and Programming Models

Participants : Pascal Fradet, Alain Girault, Gregor Goessler, Xavier Nicollin, Arash Shafiei, Jean-Bernard Stefani, Martin Vassor, Souha Ben Rayana.


The location graph framework we have introduced in [66] has evolved into the Hypercell framework presented in [18]. The Hypercell framework allows the definition of different component models for dynamic software architectures featuring both sharing and encapsulation. The basic behavioral theory of hypercells in the form of a contextual bisimulation has been developed and we are currently developing proofs of correctness for encapsulation policies based on this theory.

In collaboration with the Spirals team at Inria Lille – Nord Europe, and Orange, we have used hypercells as a pivot model for developing interpretations, formally defined with the Alloy specification language, of various languages and formalisms for the description of software configurations for cloud computing environments. Configuration languages considered include the TOSCA and OCCI standards, as well as the Open Stack Heat Orchestration Template (HOT), Docker Compose, and the Aeolus component model for cloud deployment. This work, developed as part of a bilateral contract with Orange, allowed the development of a verification tool for the correctness of HOT configurations, and helped uncover several flaws in the ETSI NFV standard.

Dynamicity in dataflow models

Recent dataflow programming environments support applications whose behavior is characterized by dynamic variations in resource requirements. The high expressive power of the underlying models (e.g., Kahn Process Networks or the CAL actor language) makes it challenging to ensure predictable behavior. In particular, checking liveness (i.e., no part of the system will deadlock) and boundedness (i.e., the system can be executed in finite memory) is known to be hard or even undecidable for such models. This situation is troublesome for the design of high-quality embedded systems. In the past few years, we have proposed several parametric dataflow models of computation (MoCs)  [49], [39], we have written a survey providing a comprehensive description of the existing parametric dataflow MoCs  [42], and we have studied symbolic analyses of dataflow graphs  [43]. More recently, we have proposed an original method to deal with lossy communication channels in dataflow graphs  [48].

We are nowadays studying models allowing dynamic reconfigurations of the topology of the dataflow graphs. This is required by many modern streaming applications that have a strong need for reconfigurability, for instance to accommodate changes in the input data, the control objectives, or the environment.

We have proposed a new MoC called Reconfigurable Dataflow (RDF) [13]. RDF extends SDF with transformation rules that specify how the topology and actors of the graph may be reconfigured. Starting from an initial RDF graph and a set of transformation rules, an arbitrary number of new RDF graphs can be generated at runtime. Transformations can be seen as graph rewriting rules that match some sub-part of the dataflow graph and replace it by another one. Transformations can be applied an arbitrary number of times during execution and therefore can produce an arbitrary number of new graphs. The major feature and advantage of RDF is that it can be statically analyzed to guarantee that all possible graphs generated at runtime will be connected, consistent, and live. To the best of our knowledge, RDF is the only dataflow MoC allowing an arbitrary number of topological reconfigurations while remaining statically analyzable. It remains to complete the RDF implementation and to evaluate it on realistic case studies. Preliminary results indicate that dynamic reconfigurations can be implemented efficiently.

This is the research topic of Arash Shafiei's PhD, in collaboration with Orange Labs.