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Implementation of Machining on the Cloud
Implementation of Machining on the Cloud: A Case Study in PLM Environment Saurav Bhatt, Frédéric Segonds, Nicolas Maranzana, Ameziane Aoussat, Vincent Frerebeau, Damien Chasset To cite this version: Saurav Bhatt, Frédéric Segonds, Nicolas Maranzana, Ameziane Aoussat, Vincent Frerebeau, et al.. Implementation of Machining on the Cloud: A Case Study in PLM Environment. 13th IFIP Interna- tional Conference on Product Lifecycle Management (PLM), Jul 2016, Columbia, SC, United States. pp.341-355, 10.1007/978-3-319-54660-5_31. hal-01699699 HAL Id: hal-01699699 https://hal.inria.fr/hal-01699699 Submitted on 2 Feb 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Implementation of Machining on the Cloud: A case study in PLM environment Saurav Bhatt1,3, Frédéric Segonds2, Nicolas Maranzana2, Améziane Aoussat2, Vincent Frerebeau3, Damien Chasset3 1 Delhi College of Engineering, Delhi, India 2 Arts et Métiers, Paris Tech, LCPI, 151 Boulevard de l’Hôpital, 75013 Paris, France 3 Dassault Systemes, 10 Rue Marcel Dassault, 78140 Velizy Villacoublay, France [email protected] Abstract. This paper focuses on the implementation of cloud solutions in the field of machining which is encompassed by the much larger field of manufacturing. -
Enhancing Undergraduate Understanding of Subtractive Manufacturability Through Virtualized Simulation of CNC Machining
Paper ID #18310 Enhancing Undergraduate Understanding of Subtractive Manufacturability through Virtualized Simulation of CNC Machining Mr. Roby Lynn Dr. Kathryn W. Jablokow, Pennsylvania State University, Great Valley Dr. Kathryn Jablokow is an Associate Professor of Engineering Design and Mechanical Engineering at Penn State University. A graduate of Ohio State University (Ph.D., Electrical Engineering), Dr. Jablokow’s teaching and research interests include problem solving, invention, and creativity in science and engineer- ing, as well as robotics and manufacturing education. In addition to her membership in ASEE, she is a Senior Member of IEEE, a Fellow of ASME, and the recipient of the 2016 ASME Ruth and Joel Spira Outstanding Design Educator Award. Dr. Jablokow is the architect of a unique 4-course module fo- cused on creativity and problem solving leadership and is currently developing a new methodology for cognition-based design. She is one of three instructors for Penn State’s Massive Open Online Course (MOOC) on Creativity, Innovation, and Change, and she is the founding director of the Problem Solving Research Group, whose 50+ collaborating members include faculty and students from several universities, as well as industrial representatives, military leaders, and corporate consultants. Prof. Christopher Saldana Dr. Thomas Marshall Tucker, Tucker Innovations Dr. Tommy Tucker is the CEO and owner of Tucker Innovations. He has a Ph.D. in Mechanical Engineer- ing from the Georgia Institute of Technology. He has over 15 years of experience writing computationally intensive software applications for engineering, medical, and defense applications. After spending the early part of his career at high tech start-up companies, Dr. -
Real-Time, Safe and Certified OS
Real-Time, Safe and Certified OS Roman Kapl <[email protected]> drivers, customer projects, development Tomas Martinec <[email protected]> testing and certification © SYSGO AG · INTERNAL 1 Introduction • PikeOS – real-time, safety certified OS • Desktop and Server vs. • Embedded • Real-Time • Safety-Critical • Certified • Differences • Scheduling • Resource management • Features • Development © SYSGO AG · INTERNAL 2 Certification • Testing • Analysis • Lot of time • Even more paper • Required for safety-critical systems • Trains • Airplanes © SYSGO AG · INTERNAL 3 PikeOS • Embedded, real-time, certified OS • ~150 people (not just engineers) • Rail • Avionics • Space • This presentation is not about PikeOS specifically © SYSGO AG · INTERNAL 4 PikeOS technical • Microkernel • Inspired by L4 • Memory protection (MMU) • More complex than FreeRTOS • Virtualization hypervisor • X86, ARM, SPARC, PowerPC • Eclipse IDE for development © SYSGO AG · INTERNAL 5 Personalities • General • POSIX • Linux • Domain specific • ARINC653 • PikeOS native • Other • Ada, RT JAVA, AUTOSAR, ITRON, RTEMS © SYSGO AG · INTERNAL 6 PikeOS Architecture App. App. App. App. App. App. Volume Syste m Provider Partition PikeOS Para-Virtualized HW Virtualized File System (Native, POSIX, Guest OS PikeOS Native ARINC653, ...) Guest OS Linux, Android Linux, Android Device Driver User Space / Partitions Syste m PikeOS System Software ExtensionSyste m Extension PikeOS Microkernel Kernel Space / Hypervisor Architecture Platform Kernel Level Support Package Support Package Driver SoC / -
Sistemi Operativi Real-Time Marco Cesati Lezione R13 Sistemi Operativi Real-Time – II Schema Della Lezione
Sistemi operativi real-time Marco Cesati Lezione R13 Sistemi operativi real-time – II Schema della lezione Caratteristiche comuni VxWorks LynxOS Sistemi embedded e real-time QNX eCos Windows Linux come RTOS 15 gennaio 2013 Marco Cesati Dipartimento di Ingegneria Civile e Ingegneria Informatica Università degli Studi di Roma Tor Vergata SERT’13 R13.1 Sistemi operativi Di cosa parliamo in questa lezione? real-time Marco Cesati In questa lezione descriviamo brevemente alcuni dei più diffusi sistemi operativi real-time Schema della lezione Caratteristiche comuni VxWorks LynxOS 1 Caratteristiche comuni degli RTOS QNX 2 VxWorks eCos 3 LynxOS Windows Linux come RTOS 4 QNX Neutrino 5 eCos 6 Windows Embedded CE 7 Linux come RTOS SERT’13 R13.2 Sistemi operativi Caratteristiche comuni dei principali RTOS real-time Marco Cesati Corrispondenza agli standard: generalmente le API sono proprietarie, ma gli RTOS offrono anche compatibilità (compliancy) o conformità (conformancy) allo standard Real-Time POSIX Modularità e Scalabilità: il kernel ha una dimensione Schema della lezione Caratteristiche comuni (footprint) ridotta e le sue funzionalità sono configurabili VxWorks Dimensione del codice: spesso basati su microkernel LynxOS QNX Velocità e Efficienza: basso overhead per cambi di eCos contesto, latenza delle interruzioni e primitive di Windows sincronizzazione Linux come RTOS Porzioni di codice non interrompibile: generalmente molto corte e di durata predicibile Gestione delle interruzioni “separata”: interrupt handler corto e predicibile, ISR lunga -
Performance Study of Real-Time Operating Systems for Internet Of
IET Software Research Article ISSN 1751-8806 Performance study of real-time operating Received on 11th April 2017 Revised 13th December 2017 systems for internet of things devices Accepted on 13th January 2018 E-First on 16th February 2018 doi: 10.1049/iet-sen.2017.0048 www.ietdl.org Rafael Raymundo Belleza1 , Edison Pignaton de Freitas1 1Institute of Informatics, Federal University of Rio Grande do Sul, Av. Bento Gonçalves, 9500, CP 15064, Porto Alegre CEP: 91501-970, Brazil E-mail: [email protected] Abstract: The development of constrained devices for the internet of things (IoT) presents lots of challenges to software developers who build applications on top of these devices. Many applications in this domain have severe non-functional requirements related to timing properties, which are important concerns that have to be handled. By using real-time operating systems (RTOSs), developers have greater productivity, as they provide native support for real-time properties handling. Some of the key points in the software development for IoT in these constrained devices, like task synchronisation and network communications, are already solved by this provided real-time support. However, different RTOSs offer different degrees of support to the different demanded real-time properties. Observing this aspect, this study presents a set of benchmark tests on the selected open source and proprietary RTOSs focused on the IoT. The benchmark results show that there is no clear winner, as each RTOS performs well at least on some criteria, but general conclusions can be drawn on the suitability of each of them according to their performance evaluation in the obtained results. -
Security Target Pikeos Separation Kernel V4.2.2
Security Target PikeOS Separation Kernel v4.2.2 Document ID Revision DOORS Baseline Date State 00101-8000-ST 20.6 N.A. 2018-10-10 App Author: Dominic Eschweiler SYSGO AG Am Pfaffenstein 14, D-55270 Klein-Winternheim Notice: The contents of this document are proprietary to SYSGO AG and shall not be disclosed, disseminated, copied, or used except for purposes expressly authorized in writing by SYSGO AG. Doc. ID: 00101-8000-ST Revision: 20.6 This page intentionally left blank Copyright 2018 Page 2 of 47 All rights reserved. SYSGO AG Doc. ID: 00101-8000-ST Revision: 20.6 This page intentionally left blank Copyright 2018 Page 3 of 47 All rights reserved. SYSGO AG Doc. ID: 00101-8000-ST Revision: 20.6 Table of Contents 1 Introduction .................................................................................................................... 6 1.1 Purpose of this Document ........................................................................................... 6 1.2 Document References ................................................................................................ 6 1.2.1 Applicable Documents......................................................................................... 6 1.2.2 Referenced Documents ....................................................................................... 6 1.3 Abbreviations and Acronyms ....................................................................................... 6 1.4 Terms and Definitions................................................................................................ -
Oracle® Linux Virtualization Manager Getting Started Guide
Oracle® Linux Virtualization Manager Getting Started Guide F25124-11 September 2021 Oracle Legal Notices Copyright © 2019, 2021 Oracle and/or its affiliates. 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 embedded, installed or activated on delivered hardware, and modifications of such programs) and Oracle computer documentation or other Oracle data delivered to or accessed by U.S. Government end users are "commercial computer software" or "commercial computer software documentation" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, the use, reproduction, duplication, release, display, disclosure, modification, preparation of derivative works, and/or adaptation of i) Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs), ii) Oracle computer documentation and/or iii) other Oracle data, is subject to the rights and limitations specified in the license contained in the applicable contract. -
Container and Kernel-Based Virtual Machine (KVM) Virtualization for Network Function Virtualization (NFV)
Container and Kernel-Based Virtual Machine (KVM) Virtualization for Network Function Virtualization (NFV) White Paper August 2015 Order Number: 332860-001US YouLegal Lines andmay Disclaimers not use or facilitate the use of this document in connection with any infringement or other legal analysis concerning Intel products described herein. You agree to grant Intel a non-exclusive, royalty-free license to any patent claim thereafter drafted which includes subject matter disclosed herein. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest Intel product specifications and roadmaps. The products described may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request. Copies of documents which have an order number and are referenced in this document may be obtained by calling 1-800-548-4725 or by visiting: http://www.intel.com/ design/literature.htm. Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Learn more at http:// www.intel.com/ or from the OEM or retailer. Results have been estimated or simulated using internal Intel analysis or architecture simulation or modeling, and provided to you for informational purposes. Any differences in your system hardware, software or configuration may affect your actual performance. For more complete information about performance and benchmark results, visit www.intel.com/benchmarks. Tests document performance of components on a particular test, in specific systems. -
Us 2019 / 0319868 A1
US 20190319868A1 ( 19) United States (12 ) Patent Application Publication ( 10) Pub . No. : US 2019 /0319868 A1 Svennebring et al. ( 43 ) Pub . Date : Oct. 17 , 2019 ( 54 ) LINK PERFORMANCE PREDICTION (52 ) U . S . CI. TECHNOLOGIES CPC .. .. H04L 43/ 0882 (2013 . 01 ); H04W 24 /08 ( 2013 . 01 ) (71 ) Applicant : Intel Corporation , Santa Clara , CA (57 ) ABSTRACT (US ) Various systems and methods for determining and commu nicating Link Performance Predictions (LPPs ), such as in ( 72 ) Inventors : Jonas Svennebring , Sollentuna (SE ) ; connection with management of radio communication links, Antony Vance Jeyaraj, Bengaluru ( IN ) are discussed herein . The LPPs are predictions of future network behaviors /metrics ( e . g . , bandwidth , latency , capac (21 ) Appl . No. : 16 /452 , 352 ity , coverage holes , etc . ) . The LPPs are communicated to applications and /or network infrastructure, which allows the applications/ infrastructure to make operational decisions for ( 22 ) Filed : Jun . 25 , 2019 improved signaling / link resource utilization . In embodi ments , the link performance analysis is divided into multiple layers that determine their own link performance metrics, Publication Classification which are then fused together to make an LPP. Each layer (51 ) Int . Cl. runs different algorithms, and provides respective results to H04L 12 / 26 ( 2006 .01 ) an LPP layer /engine that fuses the results together to obtain H04W 24 / 08 (2006 .01 ) the LPP . Other embodiments are described and / or claimed . 700 Spatio - Temporal History Data Tx1 : C1 TIDE _ 1, DE _ 2 . .. Txt : C2 T2 DE _ 1 , DE _ 2 , . .. win Txs : C3 122 T : DE _ 1, DE _ 2 , .. TN DE _ 1 , DE _ 2 .. TxN : CN CELL LOAD MODEL 710 Real- Time Data 744 704 Patent Application Publication Oct. -
Embedded Operating Systems
7 Embedded Operating Systems Claudio Scordino1, Errico Guidieri1, Bruno Morelli1, Andrea Marongiu2,3, Giuseppe Tagliavini3 and Paolo Gai1 1Evidence SRL, Italy 2Swiss Federal Institute of Technology in Zurich (ETHZ), Switzerland 3University of Bologna, Italy In this chapter, we will provide a description of existing open-source operating systems (OSs) which have been analyzed with the objective of providing a porting for the reference architecture described in Chapter 2. Among the various possibilities, the ERIKA Enterprise RTOS (Real-Time Operating System) and Linux with preemption patches have been selected. A description of the porting effort on the reference architecture has also been provided. 7.1 Introduction In the past, OSs for high-performance computing (HPC) were based on custom-tailored solutions to fully exploit all performance opportunities of supercomputers. Nowadays, instead, HPC systems are being moved away from in-house OSs to more generic OS solutions like Linux. Such a trend can be observed in the TOP500 list [1] that includes the 500 most powerful supercomputers in the world, in which Linux dominates the competition. In fact, in around 20 years, Linux has been capable of conquering all the TOP500 list from scratch (for the first time in November 2017). Each manufacturer, however, still implements specific changes to the Linux OS to better exploit specific computer hardware features. This is especially true in the case of computing nodes in which lightweight kernels are used to speed up the computation. 173 174 Embedded Operating Systems Figure 7.1 Number of Linux-based supercomputers in the TOP500 list. Linux is a full-featured OS, originally designed to be used in server or desktop environments. -
Camworks 2019 Camworks
Virtual Machining Using CAMWorks 2019 Virtual Machining Using CAMWorks Virtual Machining Using CAMWorks® 2019 CAMWorks as a SOLIDWORKS® Module Chang Lower Prices Better Textbooks Kuang-Hua Chang, Ph.D. SDC SDC Better Textbooks. Lower Prices. PUBLICATIONS www.SDCpublications.com Visit the following websites to learn more about this book: Powered by TCPDF (www.tcpdf.org) Lesson 1: Introduction to CAMWorks 1 Lesson 1: Introduction to CAMWorks 1.1 Overview of the Lesson CAMWorks, developed by Geometric Americas Inc. (www.camworks.com/about), is a parametric, feature-based virtual machining software. By defining areas to be machined as machinable features, CAMWorks is able to apply more automation and intelligence into CNC (Computer Numerical Control) toolpath creation. This approach is more intuitive and follows the feature-based modeling concepts of computer-aided design (CAD) systems. Consequently, CAMWorks is fully integrated with CAD systems, such as SOLIDWORKS (and Solid Edge and CAMWorks Solids). Because of this integration, you can use the same user interface and solid models for design and later to create machining simulation. Such a tight integration completely eliminates file transfers using less-desirable standard file formats such as IGES, STEP, SAT, or Parasolid. Hence, the toolpaths generated are on the SOLIDWORKS part, not on an imported approximation. In addition, the toolpaths generated are associative with SOLIDWORKS parametric solid model. This means that if the solid model is changed, the toolpaths are changed automatically with minimal user intervention. In addition, CAMWorks is available as a standalone CAD/CAM package, with embedded CAMWorks Solids as an integrated solid modeler. One unique feature of CAMWorks is the AFR (automatic feature recognition) technology. -
A Comparative Study of Containers and Virtual Machines in Big Data Environment
A Comparative Study of Containers and Virtual Machines in Big Data Environment Qi Zhang1, Ling Liu2, Calton Pu2, Qiwei Dou3, Liren Wu3, and Wei Zhou3 1IBM Thomas J. Watson Research, New York, USA 2College of Computing, Georgia Institute of Technology, Georgia, USA 3Department of Computer Science, Yunnan University, Yunnan, China Abstract—Container technique is gaining increasing attention able to handle peak resources demands, even when there in recent years and has become an alternative to traditional exist free resources [37], [36]. Another example is the poor virtual machines. Some of the primary motivations for the reproducibility for scientific research when the workloads are enterprise to adopt the container technology include its convenience to encapsulate and deploy applications, lightweight moved from one cloud environment to the other [15]. Even operations, as well as efficiency and flexibility in resources though the workloads are the same, their dependent softwares sharing. However, there still lacks an in-depth and systematic could be slightly different, which leads to inconsistent results. comparison study on how big data applications, such as Spark Recently, container-based techniques, such as Docker[3], jobs, perform between a container environment and a virtual OpenVZ [8], and LXC(Linux Containers) [5], become machine environment. In this paper, by running various Spark applications with different configurations, we evaluate the two an alternative to traditional virtual machines because of environments from many interesting aspects, such as how their agility. The primary motivations for containers to be convenient the execution environment can be set up, what are increasingly adopted are their conveniency to encapsulate, makespans of different workloads running in each setup, how deploy, and isolate applications, lightweight operations, as well efficient the hardware resources, such as CPU and memory, are as efficiency and flexibility in resource sharing.