From Out-Place Transformation Evolution to In-Place Model Patching

Model transformation is a key technique to automate software engineering tasks. Like any other software, transformations are not resilient to change. As changes to transformations can invalidate previously produced models, these changes need to be reflected on existing models. Currently, revised out-place transformations are re-executed entirely to achieve this co-evolution task. However, this induces an unnecessary overhead, particularly when computation- intensive transformations are marginally revised, and if existing models have undergone updates prior the re-execution, these updates get discarded in the newly produced models. To overcome this co-evolution challenge, our idea is to infer from evolved out-place transformations patch transformations that propagate changes to existing models by re-executing solely the affected parts based on an in-place execution strategy. Thereby, existing models are only updated by a patch instead of newly produced. In this paper, we present the conceptual foundation of our approach and report on its evaluation in a real-world case study.European Commission ICT Policy Support Programme 31785

Optimal and Automated Deployment for Microservices

Microservices are highly modular and scalable Service Oriented Architectures. They underpin automated deployment practices like Continuous Deployment and Autoscaling. In this paper, we formalize these practices and show that automated deployment - proven undecidable in the general case - is algorithmically treatable for microservices. Our key assumption is that the configuration life-cycle of a microservice is split into two phases: (i) creation, which entails establishing initial connections with already available microservices, and (ii) subsequent binding/unbinding with other microservices. To illustrate the applicability of our approach, we implement an automatic optimal deployment tool and compute deployment plans for a realistic microservice architecture, modeled in the Abstract Behavioral Specification (ABS) language

SLO-ML:A Language for Service Level Objective Modelling in Multi-cloud applications

Cloud modelling languages (CMLs) are designed to assist customers in tackling the diversity of services in the current cloud market. While many CMLs have been proposed in the literature, they lack practical support for automating the selection of services based on the specific service level objectives of a customer's application. We put forward SLO-ML, a novel and generative CML to capture service level requirements. Subsequently, SLO-ML selects the services to honour the customer's requirements and generates the deployment code appropriate to these services. We present the architectural design of SLO-ML and the associated broker that realises the deployment operations. We evaluate SLO-ML using an experimental case study with a group of researchers and developers using a real-world cloud application. We also assess SLO-ML's overheads through empirical scalability tests. We express the promises of SLO-ML in terms of gained productivity and experienced usability, and we highlight its limitations by analysing it as application requirements grow

Towards automatic generation of UML profile graphical editors for papyrus

We present an approach for defining the abstract and concrete syntax of UML profiles and their equivalent Papyrus graphical editors using annotated Ecore metamodels, driven by automated model-to-model and model-to-text transformations. We compare our approach against manual UML profile specification and implementation using Archimate, a non-trivial enterprise modelling language, and we demonstrate the substantial productivity and maintainability benefits it delivers

An exploration of the determinants for decision to migrate existing resources to cloud computing using an integrated TOE-DOI model

Migrating existing resources to cloud computing is a strategic organisational decision that can be difficult. It requires the consideration and evaluation of a wide range of technical and organisational aspects. Although a significant amount of attention has been paid by many industrialists and academics to aid migration decisions, the procedure remains difficult. This is mainly due to underestimation of the range of factors and characteristics affecting the decision for cloud migration. Further research is needed to investigate the level of effect these factors have on migration decisions and the overall complexity. This paper aims to explore the level of complexity of the decision to migrate the cloud. A research model based on the diffusion of innovation (DOI) theory and the technology-organization-environment (TOE) framework was developed. The model was tested using exploratory and confirmatory factor analysis. The quantitative analysis shows the level of impact of the identified variables on the decision to migrate. Seven determinants that contribute to the complexity of the decisions are identified. They need to be taken into account to ensure successful migration. This result has expanded the collective knowledge about the complexity of the issues that have to be considered when making decisions to migrate to the cloud. It contributes to the literature that addresses the complex and multidimensional nature of migrating to the cloud

Postnatal Development of Numbers and Mean Sizes of Pancreatic Islets and Beta-Cells in Healthy Mice and GIPRdn Transgenic Diabetic Mice

The aim of this study was to examine postnatal islet and beta-cell expansion in healthy female control mice and its disturbances in diabetic GIPRdn transgenic mice, which exhibit an early reduction of beta-cell mass. Pancreata of female control and GIPRdn transgenic mice, aged 10, 45, 90 and 180 days were examined, using state-of-the-art quantitative-stereological methods. Total islet and beta-cell volumes, as well as their absolute numbers increased significantly until 90 days in control mice, and remained stable thereafter. The mean islet volumes of controls also increased slightly but significantly between 10 and 45 days of age, and then remained stable until 180 days. The total volume of isolated beta-cells, an indicator of islet neogenesis, and the number of proliferating (BrdU-positive) islet cells were highest in 10-day-old controls and declined significantly between 10 and 45 days. In GIPRdn transgenic mice, the numbers of islets and beta-cells were significantly reduced from 10 days of age onwards vs. controls, and no postnatal expansion of total islet and beta-cell volumes occurred due to a reduction in islet neogenesis whereas early islet-cell proliferation and apoptosis were unchanged as compared to control mice. Insulin secretion in response to pharmacological doses of GIP was preserved in GIPRdn transgenic mice, and serum insulin to pancreatic insulin content in response to GLP-1 and arginine was significantly higher in GIPRdn transgenic mice vs. controls. We could show that the increase in islet number is mainly responsible for expansion of islet and beta-cell mass in healthy control mice. GIPRdn transgenic mice show a disturbed expansion of the endocrine pancreas, due to perturbed islet neogenesis

Are Cloud Platforms Ready for Multi-cloud?

Part 2: Cloud Service and Platform SelectionInternational audienceMulti-cloud computing is getting a momentum as it offers various advantages, including vendor lock-in avoidance, better client proximity and application performance improvement. As such, various multi-cloud platforms have been developed, each with its own strengths and limitations. This paper aims at comparing all these platforms to unveil the best one as well as ease the selection of the right platform based on the user requirements and preferences. Further, it identifies the current gaps in the platforms to be covered so as to enable the full potential of multi-cloud computing. Finally, it draws directions for further research