Database integrated analytics using R : initial experiences with SQL-Server + R

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Most data scientists use nowadays functional or semi-functional languages like SQL, Scala or R to treat data, obtained directly from databases. Such process requires to fetch data, process it, then store again, and such process tends to be done outside the DB, in often complex data-flows. Recently, database service providers have decided to integrate “R-as-a-Service” in their DB solutions. The analytics engine is called directly from the SQL query tree, and results are returned as part of the same query. Here we show a first taste of such technology by testing the portability of our ALOJA-ML analytics framework, coded in R, to Microsoft SQL-Server 2016, one of the SQL+R solutions released recently. In this work we discuss some data-flow schemes for porting a local DB + analytics engine architecture towards Big Data, focusing specially on the new DB Integrated Analytics approach, and commenting the first experiences in usability and performance obtained from such new services and capabilities.Peer ReviewedPostprint (author's final draft

SoC-Based In-Storage Processing: Bringing Flexibility and Efficiency to Near-Data Processing

Data are among the most valuable assets in the modern world, and they have caused a revolutionary stage in human life. Nowadays, companies make knowledge-based decisions by analyzing a huge volume of data, super-scale data centers are used to process customers’ data to suggest products to them, government services rely on the data people provide to them, and there are many similar cases wherein data are used as an important asset. Data are originally stored in storage systems. To process data, application servers need to fetch the data from storage units, which imposes the cost of moving the data to the system. This cost has a direct relationship to the distance of the processing engines from the data, and this is the key motivation for the emergence of distributed processing platforms such as Hadoop, which bring the process closer to the data.In-storage processing (ISP) pushes the “bring the process to data” paradigm to its ultimate boundaries by utilizing processing engines inside the storage units to process data. The architecture of modern solid-state drives (SSDs) provides a suitable environment for implementing such technology. Thus, this dissertation focuses on SSD architectures that are able to run user applications in-place, which are called computational storage devices (CSDs). In this dissertation, we propose CSD architectures and investigate the benefits of deploying CSDs for running different applications. This research uses a practical approach that includes building fully functional prototypes of the proposed CSD architectures, developing storage systems equipped with the CSDs, and running different benchmarks to investigate the benefits of deploying the CSDs in the systems. This research proposes two different CSD architectures, namely CompStor and Catalina.These are the first CSDs to be equipped with a dedicated ISP engine for running user applications in-place that includes a quad-core ARM Cortex-A53 processor together with FPGA- and application-specific integrated circuit (ASIC) based accelerators. The proposed architectures run a full-fledged operating system inside, which provides a flexible environment for running a wide range of user applications in-place. The system-on-chip (SOC) based architecture of Catalina CSD, together with a software stack developed for seamless deployment of the CSD, makes it a platform for the implementation of different ISP concepts and ideas.To the best of our knowledge, Catalina is the only ISP platform that can be seamlessly deployed in clusters to run distributed applications such as Hadoop MapReduce and message passing interface (MPI) based applications in-place without any modifications to the underlying distributed processing framework. We performed extensive experimental tests using several datasets on both CompStor and Catalina CSDs. The experimental results show up to 2.2x and 4.3x improvements in performance and energy consumption, respectively, for running Hadoop MapReduce benchmarks using Catalina CSDs and up to 5.4x and 8.9x improvements for running 1-, 2-, and 3-dimensional DFT algorithms due to the Neon SIMD engines inside Catalina. Additionally, using FPGA-based accelerators, Catalina CSDs can improve the performance and energy consumption of a highly demanding image similarity search application up to 11x and 7x, respectively

Scientific Computing Meets Big Data Technology: An Astronomy Use Case

Scientific analyses commonly compose multiple single-process programs into a dataflow. An end-to-end dataflow of single-process programs is known as a many-task application. Typically, tools from the HPC software stack are used to parallelize these analyses. In this work, we investigate an alternate approach that uses Apache Spark -- a modern big data platform -- to parallelize many-task applications. We present Kira, a flexible and distributed astronomy image processing toolkit using Apache Spark. We then use the Kira toolkit to implement a Source Extractor application for astronomy images, called Kira SE. With Kira SE as the use case, we study the programming flexibility, dataflow richness, scheduling capacity and performance of Apache Spark running on the EC2 cloud. By exploiting data locality, Kira SE achieves a 2.5x speedup over an equivalent C program when analyzing a 1TB dataset using 512 cores on the Amazon EC2 cloud. Furthermore, we show that by leveraging software originally designed for big data infrastructure, Kira SE achieves competitive performance to the C implementation running on the NERSC Edison supercomputer. Our experience with Kira indicates that emerging Big Data platforms such as Apache Spark are a performant alternative for many-task scientific applications

Big Data-Oriented PaaS Architecture with Disk-as-a-Resource Capability and Container-Based Virtualization

This is a post-peer-review, pre-copyedit version of an article published in Journal of Grid Computing. The final authenticated version is available online at: https://doi.org/10.1007/s10723-018-9460-4[Abstract] With the increasing adoption of Big Data technologies as basic tools for the ongoing Digital Transformation, there is a high demand for data-intensive applications. In order to efficiently execute such applications, it is vital that cloud providers change the way hardware infrastructure resources are managed to improve their performance. However, the increasing use of virtualization technologies to achieve an efficient usage of infrastructure resources continuously widens the gap between applications and the underlying hardware, thus decreasing resource efficiency for the end user. Moreover, this scenario is especially troublesome for Big Data applications, as storage resources are one of the most heavily virtualized, thus imposing a significant overhead for large-scale data processing. This paper proposes a novel PaaS architecture specifically oriented for Big Data where the scheduler offers disks as resources alongside the more common CPU and memory resources, looking forward to provide a better storage solution for the user. Furthermore, virtualization overheads are reduced to the bare minimum by replacing heavy hypervisor-based technologies with operating-system-level virtualization based on light software containers. This architecture has been deployed on a Big Data infrastructure at the CESGA supercomputing center, used as a testbed to compare its performance with OpenStack, a popular private cloud platform. Results have shown significant performance improvements, reducing the execution time of representative Big Data workloads by up to 4.5×.Ministerio de Economía, Industria y Competitividad; TIN2016-75845-P, AEI/FEDER, EUMinisterio de Educación; FPU15/0338

Big Data Analytics on Traditional HPC Infrastructure Using Two-Level Storage

Data-intensive computing has become one of the major workloads on traditional high-performance computing (HPC) clusters. Currently, deploying data-intensive computing software framework on HPC clusters still faces performance and scalability issues. In this paper, we develop a new two-level storage system by integrating Tachyon, an in-memory file system with OrangeFS, a parallel file system. We model the I/O throughputs of four storage structures: HDFS, OrangeFS, Tachyon and two-level storage. We conduct computational experiments to characterize I/O throughput behavior of two-level storage and compare its performance to that of HDFS and OrangeFS, using TeraSort benchmark. Theoretical models and experimental tests both show that the two-level storage system can increase the aggregate I/O throughputs. This work lays a solid foundation for future work in designing and building HPC systems that can provide a better support on I/O intensive workloads with preserving existing computing resources.Comment: Submitted to SC15, 8 pages, 7 figures, 3 table

Contribution à la convergence d'infrastructure entre le calcul haute performance et le traitement de données à large échelle

The amount of produced data, either in the scientific community or the commercialworld, is constantly growing. The field of Big Data has emerged to handle largeamounts of data on distributed computing infrastructures. High-Performance Computing (HPC) infrastructures are traditionally used for the execution of computeintensive workloads. However, the HPC community is also facing an increasingneed to process large amounts of data derived from high definition sensors andlarge physics apparati. The convergence of the two fields -HPC and Big Data- iscurrently taking place. In fact, the HPC community already uses Big Data tools,which are not always integrated correctly, especially at the level of the file systemand the Resource and Job Management System (RJMS).In order to understand how we can leverage HPC clusters for Big Data usage, andwhat are the challenges for the HPC infrastructures, we have studied multipleaspects of the convergence: We initially provide a survey on the software provisioning methods, with a focus on data-intensive applications. We contribute a newRJMS collaboration technique called BeBiDa which is based on 50 lines of codewhereas similar solutions use at least 1000 times more. We evaluate this mechanism on real conditions and in simulated environment with our simulator Batsim.Furthermore, we provide extensions to Batsim to support I/O, and showcase thedevelopments of a generic file system model along with a Big Data applicationmodel. This allows us to complement BeBiDa real conditions experiments withsimulations while enabling us to study file system dimensioning and trade-offs.All the experiments and analysis of this work have been done with reproducibilityin mind. Based on this experience, we propose to integrate the developmentworkflow and data analysis in the reproducibility mindset, and give feedback onour experiences with a list of best practices.RésuméLa quantité de données produites, que ce soit dans la communauté scientifiqueou commerciale, est en croissance constante. Le domaine du Big Data a émergéface au traitement de grandes quantités de données sur les infrastructures informatiques distribuées. Les infrastructures de calcul haute performance (HPC) sont traditionnellement utilisées pour l’exécution de charges de travail intensives en calcul. Cependant, la communauté HPC fait également face à un nombre croissant debesoin de traitement de grandes quantités de données dérivées de capteurs hautedéfinition et de grands appareils physique. La convergence des deux domaines-HPC et Big Data- est en cours. En fait, la communauté HPC utilise déjà des outilsBig Data, qui ne sont pas toujours correctement intégrés, en particulier au niveaudu système de fichiers ainsi que du système de gestion des ressources (RJMS).Afin de comprendre comment nous pouvons tirer parti des clusters HPC pourl’utilisation du Big Data, et quels sont les défis pour les infrastructures HPC, nousavons étudié plusieurs aspects de la convergence: nous avons d’abord proposé uneétude sur les méthodes de provisionnement logiciel, en mettant l’accent sur lesapplications utilisant beaucoup de données. Nous contribuons a l’état de l’art avecune nouvelle technique de collaboration entre RJMS appelée BeBiDa basée sur 50lignes de code alors que des solutions similaires en utilisent au moins 1000 fois plus.Nous évaluons ce mécanisme en conditions réelles et en environnement simuléavec notre simulateur Batsim. En outre, nous fournissons des extensions à Batsimpour prendre en charge les entrées/sorties et présentons le développements d’unmodèle de système de fichiers générique accompagné d’un modèle d’applicationBig Data. Cela nous permet de compléter les expériences en conditions réellesde BeBiDa en simulation tout en étudiant le dimensionnement et les différentscompromis autours des systèmes de fichiers.Toutes les expériences et analyses de ce travail ont été effectuées avec la reproductibilité à l’esprit. Sur la base de cette expérience, nous proposons d’intégrerle flux de travail du développement et de l’analyse des données dans l’esprit dela reproductibilité, et de donner un retour sur nos expériences avec une liste debonnes pratiques

ALOJA: A framework for benchmarking and predictive analytics in Hadoop deployments

This article presents the ALOJA project and its analytics tools, which leverages machine learning to interpret Big Data benchmark performance data and tuning. ALOJA is part of a long-term collaboration between BSC and Microsoft to automate the characterization of cost-effectiveness on Big Data deployments, currently focusing on Hadoop. Hadoop presents a complex run-time environment, where costs and performance depend on a large number of configuration choices. The ALOJA project has created an open, vendor-neutral repository, featuring over 40,000 Hadoop job executions and their performance details. The repository is accompanied by a test-bed and tools to deploy and evaluate the cost-effectiveness of different hardware configurations, parameters and Cloud services. Despite early success within ALOJA, a comprehensive study requires automation of modeling procedures to allow an analysis of large and resource-constrained search spaces. The predictive analytics extension, ALOJA-ML, provides an automated system allowing knowledge discovery by modeling environments from observed executions. The resulting models can forecast execution behaviors, predicting execution times for new configurations and hardware choices. That also enables model-based anomaly detection or efficient benchmark guidance by prioritizing executions. In addition, the community can benefit from ALOJA data-sets and framework to improve the design and deployment of Big Data applications.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). This work is partially supported by the Ministry of Economy of Spain under contracts TIN2012-34557 and 2014SGR1051.Peer ReviewedPostprint (published version

Big Data and Large-scale Data Analytics: Efficiency of Sustainable Scalability and Security of Centralized Clouds and Edge Deployment Architectures

One of the significant shifts of the next-generation computing technologies will certainly be in the development of Big Data (BD) deployment architectures. Apache Hadoop, the BD landmark, evolved as a widely deployed BD operating system. Its new features include federation structure and many associated frameworks, which provide Hadoop 3.x with the maturity to serve different markets. This dissertation addresses two leading issues involved in exploiting BD and large-scale data analytics realm using the Hadoop platform. Namely, (i)Scalability that directly affects the system performance and overall throughput using portable Docker containers. (ii) Security that spread the adoption of data protection practices among practitioners using access controls. An Enhanced Mapreduce Environment (EME), OPportunistic and Elastic Resource Allocation (OPERA) scheduler, BD Federation Access Broker (BDFAB), and a Secure Intelligent Transportation System (SITS) of multi-tiers architecture for data streaming to the cloud computing are the main contribution of this thesis study

Performance Evaluation of LINQ to HPC and Hadoop for Big Data

There is currently considerable enthusiasm around the MapReduce paradigm, and the distributed computing paradigm for analysis of large volumes of data. The Apache Hadoop is the most popular open source implementation of MapReduce model and LINQ to HPC is Microsoft\u27s alternative to open source Hadoop. In this thesis, the performance of LINQ to HPC and Hadoop are compared using different benchmarks. To this end, we identified four benchmarks (Grep, Word Count, Read and Write) that we have run on LINQ to HPC as well as on Hadoop. For each benchmark, we measured each system’s performance metrics (Execution Time, Average CPU utilization and Average Memory utilization) for various degrees of parallelism on clusters of different sizes. Results revealed some interesting trade-offs. For example, LINQ to HPC performed better on three out of the four benchmarks (Grep, Read and Write), whereas Hadoop performed better on the Word Count benchmark. While more research that is extensive has focused on Hadoop, there are not many references to similar research on the LINQ to HPC platform, which is slowly evolving during the writing of this thesis