Introducing the IBM Linuxone™ Family
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Scalable Cloud Computing
Scalable Cloud Computing Keijo Heljanko Department of Computer Science and Engineering School of Science Aalto University [email protected] 2.10-2013 Mobile Cloud Computing - Keijo Heljanko (keijo.heljanko@aalto.fi) 1/57 Guest Lecturer I Guest Lecturer: Assoc. Prof. Keijo Heljanko, Department of Computer Science and Engineering, Aalto University, I Email: [email protected] I Homepage: https://people.aalto.fi/keijo_heljanko I For more info into today’s topic, attend the course: “T-79.5308 Scalable Cloud Computing” Mobile Cloud Computing - Keijo Heljanko (keijo.heljanko@aalto.fi) 2/57 Business Drivers of Cloud Computing I Large data centers allow for economics of scale I Cheaper hardware purchases I Cheaper cooling of hardware I Example: Google paid 40 MEur for a Summa paper mill site in Hamina, Finland: Data center cooled with sea water from the Baltic Sea I Cheaper electricity I Cheaper network capacity I Smaller number of administrators / computer I Unreliable commodity hardware is used I Reliability obtained by replication of hardware components and a combined with a fault tolerant software stack Mobile Cloud Computing - Keijo Heljanko (keijo.heljanko@aalto.fi) 3/57 Cloud Computing Technologies A collection of technologies aimed to provide elastic “pay as you go” computing I Virtualization of computing resources: Amazon EC2, Eucalyptus, OpenNebula, Open Stack Compute, . I Scalable file storage: Amazon S3, GFS, HDFS, . I Scalable batch processing: Google MapReduce, Apache Hadoop, PACT, Microsoft Dryad, Google Pregel, Spark, ::: I Scalable datastore: Amazon Dynamo, Apache Cassandra, Google Bigtable, HBase,. I Distributed Coordination: Google Chubby, Apache Zookeeper, . I Scalable Web applications hosting: Google App Engine, Microsoft Azure, Heroku, . -
2016 8Th International Conference on Cyber Conflict: Cyber Power
2016 8th International Conference on Cyber Conflict: Cyber Power N.Pissanidis, H.Rõigas, M.Veenendaal (Eds.) 31 MAY - 03 JUNE 2016, TALLINN, ESTONIA 2016 8TH International ConFerence on CYBER ConFlict: CYBER POWER Copyright © 2016 by NATO CCD COE Publications. All rights reserved. IEEE Catalog Number: CFP1626N-PRT ISBN (print): 978-9949-9544-8-3 ISBN (pdf): 978-9949-9544-9-0 CopyriGHT AND Reprint Permissions No part of this publication may be reprinted, reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior written permission of the NATO Cooperative Cyber Defence Centre of Excellence ([email protected]). This restriction does not apply to making digital or hard copies of this publication for internal use within NATO, and for personal or educational use when for non-profit or non-commercial purposes, providing that copies bear this notice and a full citation on the first page as follows: [Article author(s)], [full article title] 2016 8th International Conference on Cyber Conflict: Cyber Power N.Pissanidis, H.Rõigas, M.Veenendaal (Eds.) 2016 © NATO CCD COE Publications PrinteD copies OF THIS PUBlication are availaBLE From: NATO CCD COE Publications Filtri tee 12, 10132 Tallinn, Estonia Phone: +372 717 6800 Fax: +372 717 6308 E-mail: [email protected] Web: www.ccdcoe.org Head of publishing: Jaanika Rannu Layout: Jaakko Matsalu LEGAL NOTICE: This publication contains opinions of the respective authors only. They do not necessarily reflect the policy or the opinion of NATO CCD COE, NATO, or any agency or any government. -
Apache Sentry
Apache Sentry Prasad Mujumdar [email protected] [email protected] Agenda ● Various aspects of data security ● Apache Sentry for authorization ● Key concepts of Apache Sentry ● Sentry features ● Sentry architecture ● Integration with Hadoop ecosystem ● Sentry administration ● Future plans ● Demo ● Questions Who am I • Software engineer at Cloudera • Committer and PPMC member of Apache Sentry • also for Apache Hive and Apache Flume • Part of the the original team that started Sentry work Aspects of security Perimeter Access Visibility Data Authentication Authorization Audit, Lineage Encryption, what user can do data origin, usage Kerberos, LDAP/AD Masking with data Data access Access ● Provide user access to data Authorization ● Manage access policies what user can do ● Provide role based access with data Agenda ● Various aspects of data security ● Apache Sentry for authorization ● Key concepts of Apache Sentry ● Sentry features ● Sentry architecture ● Integration with Hadoop ecosystem ● Sentry administration ● Future plans ● Demo ● Questions Apache Sentry (Incubating) Unified Authorization module for Hadoop Unlocks Key RBAC Requirements Secure, fine-grained, role-based authorization Multi-tenant administration Enforce a common set of policies across multiple data access path in Hadoop. Key Capabilities of Sentry Fine-Grained Authorization Permissions on object hierarchie. Eg, Database, Table, Columns Role-Based Authorization Support for role templetes to manage authorization for a large set of users and data objects Multi Tanent Administration -
Machine Learning and Deep Learning for IIOT
Machine Learning and Deep Learning for IIOT Chanchal Chatterjee, Dell EMC Reston, March 22 2016 1 Goals of the Meeting ➢ Provide insights on methods and systems for machine learning and deep learning. ➢ Provide machine/deep learning use cases for IIOT. ➢ Provide architectures and frameworks for machine/deep learning for IIOT. 2 Machine Learning & Deep Learning – Confusing, Eh! From Machine Learning Mastery (http://machinelearningmastery.com/) 3 Machine Learning and Deep Learning Dependencies • Types of Data • Types of Learning • Types of Algorithms 4 Types of Data • Structured Data • Time Series • Events • Graph • Unstructured Data • Video/Images • Voice • Text 5 Types of Learning • Un-Supervised • Do not require training data • Assume normal instances far more frequent than anomalies • Semi-Supervised • Training data has labeled instances for only the normal class • Assume normal instances far more frequent than anomalies • Supervised 6 Types of Algorithms • ML: Machine Learning • Anomaly Detection • Trends, Predictions & Forecasting • Association & Grouping • DL: Deep Learning • Ladder Network • Convolutional Neural Network • Recurrent Neural Network • Deep Belief Networks 7 Some Details 8 Machine Learning • Anomaly Detection • Point Anomaly • Contextual Anomaly • Collective Anomaly • Graph Anomaly • Trends, Predictions & Forecasting • Associations & Grouping 9 Deep Learning • Ladder Network • Convolutional NN (CNN) • Recurrent NN (RNN) • Recurrent Recursive NN (R2NN) • Long Short Term Memory (LSTM) • Deep Belief Networks (DBM) • Restricted -
Cómo Citar El Artículo Número Completo Más Información Del
DYNA ISSN: 0012-7353 Universidad Nacional de Colombia Iván-Herrera-Herrera, Nelson; Luján-Mora, Sergio; Gómez-Torres, Estevan Ricardo Integración de herramientas para la toma de decisiones en la congestión vehicular DYNA, vol. 85, núm. 205, 2018, Abril-Junio, pp. 363-370 Universidad Nacional de Colombia DOI: https://doi.org/10.15446/dyna.v85n205.67745 Disponible en: https://www.redalyc.org/articulo.oa?id=49657889045 Cómo citar el artículo Número completo Sistema de Información Científica Redalyc Más información del artículo Red de Revistas Científicas de América Latina y el Caribe, España y Portugal Página de la revista en redalyc.org Proyecto académico sin fines de lucro, desarrollado bajo la iniciativa de acceso abierto Integration of tools for decision making in vehicular congestion• Nelson Iván-Herrera-Herreraa, Sergio Luján-Morab & Estevan Ricardo Gómez-Torres a a Facultad de Ciencias de la Ingeniería e Industrias, Universidad Tecnológica Equinoccial, Quito, Ecuador. [email protected], [email protected] b Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante, Alicante, España. [email protected] Received: September 15th, 2017. Received in revised form: March 15th, 2018. Accepted: March 21th, 2018. Abstract The purpose of this study is to present an analysis of the use and integration of technological tools that help decision making in situations of vehicular congestion. The city of Quito-Ecuador is considered as a case study for the done work. The research is presented according to the development of an application, using Big Data tools (Apache Flume, Apache Hadoop, Apache Pig), favoring the processing of a lot of information that is required to collect, store and process. -
Zookeeper Administrator's Guide
ZooKeeper Administrator's Guide A Guide to Deployment and Administration by Table of contents 1 Deployment........................................................................................................................ 2 1.1 System Requirements....................................................................................................2 1.2 Clustered (Multi-Server) Setup.....................................................................................2 1.3 Single Server and Developer Setup..............................................................................4 2 Administration.................................................................................................................... 4 2.1 Designing a ZooKeeper Deployment........................................................................... 5 2.2 Provisioning.................................................................................................................. 6 2.3 Things to Consider: ZooKeeper Strengths and Limitations..........................................6 2.4 Administering................................................................................................................6 2.5 Maintenance.................................................................................................................. 6 2.6 Supervision....................................................................................................................7 2.7 Monitoring.....................................................................................................................8 -
Introducing Linux on IBM Z Systems IT Simplicity with an Enterprise Grade Linux Platform
Introducing Linux on IBM z Systems IT simplicity with an enterprise grade Linux platform Wilhelm Mild IBM Executive IT Architect for Mobile, z Systems and Linux © 2016 IBM Corporation IBM Germany What is Linux? . Linux is an operating system – Operating systems are tools which enable computers to function as multi-user, multitasking, and multiprocessing servers. – Linux is typically delivered in a Distribution with many useful tools and Open Source components. Linux is hardware agnostic by design – Linux runs on multiple hardware architectures which means Linux skills are platform independent. Linux is modular and built to coexist with other operating systems – Businesses are using Linux today. More and more businesses proceed with an evolutionary solution strategy based on Linux. 2 © 2016 IBM Corporation What is IBM z Systems ? . IBM z Systems is the family name used by IBM for its mainframe computers – The z Systems families were named for their availability – z stands for zero downtime. The systems are built with spare components capable of hot failovers to ensure continuous operations. IBM z Systems paradigm – The IBM z Systems family maintains full backward compatibility. In effect, current systems are the direct, lineal descendants of System/360, built in 1964, and the System/370 from the 1970s. Many applications written for these systems can still run unmodified on the newest z Systems over five decades later. IBM z Systems variety of Operating Systems – There are different traditional Operating Systems that run on z Systems like z/OS, z/VSE or TPF. With z/VM IBM delivers a mature Hypervisor to virtualize the operating systems. -
Online Banking Customer Awareness and Education Program
Online Banking Customer Awareness and Education Program Online Banking Customer Awareness and Education Program Rev. 6/21 Table of Contents Online Banking Customer Awareness and Education Program Overview ............................................................... 1 Unsolicited Contact .................................................................................................................................................................... 1 Luther Burbank Savings Contact Information ................................................................................................................... 1 Online Banking Security Certification .................................................................................................................................. 1 Email Security............................................................................................................................................................................... 2 Malicious Emails ........................................................................................................................................................................ 2 Email Attachments .................................................................................................................................................................... 2 Verify Emails .............................................................................................................................................................................. 2 Strong and Complex -
Talend Open Studio for Big Data Release Notes
Talend Open Studio for Big Data Release Notes 6.0.0 Talend Open Studio for Big Data Adapted for v6.0.0. Supersedes previous releases. Publication date July 2, 2015 Copyleft This documentation is provided under the terms of the Creative Commons Public License (CCPL). For more information about what you can and cannot do with this documentation in accordance with the CCPL, please read: http://creativecommons.org/licenses/by-nc-sa/2.0/ Notices Talend is a trademark of Talend, Inc. All brands, product names, company names, trademarks and service marks are the properties of their respective owners. License Agreement The software described in this documentation is licensed under the Apache License, Version 2.0 (the "License"); you may not use this software except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.html. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. This product includes software developed at AOP Alliance (Java/J2EE AOP standards), ASM, Amazon, AntlR, Apache ActiveMQ, Apache Ant, Apache Avro, Apache Axiom, Apache Axis, Apache Axis 2, Apache Batik, Apache CXF, Apache Cassandra, Apache Chemistry, Apache Common Http Client, Apache Common Http Core, Apache Commons, Apache Commons Bcel, Apache Commons JxPath, Apache -
List of Versions Added in ARL #2606
List of Versions added in ARL #2606 Publisher Product Version 3bitlab Red Hot Timer 1.9 3M Medicare Inpatient Pricer Tables Software Unspecified 3T Software Labs Studio 3T 2020.10 AB Sciex Analyst Unspecified Abacom LochMaster 4.0 ABB WinCCU Unspecified ABBYY FineReader PDF 9.0 ABBYY FineReader PDF Unspecified Absolute Software DDS Unspecified Access Control ACT Enterprise Client Unspecified Access Group Access Supply Chain Client 7.2 ActiveCrypt DbDefence 2020 Adobe Photoshop Camera Raw CS6 Adobe Dreamweaver Unspecified algoriddim djay Pro 2.2 Amazon OpenJDK Platform 8.0 Anaconda Miniconda3 3.8 Anaplan Anaplan Excel Addin 4.0 Anaplan PowerPoint Add-in 1.7 Anaplan Excel Add-in 4.1 Andreas Hegenberg BetterTouchTool 2.425 Andreas Hegenberg BetterTouchTool 3.505 Andreas Pehnack Synalyze It! 1.23 Apache Software Foundation Hbase 2.1 Apache Software Foundation Hive Metastore 3.1 Apache Software Foundation JMeter 3.0 Apache Software Foundation zeppelin 0.7 Apache Software Foundation NiFi 1.11 Applanix POSPac UAV 8.5 Apple Xcode 12.1 Apple Xcode 12.2 Apple Keynote 10.2 Apple Keynote 10.3 Apple WebKit 15609 Apple VoiceOver 10.0 Apple Kerberos v5 10.15 Apple Numbers 10.2 Apple Numbers 10.3 Apple WebKit 156 Apple Enterprise Connect 16 Apple Ruby for Mac 2 Apple Monodraw 1.4 Applian Technologies Replay Video Capture 9.1 Applian Technologies Director 4.3 Applied Systems WealthTrack 10.0 Aranda Software Asset Management Unspecified ArcSoft WD Backup Unspecified ASG Technologies ASG-Zena Platform Agent 4.1 Aspose Aspose.Words for Reporting Services -
Maîtriser Apache Jmeter Du Test De Charge À Devops
Maîtriser Apache JMeter Du test de charge à Devops Antonio Gomes Rodrigues, Bruno Demion (Milamber) et Philippe Mouawad Ce livre est en vente à http://leanpub.com/maitriser-jmeter-du-test-de-charge-a-devops Version publiée le 2018-09-30 ISBN 978-2-9555036-1-4 Ce livre est publié par Leanpub. Leanpub permet aux auteurs et aux éditeurs de bénéficier du Lean Publishing. Lean Publishing consiste à publier à l’aide d’outils très simples de nombreuses itérations d’un livre électronique en cours de rédaction, d’obtenir des retours et commentaires des lecteurs afin d’améliorer le livre. © 2014 - 2018 Antonio Gomes Rodrigues, Bruno Demion (Milamber) et Philippe Mouawad Tweet ce livre ! S’il vous plaît aidez Antonio Gomes Rodrigues, Bruno Demion (Milamber) et Philippe Mouawad en parlant de ce livre sur Twitter ! Le tweet suggéré pour ce livre est : Je viens d’acheter Maîtriser Apache JMeter : Du test de charge à #Devops par @ra0077, @milamberspace, @philmdot sur https ://leanpub.com/maitriser-jmeter-du-test-de-charge-a-devops Le hashtag suggéré pour ce livre est #jmeter. Découvrez ce que les gens disent à propos du livre en cliquant sur ce lien pour rechercher ce hashtag sur Twitter : #jmeter Couverture et quatrième de couverture conçues par Cécile Platteeuw (C’grafic) Table des matières Droits ............................................ 1 Présentation des auteurs ................................ 2 Antonio Gomes Rodrigues ............................. 2 Bruno Demion (Milamber) ............................. 2 Philippe Mouawad (Philippe M.) ......................... 3 L’écosystème d’Apache JMeter ............................ 5 Introduction ...................................... 5 Plugin polyvalent ................................... 5 JMeter Plugins .................................. 5 JMeter dans le cloud ................................. 18 BlazeMeter .................................... 19 Tricentis Flood .................................. 23 Redline 13 ................................... -
Database Licensing Information User Manual
Oracle® Database Database Licensing Information User Manual 19c E94254-04 April 2019 Oracle Database Database Licensing Information User Manual, 19c E94254-04 Copyright © 2004, 2019, Oracle and/or its affiliates. All rights reserved. Contributors: Penny Avril, Prabhaker Gongloor, Mughees Minhas, Anu Natarajan, Jill Robinson This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, then the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs, including any operating system, integrated software, any programs installed on the hardware, and/or documentation, delivered to U.S. Government end users are "commercial computer software" pursuant to the applicable Federal Acquisition Regulation and agency- specific supplemental regulations. As such, use, duplication, disclosure, modification, and adaptation of the programs, including any operating system, integrated software, any programs installed on the hardware, and/or documentation, shall be subject to license terms and license restrictions applicable to the programs.