Serverless Workflows for Containerised Applications in the Cloud Continuum.

This paper introduces an open-source platform to support serverless computing for scientific data-processing workflow-based applications across the Cloud continuum (i.e. simultaneously involving both on-premises and public Cloud platforms to process data captured at the edge). This is achieved via dynamic resource provisioning for FaaS platforms compatible with scale-to-zero approaches that minimise resource usage and cost for dynamic workloads with different elasticity requirements. The platform combines the usage of dynamically deployed auto-scaled Kubernetes clusters on on-premises Clouds and automated Cloud bursting into AWS Lambda to achieve higher levels of elasticity. A use case in public health for smart cities is used to assess the platform, in charge of detecting people not wearing face masks from captured videos. Faces are blurred for enhanced anonymity in the on-premises Cloud and detection via Deep Learning models is performed in AWS Lambda for this data-driven containerised workflow. The results indicate that hybrid workflows across the Cloud continuum can efficiently perform local data processing for enhanced regulations compliance and perform Cloud bursting for increased levels of elasticity.

APRICOT: Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools.

BACKGROUND: Scientific publications are meant to exchange knowledge among researchers but the inability to properly reproduce computational experiments limits the quality of scientific research. Furthermore, bibliography shows that irreproducible preclinical research exceeds 50%, which produces a huge waste of resources on nonprofitable research at Life Sciences field. As a consequence, scientific reproducibility is being fostered to promote Open Science through open databases and software tools that are typically deployed on existing computational resources. However, some computational experiments require complex virtual infrastructures, such as elastic clusters of PCs, that can be dynamically provided from multiple clouds. Obtaining these infrastructures requires not only an infrastructure provider, but also advanced knowledge in the cloud computing field. OBJECTIVES: The main aim of this paper is to improve reproducibility in life sciences to produce better and more cost-effective research. For that purpose, our intention is to simplify the infrastructure usage and deployment for researchers. METHODS: This paper introduces Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools (APRICOT), an open source extension for Jupyter to deploy deterministic virtual infrastructures across multiclouds for reproducible scientific computational experiments. To exemplify its utilization and how APRICOT can improve the reproduction of experiments with complex computation requirements, two examples in the field of life sciences are provided. All requirements to reproduce both experiments are disclosed within APRICOT and, therefore, can be reproduced by the users. RESULTS: To show the capabilities of APRICOT, we have processed a real magnetic resonance image to accurately characterize a prostate cancer using a Message Passing Interface cluster deployed automatically with APRICOT. In addition, the second example shows how APRICOT scales the deployed infrastructure, according to the workload, using a batch cluster. This example consists of a multiparametric study of a positron emission tomography image reconstruction. CONCLUSION: APRICOT's benefits are the integration of specific infrastructure deployment, the management and usage for Open Science, making experiments that involve specific computational infrastructures reproducible. All the experiment steps and details can be documented at the same Jupyter notebook which includes infrastructure specifications, data storage, experimentation execution, results gathering, and infrastructure termination. Thus, distributing the experimentation notebook and needed data should be enough to reproduce the experiment.

A Visual Dashboard to Track Learning Analytics for Educational Cloud Computing.

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, and this information can be disclosed via educational dashboards for students to understand their progress through the practical activities. To this aim, this paper introduces CloudTrail-Tracker, an open-source platform to obtain enhanced usage analytics from a shared AWS account. The tool provides the instructor with a visual dashboard that depicts the aggregated usage of resources by all the students during a certain time frame and the specific use of AWS for a specific student. To facilitate self-regulation of students, the dashboard also depicts the percentage of progress for each lab session and the pending actions by the student. The dashboard has been integrated in four Cloud subjects that use different learning methodologies (from face-to-face to online learning) and the students positively highlight the usefulness of the tool for Cloud instruction in AWS. This automated procurement of evidences of student activity on the Cloud results in close to real-time learning analytics useful both for semi-automated assessment and student self-awareness of their own training progress.

Exploring the role of pH in modulating the effects of lidocaine in virtual ischemic tissue.

Lidocaine is a class I antiarrhytmic drug that blocks Na(+) channels and exists in both neutral and charged forms at a physiological pH. In this work, a mathematical model of pH and the frequency-modulated effects of lidocaine has been developed and incorporated into the Luo-Rudy model of the ventricular action potential. We studied the effects of lidocaine on Na(+) current, maximum upstroke velocity, and conduction velocity and demonstrated that a decrease of these parameters was dependent on pH, frequency, and concentration. We also tested the action of lidocaine under pathological conditions. Specifically, we investigated its effects on conduction block under acute regional ischemia. Our results in one-dimensional fiber simulations showed a reduction of the window of block in the presence of lidocaine, thereby highlighting the role of reduced conduction velocity and safe conduction. This reduction may be related to the antifibrillatory effects of the drug by hampering wavefront fragmentation. In bidimensional acute ischemic tissue, lidocaine increased the vulnerable window for reentry and exerted proarrhythmic effects. In conclusion, the present simulation study used a newly formulated model of lidocaine, which considers pH and frequency modulation, and revealed the mechanisms by which lidocaine facilitates the onset of reentries. The results of this study also help to increase our understanding of the potential antifibrillatory effects of the drug.

Protein design based on parallel dimensional reduction.

The design of proteins with targeted properties is a computationally intensive task with large memory requirements. We have developed a novel approach that combines a dimensional reduction of the problem with a High Performance Computing platform to efficiently design large proteins. This tool overcomes the memory limits of the process, allowing the design of proteins whose requirements prevent them to be designed in traditional sequential platforms. We have applied our algorithm to the design of functional proteins, optimizing for both catalysis and stability. We have also studied the redesign of dimerization interfaces, taking simultaneously into account the stability of the subunits of the dimer. However, our methodology can be applied to any computational chemistry application requiring combinatorial optimization techniques.

A grid computing-based approach for the acceleration of simulations in cardiology.

This paper combines high-performance computing and grid computing technologies to accelerate multiple executions of a biomedical application that simulates the action potential propagation on cardiac tissues. First, a parallelization strategy was employed to accelerate the execution of simulations on a cluster of personal computers (PCs). Then, grid computing was employed to concurrently perform the multiple simulations that compose the cardiac case studies on the resources of a grid deployment, by means of a service-oriented approach. This way, biomedical experts are provided with a gateway to easily access a grid infrastructure for the execution of these research studies. Emphasis is stressed on the methodology employed. In order to assess the benefits of the grid, a cardiac case study, which analyzes the effects of premature stimulation on reentry generation during myocardial ischemia, has been carried out. The collaborative usage of a distributed computing infrastructure has reduced the time required for the execution of cardiac case studies, which allows, for example, to take more accurate decisions when evaluating the effects of new antiarrhythmic drugs on the electrical activity of the heart.

Vulnerability to reentry in a regionally ischemic tissue: a simulation study.

Sudden cardiac death is mainly provoked by arrhythmogenic processes. During myocardial ischemia many malignant arrhythmias, such as reentry, take place and can degenerate into ventricular fibrillation. It is thus of great interest to unravel the intricate mechanisms underlying the initiation and maintenance of a reentry. In this computational study, we analyze the probability of reentry during different stages of the acute phase of ischemia. We also aimed at the understanding of the role of its main components: hypoxia, hyperkalemia, and acidosis analyzing the intricate ionic mechanisms responsible for reentry generation. We simulated the electrical activity of a ventricular tissue affected by regional ischemia based on a modified version of the Luo-Rudy model (LRd00). The ischemic conditions were varied to simulate different stages of this pathology. After premature stimulation, we evaluated the vulnerability to reentry. We obtained an unimodal behavior for the vulnerable window as ischemia progressed, peaking at the eighth minute after the onset of ischemia where the vulnerable window yielded 58 ms. Under more severe conditions the vulnerable window decreased and became zero for minute 8.75. The present work provides insight into the mechanisms of reentry generation during ischemia, highlighting the role of acidosis and hypoxia when hyperkalemia is present.