Franck Chauvel

Towards Model-Driven Provisioning, Deployment, Monitoring, and Adaptation of Multi-cloud Systems

In the landscape of cloud computing, the competition between providers has led to an ever growing number of cloud solutions offered to consumers. The ability to run and manage multi-cloud systems (i.e., applications on multiple clouds) allows exploiting the peculiarities of each cloud solution and hence optimising the performance, availability, and cost of the applications. However, these cloud solutions are typically heterogeneous and the provided features are often incompatible. This diversity hinders the proper exploitation of the full potential of cloud computing, since it prevents interoperability and promotes vendor lock-in, as well as it increases the complexity of development and administration of multi-cloud systems. This problem needs to be addressed promptly. In this paper, we provide a classification of the state-of-the-art of cloud solutions, and argue for the need for model-driven engineering techniques and methods facilitating the specification of provisioning, deployment, monitoring, and adaptation concerns of multi-cloud systems at design-time and their enactment at run-time.

Cloud MF: Applying MDE to Tame the Complexity of Managing Multi-cloud Applications

The market of cloud computing encompasses an ever-growing number of cloud providers offering a multitude of infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) solutions. The heterogeneity of these solutions hinders the proper exploitation of cloud computing since it prevents interoperability and promotes vendor lock-in, which increases the complexity of executing and managing multi-cloud applications (i.e., Applications that can be deployed across multiple cloud infrastructures and platforms). Providers of multi-cloud applications seek to exploit the peculiarities of each cloud solution and to combine the delivery models of IaaS and PaaS in order to optimise performance, availability, and cost. In this paper, we show how the Cloud Modelling Framework leverages upon model-driven engineering to tame this complexity by providing: (i) a tool-supported domain-specific language for specifying the provisioning and deployment of multi-cloud applications, and (ii) a models@run-time environment for enacting the provisioning, deployment, and adaptation of these applications.

Managing multi-cloud systems with CloudMF

Dynamically adaptive systems (DAS) enable the continuous design and adaptation of complex software systems, but their main focus is limited to the application itself rather than the underlying platform and infrastructure. Cloud computing, in contrast, enables the management of the complete software stack, but it lacks integration with software engineering approaches, techniques, and methods from DAS. Model-based approaches have been successfully adopted for modelling DAS at design-time and facilitate their adaptation at run-time. Therefore, a natural next step is to adopt model-based approaches to enable cloud-based DAS. In this paper, we present the Cloud Modelling Framework (CloudMF), a model-based framework that addresses this issue. It consists of (i) a tool-supported domain-specific modelling language to model the provisioning and deployment of multi-cloud systems, and (ii) a models@run-time environment for enacting the provisioning, deployment and adaptation of these systems.

SENSAPP as a Reference Platform to Support Cloud Experiments: From the Internet of Things to the Internet of Services

The Cloud-computing paradigm was considered as a revolution. Thanks to the abstraction of computing resources in the clouds this paradigm provides anything as a service, on a pay-as-you-go basis. Unfortunately, there is no reference software that one can use to properly compare a cloud approach against others. We propose the SENSAPP platform to tackle this challenge. SENSAPP is designed as a prototypical cloud application and is provided as an open-source service-based application used to store and exploit data collected by the Internet of Things. We propose SENSAPP as a reference implementation to compare different cloud approaches. In this paper we present initial experiments about scalability based on this platform.

Web Services Composition: Mashups Driven Orchestration Definition

On the one hand, mashups are a new kind of Web application built upon the composition of different resources in a user-friendly way. Tools based on such concepts focus on graphic design and allows final users to build complex applications using pipes to connect data sources into a data--flow. It underlines a constant need for making services resuable in an easy way. On the other hand, Web Services Oriented Architecture (WSOA) supports development of high quality applications based on a control-flow between services. We explore in this paper how a WSOA can be defined as a data-flow in a mashup-like approach, where Model Driven Engineering techniques enable a clever composition of data-flows and the generation of control-flows based architecture.

Evaluating robustness of cloud-based systems

Various services are now available in the Cloud, ranging from turnkey databases and application servers to high-level services such as continuous integration or source version control. To stand out of this diversity, robustness of service compositions is an important selling argument, but which remains difficult to understand and estimate as it does not only depend on services but also on the underlying platform and infrastructure. Yet, choosing a specific service composition may fail to deliver the expected robustness, but reverting early choices may jeopardise the success of any Cloud project.Inspired by existing models used in Biology to quantify the robustness of ecosystems, we show how to tailor them to obtain early indicators of robustness for cloud-based deployments. This technique helps identify weakest parts in the overall architecture and in turn mitigates the risk of having to revert key architectural choices. We illustrate our approach by comparing the robustness of four alternative deployments of the SensApp application, which includes a MongoDB database, four REST services and a graphical web-front end.

DIVERSIFY: Ecology-inspired software evolution for diversity emergence

DIVERSIFY is an EU funded project, which aims at favoring spontaneous diversification in software systems in order to increase their adaptive capacities. This objective is founded on three observations: software has to constantly evolve to face unpredictable changes in its requirements, execution environment or to respond to failure (bugs, attacks, etc.): the emergence and maintenance of high levels of diversity are essential to provide adaptive capacities to many forms of complex systems, ranging from ecological and biological systems to social and economical systems; diversity levels tend to be very low in software systems. DIVERSIFY explores how the biological evolutionary mechanisms, which sustain high levels of biodiversity in ecosystems (speciation, phenotypic plasticity and natural selection) can be translated in software evolution principles. In this work, we consider evolution as a driver for diversity as a means to increase resilience in software systems. In particular, we are inspired by bipartite ecological relationships to investigate the automatic diversification of the server side of a client-server architecture. This type of software diversity aims at mitigating the risks of software monoculture. The consortium gathers researchers from the software-intensive, distributed systems and the ecology areas in order to transfer ecological concepts and processes as software design principles.

The BVR tool bundle to support product line engineering

The Base Variability Resolution (BVR) is a modern language to build software product lines (SPL). The language incorporates advanced concepts for feature modeling, reuse and realization of components in SPL. The BVR bundle implements and supports the language. The tool covers design, implementation and quality assurance to close the development cycle. The bundle enables feature modeling, resolution, realization and derivation of products, their testing and analysis. We integrate the SPLCA additions to provide the state of the art algorithms for analysis. The project is open-source and available for practitioners. The tool consists of Eclipse plug-ins which work seamlessly together as well as separate stand-alone components. We describe how the tool collaboration contributes to variability modeling. In addition, we present how the bundle applies well-known design patterns, principals to achieve synergy between components.

Continous deployment of multi-cloud systems

In this paper we present our mechanism and tooling for the continuous deployment and resource provisioning of multi-cloud applications. In order to facilitate collaboration between development and operation teams as promoted in the DevOps movement, our deployment and resource provisioning engine is based on the Models@Runtime principles. This enables applying the same concepts and language (i.e., CloudML) for deployment and resource provisioning at development-and operation-time.

Building product-lines of mixed-criticality systems

Mixed-Criticality Systems (MCS) reconcile safetycritical requirements with multi-core architectures, by offering spatial and temporal isolation while preserving other extrafunctional properties such as optimised energy consumption or minimised latencies. MCS designers struggle to manually balance the offered functionalities with pertinent implementation choices in order to ensure that the system eventually meets all constraints. Existing attempts to further automate this process focus on specific concerns, and fail to account for variation in system functionalities. Our contribution is to integrate product-lines that capture functional variations with evolutionary optimisation to explore possible implementations and their impact on extrafunctional properties. Our solution is a model-driven process (and a tool prototype) to automatically select functionally different products that balance well the various concerns of interest. We illustrate how this process applies to the construction of wind turbines. Moving toward product-lines eventually contributes to reduce high development costs and the long time to market associated with MCS.