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Security in Cloud Computing a Security Assessment of Cloud Computing Providers for an Online Receipt Storage
Security in Cloud Computing A Security Assessment of Cloud Computing Providers for an Online Receipt Storage Mats Andreassen Kåre Marius Blakstad Master of Science in Computer Science Submission date: June 2010 Supervisor: Lillian Røstad, IDI Norwegian University of Science and Technology Department of Computer and Information Science Problem Description We will survey some current cloud computing vendors and compare them to find patterns in how their feature sets are evolving. The start-up firm dSafe intends to exploit the promises of cloud computing in order to launch their business idea with only marginal hardware and licensing costs. We must define the criteria for how dSafe's application can be sufficiently secure in the cloud as well as how dSafe can get there. Assignment given: 14. January 2010 Supervisor: Lillian Røstad, IDI Abstract Considerations with regards to security issues and demands must be addressed before migrating an application into a cloud computing environment. Different vendors, Microsoft Azure, Amazon Web Services and Google AppEngine, provide different capabilities and solutions to the individual areas of concern presented by each application. Through a case study of an online receipt storage application from the company dSafe, a basis is formed for the evaluation. The three cloud computing vendors are assessed with regards to a security assessment framework provided by the Cloud Security Alliance and the application of this on the case study. Finally, the study is concluded with a set of general recommendations and the recommendation of a cloud vendor. This is based on a number of security as- pects related to the case study’s existence in the cloud. -
SOL: Effortless Device Support for AI Frameworks Without Source Code Changes
SOL: Effortless Device Support for AI Frameworks without Source Code Changes Nicolas Weber and Felipe Huici NEC Laboratories Europe Abstract—Modern high performance computing clusters heav- State of the Art Proposed with SOL ily rely on accelerators to overcome the limited compute power API (Python, C/C++, …) API (Python, C/C++, …) of CPUs. These supercomputers run various applications from different domains such as simulations, numerical applications or Framework Core Framework Core artificial intelligence (AI). As a result, vendors need to be able to Device Backends SOL efficiently run a wide variety of workloads on their hardware. In the AI domain this is in particular exacerbated by the Fig. 1: Abstraction layers within AI frameworks. existance of a number of popular frameworks (e.g, PyTorch, TensorFlow, etc.) that have no common code base, and can vary lines of code to their scripts in order to enable SOL and its in functionality. The code of these frameworks evolves quickly, hardware support. making it expensive to keep up with all changes and potentially We explore two strategies to integrate new devices into AI forcing developers to go through constant rounds of upstreaming. frameworks using SOL as a middleware, to keep the original In this paper we explore how to provide hardware support in AI frameworks without changing the framework’s source code in AI framework unchanged and still add support to new device order to minimize maintenance overhead. We introduce SOL, an types. The first strategy hides the entire offloading procedure AI acceleration middleware that provides a hardware abstraction from the framework, and the second only injects the necessary layer that allows us to transparently support heterogenous hard- functionality into the framework to enable the execution, but ware. -
Deep Learning Frameworks | NVIDIA Developer
4/10/2017 Deep Learning Frameworks | NVIDIA Developer Deep Learning Frameworks The NVIDIA Deep Learning SDK accelerates widelyused deep learning frameworks such as Caffe, CNTK, TensorFlow, Theano and Torch as well as many other deep learning applications. Choose a deep learning framework from the list below, download the supported version of cuDNN and follow the instructions on the framework page to get started. Caffe is a deep learning framework made with expression, speed, and modularity in mind. Caffe is developed by the Berkeley Vision and Learning Center (BVLC), as well as community contributors and is popular for computer vision. Caffe supports cuDNN v5 for GPU acceleration. Supported interfaces: C, C++, Python, MATLAB, Command line interface Learning Resources Deep learning course: Getting Started with the Caffe Framework Blog: Deep Learning for Computer Vision with Caffe and cuDNN Download Caffe Download cuDNN The Microsoft Cognitive Toolkit —previously known as CNTK— is a unified deeplearning toolkit from Microsoft Research that makes it easy to train and combine popular model types across multiple GPUs and servers. Microsoft Cognitive Toolkit implements highly efficient CNN and RNN training for speech, image and text data. Microsoft Cognitive Toolkit supports cuDNN v5.1 for GPU acceleration. Supported interfaces: Python, C++, C# and Command line interface Download CNTK Download cuDNN TensorFlow is a software library for numerical computation using data flow graphs, developed by Google’s Machine Intelligence research organization. TensorFlow supports cuDNN v5.1 for GPU acceleration. Supported interfaces: C++, Python Download TensorFlow Download cuDNN https://developer.nvidia.com/deeplearningframeworks 1/3 4/10/2017 Deep Learning Frameworks | NVIDIA Developer Theano is a math expression compiler that efficiently defines, optimizes, and evaluates mathematical expressions involving multidimensional arrays. -
1 Amazon Sagemaker
1 Amazon SageMaker 13:45~14:30 Amazon SageMaker 14:30~15:15 Amazon SageMaker re:Invent 15:15~15:45 Q&A | 15:45~17:00 Amazon SageMaker 20 SmartNews Data Scientist, Meng Lee Sagemaker SageMaker - FiNC FiNC Technologies SIGNATE Amazon SageMaker SIGNATE CTO 17:00~17:15© 2018, Amazon Web Services, Inc. or itsQ&A Affiliates. All rights reserved. Amazon Confidential and Trademark Amazon SageMaker Makoto Shimura, Solutions Architect 2019/01/15 © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark • • • • ⎼ Amazon Athena ⎼ AWS Glue ⎼ Amazon SageMaker © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark • • Amazon SageMaker • Amazon SageMasker • SageMaker SDK • [ | | ] • Amazon SageMaker • © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark 開発 学習 推論推論 学習に使うコードを記述 大量の GPU 大量のCPU や GPU 小規模データで動作確認 大規模データの処理 継続的なデプロイ 試行錯誤の繰り返し 様々なデバイスで動作 © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark 開発 学習 推論推論 エンジニアがプロダク データサイエンティストが開発環境で作業 ション環境に構築 開発と学習を同じ 1 台のインスタンスで実施 API サーバにデプロイ Deep Learning であれば GPU インスタンスを使用 エッジデバイスで動作 © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark & • 開発 学習 推論推論 • エンジニアがプロダク データサイエンティストが開発環境で作業 • ション環境に構築 開発と学習を同じ 1 台のインスタンスで実施 API サーバにデプロイ • Deep Learning であれば GPU インスタンスを使用 エッジデバイスで動作 • API • • © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark Amazon SageMaker © 2018, Amazon Web Services, Inc. -
Intel® Optimized AI Frameworks
Intel® optimized AI frameworks Dr. Fabio Baruffa & Shailen Sobhee Technical Consulting Engineers, Intel IAGS Visit: www.intel.ai/technology Speed up development using open AI software Machine learning Deep learning TOOLKITS App Open source platform for building E2E Analytics & Deep learning inference deployment Open source, scalable, and developers AI applications on Apache Spark* with distributed on CPU/GPU/FPGA/VPU for Caffe*, extensible distributed deep learning TensorFlow*, Keras*, BigDL TensorFlow*, MXNet*, ONNX*, Kaldi* platform built on Kubernetes (BETA) Python R Distributed Intel-optimized Frameworks libraries * • Scikit- • Cart • MlLib (on Spark) * * And more framework Data learn • Random • Mahout optimizations underway • Pandas Forest including PaddlePaddle*, scientists * * • NumPy • e1071 Chainer*, CNTK* & others Intel® Intel® Data Analytics Intel® Math Kernel Library Kernels Distribution Acceleration Library Library for Deep Neural Networks for Python* (Intel® DAAL) (Intel® MKL-DNN) developers Intel distribution High performance machine Open source compiler for deep learning optimized for learning & data analytics Open source DNN functions for model computations optimized for multiple machine learning library CPU / integrated graphics devices (CPU, GPU, NNP) from multiple frameworks (TF, MXNet, ONNX) 2 Visit: www.intel.ai/technology Speed up development using open AI software Machine learning Deep learning TOOLKITS App Open source platform for building E2E Analytics & Deep learning inference deployment Open source, scalable, -
Theano: a Python Framework for Fast Computation of Mathematical Expressions (The Theano Development Team)∗
Theano: A Python framework for fast computation of mathematical expressions (The Theano Development Team)∗ Rami Al-Rfou,6 Guillaume Alain,1 Amjad Almahairi,1 Christof Angermueller,7, 8 Dzmitry Bahdanau,1 Nicolas Ballas,1 Fred´ eric´ Bastien,1 Justin Bayer, Anatoly Belikov,9 Alexander Belopolsky,10 Yoshua Bengio,1, 3 Arnaud Bergeron,1 James Bergstra,1 Valentin Bisson,1 Josh Bleecher Snyder, Nicolas Bouchard,1 Nicolas Boulanger-Lewandowski,1 Xavier Bouthillier,1 Alexandre de Brebisson,´ 1 Olivier Breuleux,1 Pierre-Luc Carrier,1 Kyunghyun Cho,1, 11 Jan Chorowski,1, 12 Paul Christiano,13 Tim Cooijmans,1, 14 Marc-Alexandre Cotˆ e,´ 15 Myriam Cotˆ e,´ 1 Aaron Courville,1, 4 Yann N. Dauphin,1, 16 Olivier Delalleau,1 Julien Demouth,17 Guillaume Desjardins,1, 18 Sander Dieleman,19 Laurent Dinh,1 Melanie´ Ducoffe,1, 20 Vincent Dumoulin,1 Samira Ebrahimi Kahou,1, 2 Dumitru Erhan,1, 21 Ziye Fan,22 Orhan Firat,1, 23 Mathieu Germain,1 Xavier Glorot,1, 18 Ian Goodfellow,1, 24 Matt Graham,25 Caglar Gulcehre,1 Philippe Hamel,1 Iban Harlouchet,1 Jean-Philippe Heng,1, 26 Balazs´ Hidasi,27 Sina Honari,1 Arjun Jain,28 Sebastien´ Jean,1, 11 Kai Jia,29 Mikhail Korobov,30 Vivek Kulkarni,6 Alex Lamb,1 Pascal Lamblin,1 Eric Larsen,1, 31 Cesar´ Laurent,1 Sean Lee,17 Simon Lefrancois,1 Simon Lemieux,1 Nicholas Leonard,´ 1 Zhouhan Lin,1 Jesse A. Livezey,32 Cory Lorenz,33 Jeremiah Lowin, Qianli Ma,34 Pierre-Antoine Manzagol,1 Olivier Mastropietro,1 Robert T. McGibbon,35 Roland Memisevic,1, 4 Bart van Merrienboer,¨ 1 Vincent Michalski,1 Mehdi Mirza,1 Alberto Orlandi, Christopher Pal,1, 2 Razvan Pascanu,1, 18 Mohammad Pezeshki,1 Colin Raffel,36 Daniel Renshaw,25 Matthew Rocklin, Adriana Romero,1 Markus Roth, Peter Sadowski,37 John Salvatier,38 Franc¸ois Savard,1 Jan Schluter,¨ 39 John Schulman,24 Gabriel Schwartz,40 Iulian Vlad Serban,1 Dmitriy Serdyuk,1 Samira Shabanian,1 Etienne´ Simon,1, 41 Sigurd Spieckermann, S. -
Building Machine Learning Inference Pipelines at Scale
Building Machine Learning inference pipelines at scale Julien Simon Global Evangelist, AI & Machine Learning @julsimon © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Problem statement • Real-life Machine Learning applications require more than a single model. • Data may need pre-processing: normalization, feature engineering, dimensionality reduction, etc. • Predictions may need post-processing: filtering, sorting, combining, etc. Our goal: build scalable ML pipelines with open source (Spark, Scikit-learn, XGBoost) and managed services (Amazon EMR, AWS Glue, Amazon SageMaker) © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Apache Spark https://spark.apache.org/ • Open-source, distributed processing system • In-memory caching and optimized execution for fast performance (typically 100x faster than Hadoop) • Batch processing, streaming analytics, machine learning, graph databases and ad hoc queries • API for Java, Scala, Python, R, and SQL • Available in Amazon EMR and AWS Glue © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. MLlib – Machine learning library https://spark.apache.org/docs/latest/ml-guide.html • Algorithms: classification, regression, clustering, collaborative filtering. • Featurization: feature extraction, transformation, dimensionality reduction. • Tools for constructing, evaluating and tuning pipelines • Transformer – a transform function that maps a DataFrame into a new -
Open Source in the Enterprise
Open Source in the Enterprise Andy Oram and Zaheda Bhorat Beijing Boston Farnham Sebastopol Tokyo Open Source in the Enterprise by Andy Oram and Zaheda Bhorat Copyright © 2018 O’Reilly Media. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online edi‐ tions are also available for most titles (http://oreilly.com/safari). For more information, contact our corporate/institutional sales department: 800-998-9938 or [email protected]. Editor: Michele Cronin Interior Designer: David Futato Production Editor: Kristen Brown Cover Designer: Karen Montgomery Copyeditor: Octal Publishing Services, Inc. July 2018: First Edition Revision History for the First Edition 2018-06-18: First Release The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Open Source in the Enterprise, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the authors, and do not represent the publisher’s views. While the publisher and the authors have used good faith efforts to ensure that the informa‐ tion and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. -
AWS Certified Developer – Associate (DVA-C01) Sample Exam Questions
AWS Certified Developer – Associate (DVA-C01) Sample Exam Questions 1) A company is migrating a legacy application to Amazon EC2. The application uses a user name and password stored in the source code to connect to a MySQL database. The database will be migrated to an Amazon RDS for MySQL DB instance. As part of the migration, the company wants to implement a secure way to store and automatically rotate the database credentials. Which approach meets these requirements? A) Store the database credentials in environment variables in an Amazon Machine Image (AMI). Rotate the credentials by replacing the AMI. B) Store the database credentials in AWS Systems Manager Parameter Store. Configure Parameter Store to automatically rotate the credentials. C) Store the database credentials in environment variables on the EC2 instances. Rotate the credentials by relaunching the EC2 instances. D) Store the database credentials in AWS Secrets Manager. Configure Secrets Manager to automatically rotate the credentials. 2) A Developer is designing a web application that allows the users to post comments and receive near- real-time feedback. Which architectures meet these requirements? (Select TWO.) A) Create an AWS AppSync schema and corresponding APIs. Use an Amazon DynamoDB table as the data store. B) Create a WebSocket API in Amazon API Gateway. Use an AWS Lambda function as the backend and an Amazon DynamoDB table as the data store. C) Create an AWS Elastic Beanstalk application backed by an Amazon RDS database. Configure the application to allow long-lived TCP/IP sockets. D) Create a GraphQL endpoint in Amazon API Gateway. Use an Amazon DynamoDB table as the data store. -
Introduction to Amazon Web Services
Introduction to Amazon Web Services Jeff Barr Senior AWS Evangelist @jeffbarr / [email protected] What Does It Take to be a Global Online Retailer? The Obvious Part… And the Not-So Obvious Part How Did Amazon Get in to Cloud Computing? • We’d been working on it for over a decade • Development of a platform to enable sellers on the Amazon global infrastructure • Internal need for centralized, scalable deployment environment for applications • Early forays into web services proved developers were hungry for more This Led to a Broader Mission • Enable businesses and developers to use web services (what people now call “the cloud”) to build scalable, sophisticated applications. “It's not the customers' job to invent for themselves. It's your job to invent on their behalf. You need to listen to customers. You need to invent on their behalf. Kindle, EC2 would not have been developed if we did not have an inventive culture.” - Jeff Bezos, Founder & CEO, Amazon.com Attributes of Cloud Computing No Up-Front Capital Low Cost Pay Only for What Expense You Use Self-Service Easily Scale Up and Improve Agility & Time- Infrastructure Down to-Market Deploy Last-Generation IT Services Cloud-Generation IT Services Cloud-Generation IT Services What’s the Difference? Last-Generation Cloud-Generation • IT department • Empowered users • Manual Setup • Automated Setup • Hours/Days/Weeks • Seconds/Minutes • Error-prone • Scripted & repeatable • Small scale • Any scale AWS PLATFORM Cloud-Powered Applications Management & Administration Administration Identity & -
Serverless Predictions at Scale
Serverless Predictions at Scale Thomas Reske Global Solutions Architect, Amazon Web Services © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Serverless computing allows you to build and run applications and services without thinking about servers © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. What are the benefits of serverless computing? No server management Flexible scaling $ Automated high availability No excess capacity © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine Learning Process Fetch data Operations, Clean monitoring, and and optimization format data Integration Prepare and and deployment transform data Evaluation and Model Training verificaton © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine Learning Process Fetch data Operations, Clean How can I use pre-trained monitoring, and and models for serving optimization format data predictions? How to scale effectively? Integration Prepare and and deployment transform data Evaluation and Model Training verificaton © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. What exactly are we deploying? © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. A Pre-trained Model... • ... is simply a model which has been trained previously • Model artifacts (structure, helper files, parameters, weights, ...) are files, e.g. in JSON or binary format • Can be serialized (saved) and de-serialized (loaded) by common deep learning frameworks • Allows for fine-tuning of models („head-start“ or „freezing“), but also to apply them to similar problems and different data („transfer learning“) • Models can be made publicly available, e.g. in the MXNet Model Zoo © 2018, Amazon Web Services, Inc. -
Implementación De Los Algoritmos De Factorización 201
Universidad Complutense de Madrid Facultad de Informática Sistemas Informáticos 2010/2011 RSA@Cloud Criptoanálisis eficiente en la Nube Autores: Directores de proyecto: Alberto Megía Negrillo José Luis Vázquez-Poletti Antonio Molinera Lamas Ignacio Martin Llorente José Antonio Rueda Sánchez Dpto. Arquitectura de Computadores Página 0 Facultad de Informática UCM RSA@Cloud Sistemas Informáticos – FDI Se autoriza a la Universidad Complutense a difundir y utilizar con fines académicos, no comerciales y mencionando expresamente a sus autores, tanto la propia memoria, como el código, la documentación y/o el prototipo desarrollado. Alberto Megía Negrillo Antonio Molinera Lamas José Antonio Rueda Sánchez Página 1 Facultad de Informática UCM RSA@Cloud Sistemas Informáticos – FDI Página 2 Facultad de Informática UCM RSA@Cloud Sistemas Informáticos – FDI Agradecimientos Un trabajo como este no habría sido posible sin la cooperación de mucha gente. En primer lugar queremos agradecérselo a nuestras familias que tanto han confiado y nos han dado. A nuestros directores José Luis Vázquez-Poletti e Ignacio Martín Llorente, por conseguir llevar adelante este proyecto. Por último, a todas y cada una de las personas que forman parte de la Facultad de Informática de la Universidad Complutense de Madrid, nuestro hogar durante estos años. Página 3 Facultad de Informática UCM RSA@Cloud Sistemas Informáticos – FDI Página 4 Facultad de Informática UCM RSA@Cloud Sistemas Informáticos – FDI Resumen En el presente documento se describe un sistema que aprovecha las virtudes de la computación Cloud y el paralelismo para la factorización de números grandes, base de la seguridad del criptosistema RSA, mediante el empleo de diferentes algoritmos matemáticos como la división por tentativa y criba cuadrática.