CarPal: interconnecting overlay networks for a community-driven shared mobility

http://www-sop.inria.fr/lognet/carpalInternational audienceCar sharing and carpooling have proven to be an effective solution to reduce the amount of running vehicles by increasing the number of passengers per car amongst medium/big communities like schools or enterprises. However, the success of such practice relies on the community ability to effectively share and retrieve information about travelers and itineraries. Structured overlay networks such as Chord have emerged recently as a flexible solution to handle large amount of data without the use of high-end servers, in a decentralized manner. In this paper we present CarPal, a proof-of-concept for a mobility sharing application that leverages a Distributed Hash Table to allow a community of people to spontaneously share trip information without the costs of a centralized structure. The peer-to-peer architecture allows moreover the deployment on portable devices and opens new scenarios where trips and sharing requests can be updated in real time. Using an original protocol already developed that allows to interconnect different overlays/communities, the success rate (number of shared rides) can be boosted up thus increasing the effectiveness of our solution. Simulations results are shown to give a possible estimate of such effectiveness

Formalizing Functions as Processes

We present the first formalization of Milner’s classic translation of the λ-calculus into the π-calculus. It is a challenging result with respect to variables, names, and binders, as it requires one to relate variables and binders of the λ-calculus with names and binders in the π-calculus. We formalize it in Abella, merging the set of variables and the set of names, thus circumventing the challenge and obtaining a neat formalization. About the translation, we follow Accattoli’s factoring of Milner’s result via the linear substitution calculus, which is a λ-calculus with explicit substitutions and contextual rewriting rules, mediating between the λ-calculus and the π-calculus. Another aim of the formalization is to investigate to which extent the use of contexts in Accattoli’s refinement can be formalized

Feature-Model-Guided Online Learning for Self-Adaptive Systems

A self-adaptive system can modify its own structure and behavior at runtime based on its perception of the environment, of itself and of its requirements. To develop a self-adaptive system, software developers codify knowledge about the system and its environment, as well as how adaptation actions impact on the system. However, the codified knowledge may be insufficient due to design time uncertainty, and thus a self-adaptive system may execute adaptation actions that do not have the desired effect. Online learning is an emerging approach to address design time uncertainty by employing machine learning at runtime. Online learning accumulates knowledge at runtime by, for instance, exploring not-yet executed adaptation actions. We address two specific problems with respect to online learning for self-adaptive systems. First, the number of possible adaptation actions can be very large. Existing online learning techniques randomly explore the possible adaptation actions, but this can lead to slow convergence of the learning process. Second, the possible adaptation actions can change as a result of system evolution. Existing online learning techniques are unaware of these changes and thus do not explore new adaptation actions, but explore adaptation actions that are no longer valid. We propose using feature models to give structure to the set of adaptation actions and thereby guide the exploration process during online learning. Experimental results involving four real-world systems suggest that considering the hierarchical structure of feature models may speed up convergence by 7.2% on average. Considering the differences between feature models before and after an evolution step may speed up convergence by 64.6% on average. [...

Modeling and Analysis of Self-Adaptive Systems Based on Graph Transformation

Software systems nowadays require continuous operation despite changes both in user needs and in their operational environments. Self-adaptive systems are typically instrumented with tools to autonomously perform adaptation to these changes while maintaining some desired properties. In this paper we model and analyze self-adaptive systems by means of typed, attributed graph grammars. The interplay of different grammars representing the application and the adaptation logic is realized by an adaption manager. Within this formal framework we define consistency and operational properties that are maintained despite adaptations and we give static conditions for their verification. The overall approach is supported by the AGG tool that offers the features for modeling, simulating, and analyzing graph transformation systems. A case study modeling a business process that adapts to changing environment conditions is used to demonstrate and validate the formal framework