Disk-Level Behavioral Malware Detection
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Better Performance Through a Disk/Persistent-RAM Hybrid Design
The Conquest File System: Better Performance Through a Disk/Persistent-RAM Hybrid Design AN-I ANDY WANG Florida State University GEOFF KUENNING Harvey Mudd College PETER REIHER, GERALD POPEK University of California, Los Angeles ________________________________________________________________________ Modern file systems assume the use of disk, a system-wide performance bottleneck for over a decade. Current disk caching and RAM file systems either impose high overhead to access memory content or fail to provide mechanisms to achieve data persistence across reboots. The Conquest file system is based on the observation that memory is becoming inexpensive, which enables all file system services to be delivered from memory, except providing large storage capacity. Unlike caching, Conquest uses memory with battery backup as persistent storage, and provides specialized and separate data paths to memory and disk. Therefore, the memory data path contains no disk-related complexity. The disk data path consists of only optimizations for the specialized disk usage pattern. Compared to a memory-based file system, Conquest incurs little performance overhead. Compared to several disk-based file systems, Conquest achieves 1.3x to 19x faster memory performance, and 1.4x to 2.0x faster performance when exercising both memory and disk. Conquest realizes most of the benefits of persistent RAM at a fraction of the cost of a RAM-only solution. Conquest also demonstrates that disk-related optimizations impose high overheads for accessing memory content in a memory-rich environment. Categories and Subject Descriptors: D.4.2 [Operating Systems]: Storage Management—Storage Hierarchies; D.4.3 [Operating Systems]: File System Management—Access Methods and Directory Structures; D.4.8 [Operating Systems]: Performance—Measurements General Terms: Design, Experimentation, Measurement, and Performance Additional Key Words and Phrases: Persistent RAM, File Systems, Storage Management, and Performance Measurement ________________________________________________________________________ 1. -
System Calls System Calls
System calls We will investigate several issues related to system calls. Read chapter 12 of the book Linux system call categories file management process management error handling note that these categories are loosely defined and much is behind included, e.g. communication. Why? 1 System calls File management system call hierarchy you may not see some topics as part of “file management”, e.g., sockets 2 System calls Process management system call hierarchy 3 System calls Error handling hierarchy 4 Error Handling Anything can fail! System calls are no exception Try to read a file that does not exist! Error number: errno every process contains a global variable errno errno is set to 0 when process is created when error occurs errno is set to a specific code associated with the error cause trying to open file that does not exist sets errno to 2 5 Error Handling error constants are defined in errno.h here are the first few of errno.h on OS X 10.6.4 #define EPERM 1 /* Operation not permitted */ #define ENOENT 2 /* No such file or directory */ #define ESRCH 3 /* No such process */ #define EINTR 4 /* Interrupted system call */ #define EIO 5 /* Input/output error */ #define ENXIO 6 /* Device not configured */ #define E2BIG 7 /* Argument list too long */ #define ENOEXEC 8 /* Exec format error */ #define EBADF 9 /* Bad file descriptor */ #define ECHILD 10 /* No child processes */ #define EDEADLK 11 /* Resource deadlock avoided */ 6 Error Handling common mistake for displaying errno from Linux errno man page: 7 Error Handling Description of the perror () system call. -
INFOCOMP / Datasys 2017 International Expert Panel: Challenges on Web Semantic Mapping and Information Processing
INFOCOMP / DataSys 2017 International Expert Panel: Challenges on Web Semantic Mapping and Information Processing INFOCOMP / DataSys 2017 International Expert Panel: Challenges on Web Semantic Mapping and Information Processing June 28, 2017, Venice, Italy The Seventh International Conference on Advanced Communications and Computation (INFOCOMP 2017) The Seventh International Conference on Advances in Information Mining and Management (IMMM 2017) The Twelfth International Conference on Internet and Web Applications and Services (ICIW 2017) INFOCOMP/IMMM/ICIW (DataSys) June 25{29, 2017 - Venice, Italy INFOCOMP, June 25 { 29, 2017 - Venice, Italy INFOCOMP / DataSys 2017 International Expert Panel: Challenges on Web Semantic Mapping and Information Processing INFOCOMP / DataSys 2017 International Expert Panel: Challenges on Web Semantic Mapping and Information Processing INFOCOMP Expert Panel: Web Semantic Mapping & Information Proc. INFOCOMP Expert Panel: Web Semantic Mapping & Information Proc. Panelists Claus-Peter R¨uckemann (Moderator), Westf¨alischeWilhelms-Universit¨atM¨unster(WWU) / Leibniz Universit¨atHannover / North-German Supercomputing Alliance (HLRN), Germany Marc Jansen, University of Applied Sciences Ruhr West, Deutschland Fahad Muhammad, CSTB, Sophia Antipolis, France Kiyoshi Nagata, Daito Bunka University, Japan Claus-Peter R¨uckemann, WWU M¨unster/ Leibniz Universit¨atHannover / HLRN, Germany INFOCOMP 2017: http://www.iaria.org/conferences2017/INFOCOMP17.html Program: http://www.iaria.org/conferences2017/ProgramINFOCOMP17.html -
GPU Developments 2018
GPU Developments 2018 2018 GPU Developments 2018 © Copyright Jon Peddie Research 2019. All rights reserved. Reproduction in whole or in part is prohibited without written permission from Jon Peddie Research. This report is the property of Jon Peddie Research (JPR) and made available to a restricted number of clients only upon these terms and conditions. Agreement not to copy or disclose. This report and all future reports or other materials provided by JPR pursuant to this subscription (collectively, “Reports”) are protected by: (i) federal copyright, pursuant to the Copyright Act of 1976; and (ii) the nondisclosure provisions set forth immediately following. License, exclusive use, and agreement not to disclose. Reports are the trade secret property exclusively of JPR and are made available to a restricted number of clients, for their exclusive use and only upon the following terms and conditions. JPR grants site-wide license to read and utilize the information in the Reports, exclusively to the initial subscriber to the Reports, its subsidiaries, divisions, and employees (collectively, “Subscriber”). The Reports shall, at all times, be treated by Subscriber as proprietary and confidential documents, for internal use only. Subscriber agrees that it will not reproduce for or share any of the material in the Reports (“Material”) with any entity or individual other than Subscriber (“Shared Third Party”) (collectively, “Share” or “Sharing”), without the advance written permission of JPR. Subscriber shall be liable for any breach of this agreement and shall be subject to cancellation of its subscription to Reports. Without limiting this liability, Subscriber shall be liable for any damages suffered by JPR as a result of any Sharing of any Material, without advance written permission of JPR. -
CTL RFP Proposal
State of Maine Department of Education in coordination with the National Association of State Procurement Officials PROPOSAL COVER PAGE RFP # 201210412 MULTI-STATE LEARNING TECHNOLOGY INITIATIVE Bidder’s Organization Name: CTL Chief Executive - Name/Title: Erik Stromquist / COO Tel: 800.642.3087 x 212 Fax: 503.641.5586 E-mail: [email protected] Headquarters Street Address: 3460 NW Industrial St. Headquarters City/State/Zip: Portland, OR 97210 (provide information requested below if different from above) Lead Point of Contact for Proposal - Name/Title: Michael Mahanay / GM, Sales & Marketing Tel: 800.642.3087 x 205 Fax: 503.641.5586 E-mail: [email protected] Street Address: 3460 NW Industrial St. City/State/Zip: Portland, OR 97219 Proposed Cost: $294/yr. The proposed cost listed above is for reference purposes only, not evaluation purposes. In the event that the cost noted above does not match the Bidder’s detailed cost proposal documents, then the information on the cost proposal documents will take precedence. This proposal and the pricing structure contained herein will remain firm for a period of 180 days from the date and time of the bid opening. No personnel on the multi-state Sourcing Team or any other involved state agency participated, either directly or indirectly, in any activities relating to the preparation of the Bidder’s proposal. No attempt has been made or will be made by the Bidder to induce any other person or firm to submit or not to submit a proposal. The undersigned is authorized to enter into contractual obligations on behalf of the above-named organization. -
May 28, 2016 3:46 WSPC/INSTRUCTION FILE Output
May 28, 2016 3:46 WSPC/INSTRUCTION FILE output International Journal of Semantic Computing c World Scientific Publishing Company Methods and Resources for Computing Semantic Relatedness Yue Feng Ryerson University Toronto, Ontario, Canada [email protected] Ebrahim Bagheri Ryerson University Toronto, Ontario, Canada [email protected] Received (Day Month Year) Revised (Day Month Year) Accepted (Day Month Year) Semantic relatedness (SR) is defined as a measurement that quantitatively identifies some form of lexical or functional association between two words or concepts based on the contextual or semantic similarity of those two words regardless of their syntactical differences. Section 1 of the entry outlines the working definition of semantic related- ness and its applications and challenges. Section 2 identifies the knowledge resources that are popular among semantic relatedness methods. Section 3 reviews the primary measurements used to calculate semantic relatedness. Section 4 reviews the evaluation methodology which includes gold standard dataset and methods. Finally, Section 5 in- troduces further reading. In order to develop appropriate semantic relatedness methods, there are three key aspects that need to be examined: 1) the knowledge resources that are used as the source for extracting semantic relatedness; 2) the methods that are used to quantify semantic relatedness based on the adopted knowledge resource; and 3) the datasets and methods that are used for evaluating semantic relatedness techniques. The first aspect involves the selection of knowledge bases such as WordNet or Wikipedia. Each knowledge base has its merits and downsides which can directly affect the accurarcy and the coverage of the semantic relatedness method. The second aspect relies on different methods for utilizing the beforehand selected knowledge resources, for example, methods that depend on the path between two words, or a vector representation of the word. -
Mass-Producing Your Certified Cluster Solutions
Mass-Producing Your Certified Cluster Solutions White Paper The Intel® Cluster Ready program is designed to help you make the most of your engineering Intel® Cluster Ready resources. The program enables you to sell several different types of clusters from each solution High-Performance you design, by varying the hardware while maintaining the same software stack. You can gain a Computing broader range of cluster products to sell without engineering each one “from scratch.” As long as the software stack remains the same and works on each one of the hardware configurations, you can be confident that your clusters will interoperate with registered Intel Cluster Ready applications. Your customers can purchase your clusters with that same confidence, knowing they’ll be able to get their applications up and running quickly on your systems. Intel® Cluster Ready: Mass-Producing Your Certified Cluster Solutions Table of Contents Overview of Production-Related Activities � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 3 Creating and certifying recipes � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 3 Maintaining recipes � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 3 Mass-producing recipes � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � -
Intel Announces X25-V 40GB Solid-State Drive for $125 15 March 2010
Intel Announces X25-V 40GB Solid-State Drive for $125 15 March 2010 applications and their favorite gaming application, such as Dragon Age: Origins, and experience up to 43 percent faster overall system performance or 86 percent improvement in their gaming experience. The SSD also speeds operations such as system start up, the opening of applications and files or resuming from standby. The Intel X25-V SSD The Intel X25-V features 40GB of 34nm NAND flash memory. This non-volatile memory retains data, even when the power is turned off, and is used in applications such as smartphones, Intel Corp. announced today a new addition to its personal music players, memory cards or SSDs for award-winning lineup of high-performance solid- fast and reliable storage of data. SSD benefits over state drives (SSDs): the Intel X25-V Value SATA a traditional HDD include higher performance, SSD. Priced at $125, the 40 gigabyte (GB) drive is battery saving and ruggedness. "Adding the Intel aimed at value segment netbooks and dual- X25-V to our existing family of high-performance drive/boot drive desktop set-ups to offer users the SSDs gives our resellers a full range of high- performance and reliability advantages of solid- performing, quality SSDs for notebook upgrades, state computing at an affordable, entry-level price. dual-drive desktop set ups or embedded applications," said Pete Hazen, director of SSDs can replace or coexist with traditional hard marketing for the Intel NAND Solutions Group. disk drives (HDDs). With no movable parts or spinning platters, SSDs are more reliable and The 40GB Intel X25-V complements Intel's higher higher performing than HDDs. -
An Extensible Platform for Semantic Classification and Retrieval of Multimedia Resources
An extensible platform for semantic classification and retrieval of multimedia resources Paolo Pellegrino, Fulvio Corno Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129, Torino, Italy {paolo.pellegrino, fulvio.corno}@polito.it Abstract and seem very promising, but are still mostly exploited for textual resources only. In effect, text is the simplest form This paper introduces a possible solution to the problem of knowledge representation which can be both of semantic indexing, searching and retrieving heterogeneous understood by humans and quickly processed by resources, from textual as in most of modern search engines, machines. So, while text itself is already efficaciously to multimedia. The idea of “anchor” as information unit is used as knowledge representation for machines, digital here introduced to view resources from different perspectives multimedia resources like images and videos cannot in and to access existing resources and metadata archives. general be used as they are for efficient classification and Moreover, the platform uses an ontology as a conceptual retrieval. Some intensive processing or manual metadata representation of a well-defined domain in order to creation is necessary to extract and associate semantic semantically classify and retrieve anchors (and the related information to a given multimedia resource. resources). Specifically, the architecture of the proposed platform aims at being as modular and easily extensible as In this context, the proposed work aims at providing a possible, in order to permit the inclusion of state-of-the-art flexible, lightweight and ready-to-use platform for the techniques for the classification and retrieval of multimedia semantic classification, search and retrieval of resources. -
Career Center Annual Report
CAREER CENTER ANNUAL REPORT 2019–2020 th Best Return on 5Investment 1 73 % Students Utilized Mines % Career Services 95 Positive Outcomes Rate2,3 IN A YEAR OF UNPRECEDENTED CHALLENGES, 1,200 Organizations THE STUDENTS, FACULTY, AND STAFF OF Recruited or MINES HAVE RISEN TO THE OCCASION, Hired at Mines REFLECTING THE INDOMITABLE OREDIGGER SPIRIT OF RESILIENCE THAT IS AT THE HEART OF THE MINES EXPERIENCE. THE CAREER Average CENTER HAS BEEN NIMBLE IN ADAPTING TO Starting $ Salary 3 NEW CIRCUMSTANCES AND CHANGING NEEDS. k 1 Among public universities nationwide 2 Employed79 in industry, government, military, www.payscale.com/college-salary-report/best-schools-by- continuing education, or international students type/bachelors/public-schools returning to their home countries. 3 Combined BS, MS, and PhD grads “IT IS ABSOLUTELY AMAZING TO BE ABLE TO PUT ON GLOVES AND TOUCH A VEHICLE THAT IS GOING INTO SPACE, LET ALONE CONTRIBUTE TO A TEAM OF INCREDIBLY SMART INDIVIDUALS TO HELP BUILD THIS VEHICLE AND GET IT INTO SPACE.” NADIA SMITH Mechanical Engineering REACHING BS, Class of 2021 FOR THE STARS GR ADUATED $ STUDENT 79K Average Starting OUTCOMES Salary 1 % A Record 22 Graduates Continue with 1,548 Advanced Education3 Total Graduates The Class of 2020 graduated into a labor market unlike any in recent history with a pandemic impacting course delivery and an unexpected, % sudden shift in the economy. Despite these Positive challenges, Mines graduates saw positive outcomes, Outcomes similar to prior years—a testament to their enduring 95 1,2 Rate value as professionals and graduates. 2 Employed in industry, government, military, continuing education, 1 BS, MS, and PhD grads or international students returning to their home countries. -
NVM Express and the PCI Express* SSD Revolution SSDS003
NVM Express and the PCI Express* SSD Revolution Danny Cobb, CTO Flash Memory Business Unit, EMC Amber Huffman, Sr. Principal Engineer, Intel SSDS003 Agenda • NVM Express (NVMe) Overview • New NVMe Features in Enterprise & Client • Driver Ecosystem for NVMe • NVMe Interoperability and Plugfest Plans • EMC’s Perspective: NVMe Use Cases and Proof Points The PDF for this Session presentation is available from our Technical Session Catalog at the end of the day at: intel.com/go/idfsessions URL is on top of Session Agenda Pages in Pocket Guide 2 Agenda • NVM Express (NVMe) Overview • New NVMe Features in Enterprise & Client • Driver Ecosystem for NVMe • NVMe Interoperability and Plugfest Plans • EMC’s Perspective: NVMe Use Cases and Proof Points 3 NVM Express (NVMe) Overview • NVM Express is a scalable host controller interface designed for Enterprise and client systems that use PCI Express* SSDs • NVMe was developed by industry consortium of 80+ members and is directed by a 13-company Promoter Group • NVMe 1.0 was published March 1, 2011 • Product introductions later this year, first in Enterprise 4 Technical Basics • The focus of the effort is efficiency, scalability and performance – All parameters for 4KB command in single 64B DMA fetch – Supports deep queues (64K commands per Q, up to 64K queues) – Supports MSI-X and interrupt steering – Streamlined command set optimized for NVM (6 I/O commands) – Enterprise: Support for end-to-end data protection (i.e., DIF/DIX) – NVM technology agnostic 5 NVMe = NVM Express NVMe Command Execution 7 1 -
Semantic Computing
SEMANTIC COMPUTING 10651_9789813227910_TP.indd 1 24/7/17 1:49 PM World Scientific Encyclopedia with Semantic Computing and Robotic Intelligence ISSN: 2529-7686 Published Vol. 1 Semantic Computing edited by Phillip C.-Y. Sheu 10651 - Semantic Computing.indd 1 27-07-17 5:07:03 PM World Scientific Encyclopedia with Semantic Computing and Robotic Intelligence – Vol. 1 SEMANTIC COMPUTING Editor Phillip C-Y Sheu University of California, Irvine World Scientific NEW JERSEY • LONDON • SINGAPORE • BEIJING • SHANGHAI • HONG KONG • TAIPEI • CHENNAI • TOKYO 10651_9789813227910_TP.indd 2 24/7/17 1:49 PM World Scientific Encyclopedia with Semantic Computing and Robotic Intelligence ISSN: 2529-7686 Published Vol. 1 Semantic Computing edited by Phillip C.-Y. Sheu Catherine-D-Ong - 10651 - Semantic Computing.indd 1 22-08-17 1:34:22 PM Published by World Scientific Publishing Co. Pte. Ltd. 5 Toh Tuck Link, Singapore 596224 USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE Library of Congress Cataloging-in-Publication Data Names: Sheu, Phillip C.-Y., editor. Title: Semantic computing / editor, Phillip C-Y Sheu, University of California, Irvine. Other titles: Semantic computing (World Scientific (Firm)) Description: Hackensack, New Jersey : World Scientific, 2017. | Series: World Scientific encyclopedia with semantic computing and robotic intelligence ; vol. 1 | Includes bibliographical references and index. Identifiers: LCCN 2017032765| ISBN 9789813227910 (hardcover : alk. paper) | ISBN 9813227915 (hardcover : alk. paper) Subjects: LCSH: Semantic computing. Classification: LCC QA76.5913 .S46 2017 | DDC 006--dc23 LC record available at https://lccn.loc.gov/2017032765 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library.