In the context of Edge Computing or Cyber-Physical Systems, cross-functional, and cross-geographical DevOps teams are in charge of automating deployments, configuration, and management (i.e., reconfiguration) of complex, large-scale, highly dynamic, and geo-distributed service-oriented software systems. In this context, DevOps teams cannot reasonably manually coordinate their reconfiguration operations in a global manner. Furthermore, as disconnection is the norm in these paradigms, a central entity responsible for reconfiguration should be avoided, and the set of changes to apply should be as fast as possible. This paper presents Ballet, a fast tool to automate decentralized choreographies (i.e., coordination) of cross-DevOps reconfiguration. We show a gain of 42.6% for a deployment scenario and 24% for an update scenario on an OpenStack case study.

SyDPaCC (Systematic Development of programs for Parallel and Cloud Computing) is a framework for the Coq interactive theorem prover. It allows to systematically develop correct parallel programs from specifications via verified and automated program transformations. The obtained programs are scalable, i.e. able to run on numerous processors. SyDPaCC produces programs written in the multi-paradigm and functional programming language OCaml with calls to the BSML (Bulk Synchronous parallel ML) parallel programming library. In this paper we present ongoing work towards an extension of SyDPaCC to be able to produce Scala programs using Apache Spark for parallel processing.

Formal reasoning on model transformation languages allows users to certify model transformations against contracts. CoqTL includes a specification of a transformation engine in the Coq interactive theorem prover. An executable engine can be automatically extracted from this specification. Transformation contracts are proved by the user against the CoqTL specification and guaranteed to hold on the transformation running on the extracted implementation of CoqTL. The design of the transformation engine specification in CoqTL aims at easing the certification step, but this requirement harms the execution performance of the extracted engine.

Algorithmic skeletons are patterns of parallel computations. Skeletal parallel programming eases parallel programming: a program is merely a composition of such patterns. Data-parallel skeletons operate on parallel data-structures that have often sequential counterparts. In algorithmic skeleton approaches that offer a global view of programs, a parallel program has therefore a structure similar to a sequential program but operates on parallel data-structures. PySke is such an algorithmic skeleton library for Python to program shared or distributed memory parallel architectures in a simple way. This paper presents an extension to PySke: new algorithmic skeletons on parallel lists. This extension is evaluated on an application.