Abstract Deploying a cloud configuration in a real cloud platform is mostly cost- and time- consuming, as large number of cloud resources have to be rented for the time needed to run the configuration. Thereafter, cloud simulation tools are used as a cheap alternative to test cloud configuration. However, most of the existing cloud simulation tools require extensive technical skills and do not support simulation of any kind of cloud resources. In this context, using a model-driven approach can be helpful as it allows developers to efficiently describe their needs at a high level of abstraction. To do, we propose, in this article, a model-driven engineering approach based on the Open Cloud Computing Interface(OCCI) standard metamodel and CloudSim toolkit. We firstly extend OCCI metamodel for the supporting simulation of any kind of cloud resources. Afterward, to illustrate the extensibility of our approach, we enrich the proposed metamodel by new simulation capabilities. As proof of concept, we study the elasticity and pricing strategies of Amazon Web Services (AWS). This article benefits from OCCIware Studio to design an OCCI simulation extension and to provide a simulation designer for designing cloud configurations to be simulated. We detail the approach process from defining an OCCI simulation extension until the generation and the simulation of the OCCI cloud configurations. Finally, we validate the proposed approach by providing a realistic experimentation to study its usability, the resources coverage rate and the cost. The results are compared with the ones computed from AWS.

Elasticity is considered as a fundamental feature of cloud computing where the system capacity can adjust to the current application workloads by provisioning or de-provisioning computing resources automatically and timely. Many studies have been already conducted to elasticity management systems, however, almost all lack to offer a complete modular solution. In this article, we proposeMoDEMO, a new elasticity management system powering both vertical and horizontal elasticities, both VM and Container virtualization technologies, multiple cloud providers simultaneously, and various elasticity policies.MoDEMOis characterized by the following features: it represents (i) the first system that manages elasticity using Open Cloud Computing Interface (OCCI) model with respect to the OCCI standard specifications, (ii) the first unified system which combines the functionalities of the worldwide cloud providers: Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP), and (iii) allows a dynamic configuration at runtime during the execution of the application.MoDEMOpermits to timely adapt resource capacity according to the workload intensity and increase application performance without introducing a significant overhead.

The production of huge amount of data and the emergence of new technologies in the industry sector have introduced new requirements for big data management. Many applications need to interact with several heterogeneous data sources to ingest, harmonise (normalise), persist, analyse and synthesize results to enable informed decisions and draw benefits from data. These operations are ensured by different tools and these tools are heterogeneous and not connected with each other. Besides, the whole tool-chain lacks automation in terms of its deployment, its operational workflow and its orchestration for satisfying the elastic and resilient properties needed by Industry. In this paper, we present FADI, a framework for deploying and orchestrating a Big Data management and analysis platform fully composed of open source tools. FADI has been developed through several research projects, namely, BigData@MA, Grinding 4.0, Quality 4.0 and ARTEMTEC where Industry use cases are used for validation purposes.

With the advent of cloud computing, different cloud providers with heterogeneous services and Application Programming Interfaces (APIs) have emerged. Hence, building an interoperable multi-cloud system becomes a complex task. Our idea is to design fclouds framework to achieve semantic interoperability in multi-clouds, i.e., to identify the common concepts between cloud APIs and to reason over them. In this paper, we propose to take advantage of the Open Cloud Computing Interface (OCCI) standard and the Alloy formal specification language to define the fclouds language, which is a formal language for specifying heterogeneous cloud APIs. To do so, we formalize OCCI concepts and operational semantics, then we identify and validate five properties (consistency, sequentiality, reversibility, idempotence and safety) that denote their characteristics. To demonstrate the effectiveness of our cloud formal language, we present thirteen case studies where we formally specify infrastructure, platform, Internet of Things (IoT) and transverse cloud concerns. Thanks to the Alloy analyzer, we verify that these heterogeneous APIs uphold the properties of fclouds and also validate their own specific properties. Then, thanks to formal transformation rules and equivalence properties, we draw a precise alignment between our case studies, which promotes semantic interoperability in a multi-cloud system.

Elasticity is a key feature in cloud computing as it enables the automatic and timely provisioning and depro- visioning of computing resources. To achieve elasticity, clouds rely on virtualization techniques including Virtual Machines (VMs) and containers. While many studies address the vertical elasticity of VMs and other few works handle vertical elasticity of containers, no work manages the coordination between these two ver- tical elasticities. In this paper, we present the first approach to coordinate vertical elasticity of both VMs and containers. We propose an auto-scaling technique that allows containerized applications to adjust their resources at both container and VM levels. This work has been evaluated and validated using the RUBiS benchmark application. The results show that our approach reacts quickly and improves application perfor- mance. Our coordinated elastic controller outperforms container vertical elasticity controller by 18.34% and VM vertical elasticity controller by 70%. It also outperforms container horizontal elasticity by 39.6%.

To tackle the cloud-provider lock-in, the Open Grid Forum (OGF) is developing the Open Cloud Computing Interface (OCCI), a standardized interface for managing any kind of cloud resources. Besides the OCCI Core model, which defines the basic modeling elements for cloud resources, the OGF also defines extensions that reflect the requirements of different cloud service levels, such as IaaS and PaaS. However, so far the OCCI PaaS extension is very coarse grained and lacks of supporting use cases and implementations. Especially, it does not define how the components of the application itself can be managed. In this paper, we present a model-driven framework that extends the OCCI PaaS extension and is able to use different configuration management tools to manage the whole lifecycle of cloud applications. We demonstrate the feasibility of the approach by presenting four different use cases and prototypical implementations for three different configuration management tools.

Today, Google Cloud Platform (GCP) is one of the leaders among cloud APIs. Although it was established only five years ago, GCP has gained notable expansion due to its suite of public cloud services that it based on a huge, solid infrastructure. GCP allows developers to use these services by accessing GCP RESTful API that is described through HTML pages on its website. However, the documentation of GCP API is written in natural language (English prose) and therefore shows several drawbacks, such as Informal Heterogeneous Documentation, Imprecise Types, Implicit Attribute Metadata, Hidden Links, Redundancy and Lack of Visual Support. To avoid confusion and misunderstandings, the cloud developers obviously need a precise specification of the knowledge and activities in GCP. Therefore, this paper introduces GCP Model, an inferred formal model-driven specification of GCP which describes without ambiguity the resources offered by GCP. GCP Model is conform to the Open Cloud Computing Interface (OCCI) metamodel and is implemented based on the open source model-driven Eclipse-based OCCIware tool chain. Thanks to our GCP Model, we offer corrections to the drawbacks we identified.

ISBN : 978-989-758-210-3