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The explosion of data volumes generated by an increasing number of applications is strongly impacting the evolution of distributed digital infrastructures for data analytics and machine learning (ML). While data analytics used to be mainly performed on cloud infrastructures, the rapid development of IoT infrastructures and the requirements for low-latency, secure processing has motivated the development of edge analytics. Today, to balance various trade-offs, ML-based analytics tends to increasingly leverage an interconnected ecosystem that allows complex applications to be executed on hybrid infrastructures where IoT Edge devices are interconnected to Cloud/HPC systems in what is called the Computing Continuum , the Digital Continuum , or the Transcontinuum. Enabling learning-based analytics on such complex infrastructures is challenging. The large scale and optimized deployment of learning-based workflows across the Edge-to-Cloud Continuum requires extensive and reproducible experimental analysis of the application execution on representative testbeds. This is necessary to help understand the performance trade-offs that result from combining a variety of learning paradigms and supportive frameworks. A thorough experimental analysis requires the assessment of the impact of multiple factors, such as: model accuracy, training time, network overhead, energy consumption, processing latency, among others. This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. It describes the main learning paradigms enabling learning-based analytics on the Edge-to-Cloud Continuum. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed. Furthermore, we analyze how the selected systems provide support for experiment reproducibility. We conclude our review with a detailed discussion of relevant open research challenges and of future directions in this domain such as: holistic understanding of performance; performance optimization of applications; efficient deployment of Artificial Intelligence (AI) workflows on highly heterogeneous infrastructures; and reproducible analysis of experiments on the Computing Continuum. • After screening 1159 articles from 5 databases (2016-2021), we summarized 69 studies. • A taxonomy of Data Analytics and AI on the Edge-to-Cloud Computing Continuum. • Most exploited: frameworks/libs; hardware; metrics; models; dataset; AI paradigms. • Analysis of articles in terms of experimental research and reproducibility support. • Discussion of the relevant open challenges and future directions in 4 domains. [ABSTRACT FROM AUTHOR]

Byline: Florin Pop, Ciprian Dobre, Corina Stratan, Alexandru Costan, Valentin Cristea The scheduling process in large scale distributed systems (LSDS) became more important due to increases in the number of users and applications. This paper presents a dynamic meta-scheduling architecture model for LSDS based on monitoring. The dynamic scheduling process tries to perform task allocation on the fly as the application executes. The monitoring has an important role in this process because it can offer a full view of nodes in distributed systems. The proposed architecture is an agent framework and contains a grid monitoring service, an execution service and a discovery service. The performance of the used monitoring system, MonALISA, is very important for dynamic scheduling because it ensures the real-time process. The experimental results validate our architecture and scheduling model.

Large-scale, data-intensive scientific applications are often expressed as scientific workflows (SWfs). In this paper, we consider the problem of efficient scheduling of a large SWf in a multisite cloud, i.e., a cloud with geo-distributed cloud data centers (sites). The reasons for using multiple cloud sites to run a SWf are that data is already distributed, the necessary resources exceed the limits at a single site, or the monetary cost is lower. In a multisite cloud, metadata management has a critical impact on the efficiency of SWf scheduling as it provides a global view of data location and enables task tracking during execution. Thus, it should be readily available to the system at any given time. While it has been shown that efficient metadata handling plays a key role in performance, little research has targeted this issue in multisite cloud. In this paper, we propose to identify and exploit hot metadata (frequently accessed metadata) for efficient SWf scheduling in a multisite cloud, using a distributed approach. We implemented our approach within a scientific workflow management system, which shows that our approach reduces the execution time of highly parallel jobs up to 64 percent and that of the whole SWfs up to 55 percent. [ABSTRACT FROM AUTHOR]

The emergence of cloud computing has brought the opportunity to use large-scale compute infrastructures for a broader and broader spectrum of applications and users. As the cloud paradigm gets attractive for the 'elasticity' in resource usage and associated costs (the users only pay for resources actually used), cloud applications still suffer from the high latencies and low performance of cloud storage services. As Big Data analysis on clouds becomes more and more relevant in many application areas, enabling high-throughput massive data processing on cloud data becomes a critical issue, as it impacts the overall application performance. In this paper, we address this challenge at the level of cloud storage. We introduce a concurrency-optimized data storage system (called TomusBlobs), which federates the virtual disks associated to the Virtual Machines running the application code on the cloud. We demonstrate the performance benefits of our solution for efficient data-intensive processing by building an optimized prototype MapReduce framework for Microsoft's Azure cloud platform on the basis of TomusBlobs. Finally, we specifically address the limitations of state-of-the-art MapReduce frameworks for reduce-intensive workloads, by proposing MapIterativeReduce as an extension of the MapReduce model. We validate the aforementioned contributions through large-scale experiments with synthetic benchmarks and with real-world applications on the Azure commercial cloud by using resources distributed across multiple data centers; they demonstrate that our solutions bring substantial benefits to data-intensive applications compared with approaches relying on state-of-the-art cloud object storage. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Brain imaging is a natural intermediate phenotype to understand the link between genetic information and behavior or brain pathologies risk factors. Massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such data is carried out with increasingly sophisticated techniques and represents a great computational challenge. Fortunately, increasing computational power in distributed architectures can be harnessed, if new neuroinformatics infrastructures are designed and training to use these new tools is provided. Combining a MapReduce framework (TomusBLOB) with machine learning algorithms (Scikit-learn library), we design a scalable analysis tool that can deal with non-parametric statistics on high-dimensional data. End-users describe the statistical procedure to perform and can then test the model on their own computers before running the very same code in the cloud at a larger scale. We illustrate the potential of our approach on real data with an experiment showing how the functional signal in subcortical brain regions can be significantly fit with genome-wide genotypes. This experiment demonstrates the scalability and the reliability of our framework in the cloud with a 2 weeks deployment on hundreds of virtual machines. [ABSTRACT FROM AUTHOR]