Declarative modeling for deploying a container platform

Cloud computing services provide several benefits in terms of flexibility, scalability and cost reductions. Container technology allows to further lower the overhead of virtualization making it possible to run more components per server. Designing and deploying a cloud platform requires significant effort and it should be possibly dealt with automation tools. Automation can be dealt through either an imperative or declarative approach. We present how we designed and deployed a cloud container platform using declarative modeling. A model of the architecture of the service is described through a declarative specification and then passed to an orchestration tool that generates the actual plan of steps to be performed in the deployment

Declarative Modeling for Cloud Deployments

Cloud compung offers several benefits: resources can be allocated on demand, scaling according to varying usage paerns and eventually reducing the costs for individual groups to provision and maintain their own infrastructure. Providing cloud compung services besides connecvity is a natural evoluon for NRENs as users demand such services and also because the networking infrastructure itself is evolving towards using virtualizaon techniques. Building a cloud plaorm is though a daunng task, that requires coordinang the deployment of many services, interrelated and dependent on each other. Provisioning, servicing and maintaining the plaorm should be automated. For the deployment of the GARR Federated Cloud Compung Plaorm, we chose an intent‑based approach, which relies on declarave modeling for specifying the requirements of the service provisioning, describing the parts that compose the system, any specific constraint requirements and the supplier/consumer relaons between them. An automated orchestraon tool analyzes the model, compares it with the current state of the system being deployed, determines the resources that need to be provisioned, generates a sequence of executable steps needed to bring the deployment in line with the model, and coordinat

Deployment of job priority mechanisms in the Italian Cloud of the ATLAS experiment

An optimized use of the Grid computing resources in the ATLAS experiment requires the enforcement of a mechanism of job priorities and of resource sharing among the different activities inside the ATLAS VO. This mechanism has been implemented through the VOViews publication in the information system and the fair share implementation per UNIX group in the batch system. The VOView concept consists of publishing resource information, such as running and waiting jobs, as a function of VO groups and roles. The ATLAS Italian Cloud is composed of the CNAF Tier1 and Roma Tier2, with farms based on the LSF batch system, and the Tier2s of Frascati, Milano and Napoli based on PBS/Torque. In this paper we describe how test and deployment of the job priorities has been performed in the cloud, where the VOMS-based regional group /atlas/it has been created. We show that the VOViews are published and correctly managed by the WMS and that the resources allocated to generic VO users, users with production role and users of the /atlas/it group correspond to the defined share

HPC4AI, an AI-on-demand federated platform endeavour

In April 2018, under the auspices of the POR-FESR 2014-2020 program of Italian Piedmont Region, the Turin's Centre on High- Performance Computing for Artificial Intelligence (HPC4AI) was funded with a capital investment of 4.5Me and it began its deployment. HPC4AI aims to facilitate scientific research and engineering in the areas of Artificial Intelligence and Big Data Analytics. HPC4AI will specifically focus on methods for the on-demand provisioning of AI and BDA Cloud services to the regional and national industrial community, which includes the large regional ecosystem of Small- Medium Enterprises (SMEs) active in many different sectors such as automotive, aerospace, mechatronics, manufacturing, health and agrifood