Amanda Calatrava

Amanda Calatrava
Universitat Politècnica de València | UPV · Institute for Molecular Imaging Technologies (I3M)

Ph.D

About

17
Publications
8,167
Reads
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216
Citations
Introduction
I received the BSc, MSc and PhD degrees in computer science from the Universitat Politècnica de València (UPV) in 2010, 2012 and 2016, respectively. In 2011, I joined the Grid and High Performance Computing research group as a graduate under a collaboration fellowship while I worked on my Master’s Thesis. I currently work at the Institute for Molecular Imaging Technologies (I3M), at UPV. My research is focused in Computer Communications (Networks), Distributed Computing and Parallel Computing. I currently work in the ATMOSPHERE (http://www.atmosphere-eubrazil.eu/)' project.
Additional affiliations
December 2016 - present
Universitat Politècnica de València
Position
  • Researcher
September 2013 - January 2016
Universitat Politècnica de València
Position
  • Assistant professor in the laboratory sessions
Description
  • In the subject "Introducción a la Informática y a la Programación", as part of the Grade in Computer Science.
June 2013 - November 2016
Universitat Politècnica de València
Position
  • PhD Student
Description
  • High Performance Scientific Computing over Hybrid Cloud Platforms, available at: https://riunet.upv.es/handle/10251/75265
Education
June 2013 - September 2016
Universitat Politècnica de València
Field of study
  • Computer Science
October 2010 - March 2012
Universitat Politècnica de València
Field of study
  • Computer Science
September 2007 - September 2010
Escuela Técnica Superior de Ingeniería Informática, Universitat Politècnica de València
Field of study
  • Computer Science

Publications

Publications (17)
Article
This paper describes the developments to produce EC3 (Elastic Cloud Computing Cluster), a tool that creates self-managed cost-efficient virtual hybrid elastic clusters on top of Infrastructure as a Service (IaaS) Clouds. Using spot instances, together with checkpointing techniques, EC3 can significantly reduce the total cost of executions while int...
Conference Paper
Full-text available
This paper describes the research work in the context of the CLUVIEM project towards achieving migrat-able, self-managed virtual elastic clusters on hybrid Cloud infrastructures. These virtual clusters can span across on-premises and public Cloud infrastructures thus leveraging hybrid Cloud platforms. They are elastic since working nodes are automa...
Conference Paper
Full-text available
The advent of Cloud computing has paved the way to envision hybrid computational infrastructures based on powerful Grid resources combined with dynamic and elastic on-demand virtual infrastructures on top of Cloud deployments. However, the combination of Grid and Cloud resources for executing computationally intensive scientific applications introd...
Conference Paper
Full-text available
This paper describes a service-oriented architecture that eases the process of scientific application deployment and execution in IaaS Clouds, with a focus on High Throughput Computing applications. The system integrates i) a catalogue and repository of Virtual Machine Images, ii) an application deployment and configuration tool, iii) a meta-schedu...
Conference Paper
Full-text available
Clusters of PCs are one of the most widely used computing platforms in science and engineering, supporting different programming models. However, they suffer from lack of customizability, dificult extensibility and complex workload-balancing. To this end, this work introduces virtual hybrid elastic clusters that can simultaneously harness on-premis...
Article
Full-text available
Software programming is one of the key abilities for the development of Computational Thinking (CT) skills in Science, Technology, Engineering and Mathematics (STEM). However, specific software tools to emulate realistic scenarios are required for effective teaching. Unfortunately, these tools have some limitations in educational environments due t...
Article
Medical data processing has found a new dimension with the extensive use of machine-learning techniques to classify and extract features. Machine learning strongly benefits from computing accelerators. However, such accelerators are not easily available at hospital premises, although they can be easily found on public cloud infrastructures or resea...
Article
Full-text available
Cloud providers such as Amazon Web Services (AWS) stand out as useful platforms to teach distributed computing concepts as well as the development of Cloud-native scalable application architectures on real-world infrastructures. Instructors can benefit from high-level tools to track the progress of students during their learning paths on the Cloud,...
Conference Paper
Full-text available
Serverless computing has introduced unprecedented levels of scalability and parallelism for the execution of High Throughput Computing tasks. This represents a challenge and an opportunity for different scientific workloads to be adapted to upcoming programming models that simplify the usage of such platforms. In this paper we introduce a serverles...
Article
Full-text available
Computer clusters are widely used platforms to execute different computational workloads. Indeed, the advent of virtualization and Cloud computing has paved the way to deploy virtual elastic clusters on top of Cloud infrastructures, which are typically backed by physical computing clusters. In turn, the advances in Green computing have fostered the...
Article
Full-text available
This article describes the development of an automated configuration of a software platform for Data Analytics that supports horizontal and vertical elasticity to guarantee meeting a specific deadline. It specifies all the components, software dependencies and configurations required to build up the cluster, and analyses the deployment times of dif...
Article
Full-text available
New architectural patterns (e.g. microservices), the massive adoption of Linux containers (e.g. Docker containers), and improvements in key features of Cloud computing such as auto-scaling, have helped developers to decouple complex and monolithic systems into smaller stateless services. In turn, Cloud providers have introduced serverless computing...
Article
Full-text available
eScience demands large-scale computing clusters to support the efficient execution of resource-intensive scientific applications. Virtual Machines (VMs) have introduced the ability to provide customizable execution environments, at the expense of performance loss for applications. However, in recent years, containers have emerged as a light-weight...
Poster
Full-text available
Poster presented in the 10th IEEE International Conference on e-Science, as part of my PhD project.
Poster
Full-text available
Póster presentado en el I Encuentro de estudiantes de doctorado de la Universitat Politècnica de València.
Thesis
Full-text available
The advent of virtualization techniques in recent years has led to the emergence of Cloud Computing. This new technology has paved the way towards the use of hybrid computing infrastructures in science, based on powerful Grid resources combined with dynamic and elastic virtual infrastructures that provides the Cloud. But this combination of resourc...

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Projects

Projects (2)
Project
Expanding the capacity and capabilities of EOSC by leveraging the experience, effort and resources of national publicly-funded digital infrastructures. (https://www.eosc-synergy.eu/)
Project
In this context, INDIGO-DataCloud (INtegrating Distributed data Infrastructures for Global ExplOitation), a project funded under the Horizon2020 framework program of the European Union, aims at developing a data and computing platform targeting scientific communities, deployable on multiple hardware and provisioned over hybrid (private or public) e-infrastructures. By filling existing gaps in PaaS and SaaS levels, INDIGO-DataCloud will help developers, resources providers, e-infrastructures and scientific communities to overcome current challenges in the Cloud computing, storage and network areas.