arXiv subjects

Miguel Caballer

Publications and source records attributed to Miguel Caballer.

4 recordsLinked to original sources

arXiv2026

AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research

The rapid growth of Artificial Intelligence and Machine Learning in scientific research has highlighted a gap between industry-standard MLOps tools and platforms, and the unique requirements of modern and Open Science, particularly regarding the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This paper presents AI4EOSC, a federated, open-source platform designed to operationalize the full AI/ML lifecycle within the European Open Science Cloud (EOSC) ecosystem. Our methodology tackles the fragmentation of distributed research infrastructures by integrating a modular and distributed architecture comprising an AI development platform, a serverless AI-as-a-Service layer, and a federated orchestration model that is able to integrate heterogeneous compute and storage resources from distributed e-Infrastructures. AI4EOSC also introduces a ``FAIR-by-design'' approach that enforces metadata standardization (via MLDCAT-AP) and W3C PROV-compliant provenance tracking through a platform-integrated CI/CD pipeline. AI4EOSC added value is demonstrated through the delivery of a diverse set of community installations, showing consistent and seamless deployment across heterogeneous cloud providers. These installations are validated by a set of scientific cases, showing how our work reduces the manual burden on researchers while ensuring high levels of reproducibility and interoperability and providing an unified environment for development, training, and production of AI/ML models in the EOSC.

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arXiv2024

OSCAR-P and aMLLibrary: Profiling and Predicting the Performance of FaaS-based Applications in Computing Continua

This paper proposes an automated framework for efficient application profiling and training of Machine Learning (ML) performance models, composed of two parts: OSCAR-P and aMLLibrary. OSCAR-P is an auto-profiling tool designed to automatically test serverless application workflows running on multiple hardware and node combinations in cloud and edge environments. OSCAR-P obtains relevant profiling information on the execution time of the individual application components. These data are later used by aMLLibrary to train ML-based performance models. This makes it possible to predict the performance of applications on unseen configurations. We test our framework on clusters with different architectures (x86 and arm64) and workloads, considering multi-component use-case applications. This extensive experimental campaign proves the efficiency of OSCAR-P and aMLLibrary, significantly reducing the time needed for the application profiling, data collection, and data processing. The preliminary results obtained on the ML performance models accuracy show a Mean Absolute Percentage Error lower than 30% in all the considered scenarios.

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arXiv2021

Deployment of Elastic Virtual Hybrid Clusters Across Cloud Sites

Virtual clusters are widely used computing platforms than can be deployed in multiple cloud platforms. The ability to dynamically grow and shrink the number of nodes has paved the way for customised elastic computing both for High Performance Computing and High Throughput Computing workloads. However, elasticity is typically restricted to a single cloud site, thus hindering the ability to provision computational resources from multiple geographically distributed cloud sites. To this aim, this paper introduces an architecture of open-source components that coherently deploy a virtual elastic cluster across multiple cloud sites to perform large-scale computing. These hybrid virtual elastic clusters are automatically deployed and configured using an Infrastructure as Code (IaC) approach on a distributed hybrid testbed that spans different organizations, including on-premises and public clouds, supporting automated tunneling of communications across the cluster nodes with advanced VPN topologies. The results indicate that cluster-based computing of embarrassingly parallel jobs can benefit from hybrid virtual clusters that aggregate computing resources from multiple cloud back-ends and bring them together into a dedicated, albeit virtual network. The work presented in this article has been partially funded by the European Union's (EU) Horizon 2020 research project DEEP Hybrid-DataCloud (grant agreement No 777435).

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arXiv2017

Orchestrating Complex Application Architectures in Heterogeneous Clouds

Private cloud infrastructures are now widely deployed and adopted across technology industries and research institutions. Although cloud computing has emerged as a reality, it is now known that a single cloud provider cannot fully satisfy complex user requirements. This has resulted in a growing interest in developing hybrid cloud solutions that bind together distinct and heterogeneous cloud infrastructures. In this paper we describe the orchestration approach for heterogeneous clouds that has been implemented and used within the INDIGO-DataCloud project. This orchestration model uses existing open-source software like OpenStack and leverages the OASIS Topology and Specification for Cloud Applications (TOSCA) open standard as the modeling language. Our approach uses virtual machines and Docker containers in an homogeneous and transparent way providing consistent application deployment for the users. This approach is illustrated by means of two different use cases in different scientific communities, implemented using the INDIGO-DataCloud solutions.

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