Understanding and Managing the Performance Variation and Data
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Orchestrating Big Data Analysis Workflows in the Cloud: Research Challenges, Survey, and Future Directions
00 Orchestrating Big Data Analysis Workflows in the Cloud: Research Challenges, Survey, and Future Directions MUTAZ BARIKA, University of Tasmania SAURABH GARG, University of Tasmania ALBERT Y. ZOMAYA, University of Sydney LIZHE WANG, China University of Geoscience (Wuhan) AAD VAN MOORSEL, Newcastle University RAJIV RANJAN, Chinese University of Geoscienes and Newcastle University Interest in processing big data has increased rapidly to gain insights that can transform businesses, government policies and research outcomes. This has led to advancement in communication, programming and processing technologies, including Cloud computing services and technologies such as Hadoop, Spark and Storm. This trend also affects the needs of analytical applications, which are no longer monolithic but composed of several individual analytical steps running in the form of a workflow. These Big Data Workflows are vastly different in nature from traditional workflows. Researchers arecurrently facing the challenge of how to orchestrate and manage the execution of such workflows. In this paper, we discuss in detail orchestration requirements of these workflows as well as the challenges in achieving these requirements. We alsosurvey current trends and research that supports orchestration of big data workflows and identify open research challenges to guide future developments in this area. CCS Concepts: • General and reference → Surveys and overviews; • Information systems → Data analytics; • Computer systems organization → Cloud computing; Additional Key Words and Phrases: Big Data, Cloud Computing, Workflow Orchestration, Requirements, Approaches ACM Reference format: Mutaz Barika, Saurabh Garg, Albert Y. Zomaya, Lizhe Wang, Aad van Moorsel, and Rajiv Ranjan. 2018. Orchestrating Big Data Analysis Workflows in the Cloud: Research Challenges, Survey, and Future Directions. -
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 -
Deliverable No. 5.3 Techniques to Build the Cloud Infrastructure Available to the Community
Deliverable No. 5.3 Techniques to build the cloud infrastructure available to the community Grant Agreement No.: 600841 Deliverable No.: D5.3 Deliverable Name: Techniques to build the cloud infrastructure available to the community Contractual Submission Date: 31/03/2015 Actual Submission Date: 31/03/2015 Dissemination Level PU Public X PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services) Grant Agreement no. 600841 D5.3 – Techniques to build the cloud infrastructure available to the community COVER AND CONTROL PAGE OF DOCUMENT Project Acronym: CHIC Project Full Name: Computational Horizons In Cancer (CHIC): Developing Meta- and Hyper-Multiscale Models and Repositories for In Silico Oncology Deliverable No.: D5.3 Document name: Techniques to build the cloud infrastructure available to the community Nature (R, P, D, O)1 R Dissemination Level (PU, PP, PU RE, CO)2 Version: 1.0 Actual Submission Date: 31/03/2015 Editor: Manolis Tsiknakis Institution: FORTH E-Mail: [email protected] ABSTRACT: This deliverable reports on the technologies, techniques and configuration needed to install, configure, maintain and run a private cloud infrastructure for productive usage. KEYWORD LIST: Cloud infrastructure, OpenStack, Eucalyptus, CloudStack, VMware vSphere, virtualization, computation, storage, security, architecture. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 600841. The author is solely responsible for its content, it does not represent the opinion of the European Community and the Community is not responsible for any use that might be made of data appearing therein. -
Cloud Computing: a Taxonomy of Platform and Infrastructure-Level Offerings David Hilley College of Computing Georgia Institute of Technology
Cloud Computing: A Taxonomy of Platform and Infrastructure-level Offerings David Hilley College of Computing Georgia Institute of Technology April 2009 Cloud Computing: A Taxonomy of Platform and Infrastructure-level Offerings David Hilley 1 Introduction Cloud computing is a buzzword and umbrella term applied to several nascent trends in the turbulent landscape of information technology. Computing in the “cloud” alludes to ubiquitous and inexhaustible on-demand IT resources accessible through the Internet. Practically every new Internet-based service from Gmail [1] to Amazon Web Services [2] to Microsoft Online Services [3] to even Facebook [4] have been labeled “cloud” offerings, either officially or externally. Although cloud computing has garnered significant interest, factors such as unclear terminology, non-existent product “paper launches”, and opportunistic marketing have led to a significant lack of clarity surrounding discussions of cloud computing technology and products. The need for clarity is well-recognized within the industry [5] and by industry observers [6]. Perhaps more importantly, due to the relative infancy of the industry, currently-available product offerings are not standardized. Neither providers nor potential consumers really know what a “good” cloud computing product offering should look like and what classes of products are appropriate. Consequently, products are not easily comparable. The scope of various product offerings differ and overlap in complicated ways – for example, Ama- zon’s EC2 service [7] and Google’s App Engine [8] partially overlap in scope and applicability. EC2 is more flexible but also lower-level, while App Engine subsumes some functionality in Amazon Web Services suite of offerings [2] external to EC2. -
Cloud Interoperability with the Opennebula Toolkit
Cloud Computing: Interoperability and Data Portability Issues Microsoft, Brussels st 1 December 2009 Cloud Interoperability with the OpenNebula Toolkit Distributed Systems Architecture Research Group Universidad Complutense de Madrid 1/11 Cloud Computing in a Nutshell Cloud Interoperability with the OpenNebula Toolkit What Who Software as a Service On-demand End-user access to any (does not care about hw or sw) application Platform as a Service Platform for Developer building and (no managing of the delivering web underlying hw & swlayers) applications Infrastructure as a Raw computer System Administrator Serviceᄎ infrastructure (complete management of the computer infrastructure) Innovative open, flexible and scalable technology to build IaaS clouds Physical Infrastructure 2/11 What is OpenNebula? Cloud Interoperability with the OpenNebula Toolkit Innovations Designed to address the technology challenges in cloud computing management Open-source Toolkit OpenNebula v1.4 • Support to build new cloud interfaces • Open and flexible tool to fit into any datacenter and VM integrate with any ecosystem component VM • Private, public and hybrid clouds VM • Based on standards • Support federation of infrastructures • Efficient and scalable management of the cloud 3/11 A Toolkit for System Integrators Cloud Interoperability with the OpenNebula Toolkit One Size does not Fit All: Tailoring the Tool to Fit your Needs • Open, modular and extensible architecture • Easy to enhance and embed • Minimal installation requirements (distributed in Ubuntu) • Open Source – Apache 2 Virt. Virt. InterfacesVirt. SchedulersVirt. OpenNebula API Virtual and Physical Resource Management Driver API Virt. Virt. Virt. Virt. ComputeVirt. StorageVirt. NetworkVirt. CloudVirt. 4/11 Interoperability in the OpenNebula Toolkit Cloud Interoperability with the OpenNebula Toolkit Interoperation from Different Perspectives 1. -
Evaluation of SPARQL Queries on Apache Flink
applied sciences Article SPARQL2Flink: Evaluation of SPARQL Queries on Apache Flink Oscar Ceballos 1 , Carlos Alberto Ramírez Restrepo 2 , María Constanza Pabón 2 , Andres M. Castillo 1,* and Oscar Corcho 3 1 Escuela de Ingeniería de Sistemas y Computación, Universidad del Valle, Ciudad Universitaria Meléndez Calle 13 No. 100-00, Cali 760032, Colombia; [email protected] 2 Departamento de Electrónica y Ciencias de la Computación, Pontificia Universidad Javeriana Cali, Calle 18 No. 118-250, Cali 760031, Colombia; [email protected] (C.A.R.R.); [email protected] (M.C.P.) 3 Ontology Engineering Group, Universidad Politécnica de Madrid, Campus de Montegancedo, Boadilla del Monte, 28660 Madrid, Spain; ocorcho@fi.upm.es * Correspondence: [email protected] Abstract: Existing SPARQL query engines and triple stores are continuously improved to handle more massive datasets. Several approaches have been developed in this context proposing the storage and querying of RDF data in a distributed fashion, mainly using the MapReduce Programming Model and Hadoop-based ecosystems. New trends in Big Data technologies have also emerged (e.g., Apache Spark, Apache Flink); they use distributed in-memory processing and promise to deliver higher data processing performance. In this paper, we present a formal interpretation of some PACT transformations implemented in the Apache Flink DataSet API. We use this formalization to provide a mapping to translate a SPARQL query to a Flink program. The mapping was implemented in a prototype used to determine the correctness and performance of the solution. The source code of the Citation: Ceballos, O.; Ramírez project is available in Github under the MIT license. -
Python for Bioinformatics, Second Edition
PYTHON FOR BIOINFORMATICS SECOND EDITION CHAPMAN & HALL/CRC Mathematical and Computational Biology Series Aims and scope: This series aims to capture new developments and summarize what is known over the entire spectrum of mathematical and computational biology and medicine. It seeks to encourage the integration of mathematical, statistical, and computational methods into biology by publishing a broad range of textbooks, reference works, and handbooks. The titles included in the series are meant to appeal to students, researchers, and professionals in the mathematical, statistical and computational sciences, fundamental biology and bioengineering, as well as interdisciplinary researchers involved in the field. The inclusion of concrete examples and applications, and programming techniques and examples, is highly encouraged. Series Editors N. F. Britton Department of Mathematical Sciences University of Bath Xihong Lin Department of Biostatistics Harvard University Nicola Mulder University of Cape Town South Africa Maria Victoria Schneider European Bioinformatics Institute Mona Singh Department of Computer Science Princeton University Anna Tramontano Department of Physics University of Rome La Sapienza Proposals for the series should be submitted to one of the series editors above or directly to: CRC Press, Taylor & Francis Group 3 Park Square, Milton Park Abingdon, Oxfordshire OX14 4RN UK Published Titles An Introduction to Systems Biology: Statistical Methods for QTL Mapping Design Principles of Biological Circuits Zehua Chen Uri Alon -
Polycom Realpresence Cloudaxis Open Source Software OFFER
Polycom® RealPresence® CloudAXIS™ Suite OFFER of Source for GPL and LGPL Software You may have received from Polycom®, certain products that contain—in part—some free software (software licensed in a way that allows you the freedom to run, copy, distribute, change, and improve the software). As a part of this product, Polycom may have distributed to you software, or made electronic downloads, that contain a version of several software packages, which are free software programs developed by the Free Software Foundation. With your purchase of the Polycom RealPresence® CloudAXIS™ Suite, Polycom has granted you a license to the above-mentioned software under the terms of the GNU General Public License (GPL), GNU Library General Public License (LGPLv2), GNU Lesser General Public License (LGPL), or BSD License. The text of these Licenses can be found at the internet address provided in Table A. For at least three years from the date of distribution of the applicable product or software, we will give to anyone who contacts us at the contact information provided below, for a charge of no more than our cost of physically distributing, the following items: • A copy of the complete corresponding machine-readable source code for programs listed below that are distributed under the GNU GPL • A copy of the corresponding machine-readable source code for the libraries listed below that are distributed under the GNU LGPL, as well as the executable object code of the Polycom work that the library links with The software included or distributed for the product, including any software that may be downloaded electronically via the internet or otherwise (the "Software") is licensed, not sold. -
Design of a Platform of Virtual Service Containers for Service Oriented Cloud Computing
DESIGN OF A PLATFORM OF VIRTUAL SERVICE CONTAINERS FOR SERVICE ORIENTED CLOUD COMPUTING Carlos de Alfonso Andrés García Vicente Hernández INDEX • Introduction • Our approach • Platform design • Storage • Security • Management and QoS • Catalogue • User Interface • Virtual Container • User profiles • Working lines and open problems 2 INTRODUCTION • In the latter years there have been a growing interest in Cloud Computing technologies and its possible applications. • There have appeared many commercial products • Mainly storage and Infrastructure as a Service. • Storage examples: Amazon S3, Nirvanix, Savvis • IaaS examples: Amazon EC2, GoGrid, 3Tera • Other examples: Google App Engine, Salesforce, Engine Yard • Some small sets of open Cloud Computing tools have been also developed. • Eucalyptus, Nimbus, OpenNebula, Enomaly, Abicloud, etc. • Almost any of them are oriented to the IaaS level, since there is a lack of research projects towards other cloud approaches. 3 INTRODUCTION • Currently most of cloud computing solutions are focusing in the lowest levels of the infrastructure. • Virtual machine deployment and management infrastructure. • Storage management infrastructure. • The current approach of vendors, to deploy Services in the cloud, is based in those levels instead of Services virtualization. • Provider’s approach is to package a preconfigured service in a virtual machine image, and deploy a whole virtual appliance to run the service. • There are some drawbacks like the waste of resources, a less direct service management, the low abstraction level, etc. OUR APPROACH • To offer a platform which enable developers to deploy and to manage virtual services as IaaS platforms deploy and manage virtual machines. • That means to deploy only the service instance that is needed, instead of deploying the whole virtual appliance. -
The Opennebula Standard-Based Open-Source Toolkit to Build Cloud Infrastructures
Jornadas Técnicas de RedIRIS 2009 Santiago de Compostela 27th November 2009 The OpenNebula Standard-based Open -source Toolkit to Build Cloud Infrastructures Distributed Systems Architecture Research Group Universidad Complutense de Madrid 1/20 Cloud Computing in a Nutshell The OpenNebula Standard-based Open-source Toolkit to Build Cloud Infrastructures What Who Software as a Service On-demand End-user access to any (does not care about hw or sw) application Platform as a Service Platform for Developer building and (no managing of the delivering web underlying hw & swlayers) applications Infrastructure as a Raw computer System Administrator Serviceᄎ infrastructure (complete management of the computer infrastructure) Innovative open, flexible and scalable technology to build IaaS clouds Physical Infrastructure 2/20 From Public to Private Cloud Computing The OpenNebula Standard-based Open-source Toolkit to Build Cloud Infrastructures Public Cloud • Flexible and elastic capacity • Ubiquitous network access • On-demand access • Pay per use Service Cloud User/Service Provider User (Cloud Interface) Private Cloud • Centralized management VM • VM placement optimization VM • Dynamic resizing and partitioning VM of the infrastructure • Support for heterogeneous workloads 3/20 Contents The OpenNebula Standard-based Open-source Toolkit to Build Cloud Infrastructures Innovations Designed to address the technology challenges in cloud computing management Toolkit OpenNebula v1.4 Community Users, projects and ecosystem Open-source and Standardization -
Flare: Optimizing Apache Spark with Native Compilation
Flare: Optimizing Apache Spark with Native Compilation for Scale-Up Architectures and Medium-Size Data Gregory Essertel, Ruby Tahboub, and James Decker, Purdue University; Kevin Brown and Kunle Olukotun, Stanford University; Tiark Rompf, Purdue University https://www.usenix.org/conference/osdi18/presentation/essertel This paper is included in the Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’18). October 8–10, 2018 • Carlsbad, CA, USA ISBN 978-1-939133-08-3 Open access to the Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation is sponsored by USENIX. Flare: Optimizing Apache Spark with Native Compilation for Scale-Up Architectures and Medium-Size Data Grégory M. Essertel1, Ruby Y. Tahboub1, James M. Decker1, Kevin J. Brown2, Kunle Olukotun2, Tiark Rompf1 1Purdue University, 2Stanford University {gesserte,rtahboub,decker31,tiark}@purdue.edu, {kjbrown,kunle}@stanford.edu Abstract cessing. Systems like Apache Spark [8] have gained enormous traction thanks to their intuitive APIs and abil- In recent years, Apache Spark has become the de facto ity to scale to very large data sizes, thereby commoditiz- standard for big data processing. Spark has enabled a ing petabyte-scale (PB) data processing for large num- wide audience of users to process petabyte-scale work- bers of users. But thanks to its attractive programming loads due to its flexibility and ease of use: users are able interface and tooling, people are also increasingly using to mix SQL-style relational queries with Scala or Python Spark for smaller workloads. Even for companies that code, and have the resultant programs distributed across also have PB-scale data, there is typically a long tail of an entire cluster, all without having to work with low- tasks of much smaller size, which make up a very impor- level parallelization or network primitives. -
Large Scale Querying and Processing for Property Graphs Phd Symposium∗
Large Scale Querying and Processing for Property Graphs PhD Symposium∗ Mohamed Ragab Data Systems Group, University of Tartu Tartu, Estonia [email protected] ABSTRACT Recently, large scale graph data management, querying and pro- cessing have experienced a renaissance in several timely applica- tion domains (e.g., social networks, bibliographical networks and knowledge graphs). However, these applications still introduce new challenges with large-scale graph processing. Therefore, recently, we have witnessed a remarkable growth in the preva- lence of work on graph processing in both academia and industry. Querying and processing large graphs is an interesting and chal- lenging task. Recently, several centralized/distributed large-scale graph processing frameworks have been developed. However, they mainly focus on batch graph analytics. On the other hand, the state-of-the-art graph databases can’t sustain for distributed Figure 1: A simple example of a Property Graph efficient querying for large graphs with complex queries. Inpar- ticular, online large scale graph querying engines are still limited. In this paper, we present a research plan shipped with the state- graph data following the core principles of relational database systems [10]. Popular Graph databases include Neo4j1, Titan2, of-the-art techniques for large-scale property graph querying and 3 4 processing. We present our goals and initial results for querying ArangoDB and HyperGraphDB among many others. and processing large property graphs based on the emerging and In general, graphs can be represented in different data mod- promising Apache Spark framework, a defacto standard platform els [1]. In practice, the two most commonly-used graph data models are: Edge-Directed/Labelled graph (e.g.