Impliance: A Next Generation Information Management Appliance

ably successful in building a large market and adapting to the changes of the last three decades, its impact on the broader market of information management is surprisingly limited. If we were to design an information management system from scratch, based upon today's requirements and hardware capabilities, would it look anything like today's database systems?" In this paper, we introduce Impliance, a next-generation information management system consisting of hardware and software components integrated to form an easy-to-administer appliance that can store, retrieve, and analyze all types of structured, semi-structured, and unstructured information. We first summarize the trends that will shape information management for the foreseeable future. Those trends imply three major requirements for Impliance: (1) to be able to store, manage, and uniformly query all data, not just structured records; (2) to be able to scale out as the volume of this data grows; and (3) to be simple and robust in operation. We then describe four key ideas that are uniquely combined in Impliance to address these requirements, namely the ideas of: (a) integrating software and off-the-shelf hardware into a generic information appliance; (b) automatically discovering, organizing, and managing all data - unstructured as well as structured - in a uniform way; (c) achieving scale-out by exploiting simple, massive parallel processing, and (d) virtualizing compute and storage resources to unify, simplify, and streamline the management of Impliance. Impliance is an ambitious, long-term effort to define simpler, more robust, and more scalable information systems for tomorrow's enterprises.Comment: This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but, you must attribute the work to the author and CIDR 2007. 3rd Biennial Conference on Innovative Data Systems Research (CIDR) January 710, 2007, Asilomar, California, US

MAPREDUCE CHALLENGES ON PERVASIVE GRIDS

International audienceThis study presents the advances on designing and implementing scalable techniques to support the development and execution of MapReduce application in pervasive distributed computing infrastructures, in the context of the PER-MARE project. A pervasive framework for MapReduce applications is very useful in practice, especially in those scientific, enterprises and educational centers which have many unused or underused computing resources, which can be fully exploited to solve relevant problems that demand large computing power, such as scientific computing applications, big data processing, etc. In this study, we pro-pose the study of multiple techniques to support volatility and heterogeneity on MapReduce, by applying two complementary approaches: Improving the Apache Hadoop middleware by including context-awareness and fault-tolerance features; and providing an alternative pervasive grid implementation, fully adapted to dynamic environments. The main design and implementation decisions for both alternatives are described and validated through experiments, demonstrating that our approaches provide high reliability when executing on pervasive environments. The analysis of the experiments also leads to several insights on the requirements and constraints from dynamic and volatile systems, reinforcing the importance of context-aware information and advanced fault-tolerance features to provide efficient and reliable MapReduce services on pervasive grids

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

Arquitectura, técnicas y modelos para posibilitar la Ciencia de Datos en el Archivo de la Misión Gaia

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 26/05/2017.The massive amounts of data that the world produces every day pose new challenges to modern societies in terms of how to leverage their inherent value. Social networks, instant messaging, video, smart devices and scientific missions are just mere examples of the vast number of sources generating data every second. As the world becomes more and more digitalized, new needs arise for organizing, archiving, sharing, analyzing, visualizing and protecting the ever-increasing data sets, so that we can truly develop into a data-driven economy that reduces inefficiencies and increases sustainability, creating new business opportunities on the way. Traditional approaches for harnessing data are not suitable any more as they lack the means for scaling to the larger volumes in a timely and cost efficient manner. This has somehow changed with the advent of Internet companies like Google and Facebook, which have devised new ways of tackling this issue. However, the variety and complexity of the value chains in the private sector as well as the increasing demands and constraints in which the public one operates, needs an ongoing research that can yield newer strategies for dealing with data, facilitate the integration of providers and consumers of information, and guarantee a smooth and prompt transition when adopting these cutting-edge technological advances. This thesis aims at providing novel architectures and techniques that will help perform this transition towards Big Data in massive scientific archives. It highlights the common pitfalls that must be faced when embracing it and how to overcome them, especially when the data sets, their transformation pipelines and the tools used for the analysis are already present in the organizations. Furthermore, a new perspective for facilitating a smoother transition is laid out. It involves the usage of higher-level and use case specific frameworks and models, which will naturally bridge the gap between the technological and scientific domains. This alternative will effectively widen the possibilities of scientific archives and therefore will contribute to the reduction of the time to science. The research will be applied to the European Space Agency cornerstone mission Gaia, whose final data archive will represent a tremendous discovery potential. It will create the largest and most precise three dimensional chart of our galaxy (the Milky Way), providing unprecedented position, parallax and proper motion measurements for about one billion stars. The successful exploitation of this data archive will depend to a large degree on the ability to offer the proper architecture, i.e. infrastructure and middleware, upon which scientists will be able to do exploration and modeling with this huge data set. In consequence, the approach taken needs to enable data fusion with other scientific archives, as this will produce the synergies leading to an increment in scientific outcome, both in volume and in quality. The set of novel techniques and frameworks presented in this work addresses these issues by contextualizing them with the data products that will be generated in the Gaia mission. All these considerations have led to the foundations of the architecture that will be leveraged by the Science Enabling Applications Work Package. Last but not least, the effectiveness of the proposed solution will be demonstrated through the implementation of some ambitious statistical problems that will require significant computational capabilities, and which will use Gaia-like simulated data (the first Gaia data release has recently taken place on September 14th, 2016). These ambitious problems will be referred to as the Grand Challenge, a somewhat grandiloquent name that consists in inferring a set of parameters from a probabilistic point of view for the Initial Mass Function (IMF) and Star Formation Rate (SFR) of a given set of stars (with a huge sample size), from noisy estimates of their masses and ages respectively. This will be achieved by using Hierarchical Bayesian Modeling (HBM). In principle, the HBM can incorporate stellar evolution models to infer the IMF and SFR directly, but in this first step presented in this thesis, we will start with a somewhat less ambitious goal: inferring the PDMF and PDAD. Moreover, the performance and scalability analyses carried out will also prove the suitability of the models for the large amounts of data that will be available in the Gaia data archive.Las grandes cantidades de datos que se producen en el mundo diariamente plantean nuevos retos a la sociedad en términos de cómo extraer su valor inherente. Las redes sociales, mensajería instantánea, los dispositivos inteligentes y las misiones científicas son meros ejemplos del gran número de fuentes generando datos en cada momento. Al mismo tiempo que el mundo se digitaliza cada vez más, aparecen nuevas necesidades para organizar, archivar, compartir, analizar, visualizar y proteger la creciente cantidad de datos, para que podamos desarrollar economías basadas en datos e información que sean capaces de reducir las ineficiencias e incrementar la sostenibilidad, creando nuevas oportunidades de negocio por el camino. La forma en la que se han manejado los datos tradicionalmente no es la adecuada hoy en día, ya que carece de los medios para escalar a los volúmenes más grandes de datos de una forma oportuna y eficiente. Esto ha cambiado de alguna manera con la llegada de compañías que operan en Internet como Google o Facebook, ya que han concebido nuevas aproximaciones para abordar el problema. Sin embargo, la variedad y complejidad de las cadenas de valor en el sector privado y las crecientes demandas y limitaciones en las que el sector público opera, necesitan una investigación continua en la materia que pueda proporcionar nuevas estrategias para procesar las enormes cantidades de datos, facilitar la integración de productores y consumidores de información, y garantizar una transición rápida y fluida a la hora de adoptar estos avances tecnológicos innovadores. Esta tesis tiene como objetivo proporcionar nuevas arquitecturas y técnicas que ayudarán a realizar esta transición hacia Big Data en archivos científicos masivos. La investigación destaca los escollos principales a encarar cuando se adoptan estas nuevas tecnologías y cómo afrontarlos, principalmente cuando los datos y las herramientas de transformación utilizadas en el análisis existen en la organización. Además, se exponen nuevas medidas para facilitar una transición más fluida. Éstas incluyen la utilización de software de alto nivel y específico al caso de uso en cuestión, que haga de puente entre el dominio científico y tecnológico. Esta alternativa ampliará de una forma efectiva las posibilidades de los archivos científicos y por tanto contribuirá a la reducción del tiempo necesario para generar resultados científicos a partir de los datos recogidos en las misiones de astronomía espacial y planetaria. La investigación se aplicará a la misión de la Agencia Espacial Europea (ESA) Gaia, cuyo archivo final de datos presentará un gran potencial para el descubrimiento y hallazgo desde el punto de vista científico. La misión creará el catálogo en tres dimensiones más grande y preciso de nuestra galaxia (la Vía Láctea), proporcionando medidas sin precedente acerca del posicionamiento, paralaje y movimiento propio de alrededor de mil millones de estrellas. Las oportunidades para la explotación exitosa de este archivo de datos dependerán en gran medida de la capacidad de ofrecer la arquitectura adecuada, es decir infraestructura y servicios, sobre la cual los científicos puedan realizar la exploración y modelado con esta inmensa cantidad de datos. Por tanto, la estrategia a realizar debe ser capaz de combinar los datos con otros archivos científicos, ya que esto producirá sinergias que contribuirán a un incremento en la ciencia producida, tanto en volumen como en calidad de la misma. El conjunto de técnicas e infraestructuras innovadoras presentadas en este trabajo aborda estos problemas, contextualizándolos con los productos de datos que se generarán en la misión Gaia. Todas estas consideraciones han conducido a los fundamentos de la arquitectura que se utilizará en el paquete de trabajo de aplicaciones que posibilitarán la ciencia en el archivo de la misión Gaia (Science Enabling Applications). Por último, la eficacia de la solución propuesta se demostrará a través de la implementación de dos problemas estadísticos que requerirán cantidades significativas de cómputo, y que usarán datos simulados en el mismo formato en el que se producirán en el archivo de la misión Gaia (la primera versión de datos recogidos por la misión está disponible desde el día 14 de Septiembre de 2016). Estos ambiciosos problemas representan el Gran Reto (Grand Challenge), un nombre grandilocuente que consiste en inferir una serie de parámetros desde un punto de vista probabilístico para la función de masa inicial (Initial Mass Function) y la tasa de formación estelar (Star Formation Rate) dado un conjunto de estrellas (con una muestra grande), desde estimaciones con ruido de sus masas y edades respectivamente. Esto se abordará utilizando modelos jerárquicos bayesianos (Hierarchical Bayesian Modeling). Enprincipio,losmodelospropuestos pueden incorporar otros modelos de evolución estelar para inferir directamente la función de masa inicial y la tasa de formación estelar, pero en este primer paso presentado en esta tesis, empezaremos con un objetivo algo menos ambicioso: la inferencia de la función de masa y distribución de edades actual (Present-Day Mass Function y Present-Day Age Distribution respectivamente). Además, se llevará a cabo el análisis de rendimiento y escalabilidad para probar la idoneidad de la implementación de dichos modelos dadas las enormes cantidades de datos que estarán disponibles en el archivo de la misión Gaia...Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Proceedings of the Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015) Krakow, Poland

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015

Computation Against a Neighbour

Recent works in contexts like the Internet of Things (IoT) and large-scale Cyber-Physical Systems (CPS) propose the idea of programming distributed systems by focussing on their global behaviour across space and time. In this view, a potentially vast and heterogeneous set of devices is considered as an "aggregate" to be programmed as a whole, while abstracting away the details of individual behaviour and exchange of messages, which are expressed declaratively. One such a paradigm, known as aggregate programming, builds on computational models inspired by field-based coordination. Existing models such as the field calculus capture interaction with neighbours by a so-called "neighbouring field" (a map from neighbours to values). This requires ad-hoc mechanisms to smoothly compose with standard values, thus complicating programming and introducing clutter in aggregate programs, libraries and domain-specific languages (DSLs). To address this key issue we introduce the novel notion of "computation against a neighbour", whereby the evaluation of certain subexpressions of the aggregate program are affected by recent corresponding evaluations in neighbours. We capture this notion in the neighbours calculus (NC), a new field calculus variant which is shown to smoothly support declarative specification of interaction with neighbours, and correspondingly facilitate the embedding of field computations as internal DSLs in common general-purpose programming languages -- as exemplified by a Scala implementation, called ScaFi. This paper formalises NC, thoroughly compares it with respect to the classic field calculus, and shows its expressiveness by means of a case study in edge computing, developed in ScaFi.Comment: 50 pages, 16 figure

Data-Driven Methods for Data Center Operations Support

During the last decade, cloud technologies have been evolving at an impressive pace, such that we are now living in a cloud-native era where developers can leverage on an unprecedented landscape of (possibly managed) services for orchestration, compute, storage, load-balancing, monitoring, etc. The possibility to have on-demand access to a diverse set of configurable virtualized resources allows for building more elastic, flexible and highly-resilient distributed applications. Behind the scenes, cloud providers sustain the heavy burden of maintaining the underlying infrastructures, consisting in large-scale distributed systems, partitioned and replicated among many geographically dislocated data centers to guarantee scalability, robustness to failures, high availability and low latency. The larger the scale, the more cloud providers have to deal with complex interactions among the various components, such that monitoring, diagnosing and troubleshooting issues become incredibly daunting tasks. To keep up with these challenges, development and operations practices have undergone significant transformations, especially in terms of improving the automations that make releasing new software, and responding to unforeseen issues, faster and sustainable at scale. The resulting paradigm is nowadays referred to as DevOps. However, while such automations can be very sophisticated, traditional DevOps practices fundamentally rely on reactive mechanisms, that typically require careful manual tuning and supervision from human experts. To minimize the risk of outages—and the related costs—it is crucial to provide DevOps teams with suitable tools that can enable a proactive approach to data center operations. This work presents a comprehensive data-driven framework to address the most relevant problems that can be experienced in large-scale distributed cloud infrastructures. These environments are indeed characterized by a very large availability of diverse data, collected at each level of the stack, such as: time-series (e.g., physical host measurements, virtual machine or container metrics, networking components logs, application KPIs); graphs (e.g., network topologies, fault graphs reporting dependencies among hardware and software components, performance issues propagation networks); and text (e.g., source code, system logs, version control system history, code review feedbacks). Such data are also typically updated with relatively high frequency, and subject to distribution drifts caused by continuous configuration changes to the underlying infrastructure. In such a highly dynamic scenario, traditional model-driven approaches alone may be inadequate at capturing the complexity of the interactions among system components. DevOps teams would certainly benefit from having robust data-driven methods to support their decisions based on historical information. For instance, effective anomaly detection capabilities may also help in conducting more precise and efficient root-cause analysis. Also, leveraging on accurate forecasting and intelligent control strategies would improve resource management. Given their ability to deal with high-dimensional, complex data, Deep Learning-based methods are the most straightforward option for the realization of the aforementioned support tools. On the other hand, because of their complexity, this kind of models often requires huge processing power, and suitable hardware, to be operated effectively at scale. These aspects must be carefully addressed when applying such methods in the context of data center operations. Automated operations approaches must be dependable and cost-efficient, not to degrade the services they are built to improve. i

3rd Many-core Applications Research Community (MARC) Symposium. (KIT Scientific Reports ; 7598)

This manuscript includes recent scientific work regarding the Intel Single Chip Cloud computer and describes approaches for novel approaches for programming and run-time organization