A Technique for Enabling and Supporting Debugging of Field Failures
|
|
|
- Brent Wilkerson
- 6 years ago
- Views:
Transcription
1 A Technique for Enabling and Supporting Debugging of Field Failures James Clause and Alessandro Orso Georgia Institute of Technology This work was supported in part by NSF awards CCF and CCR to Georgia Tech. 1
2
3
4
5
6
7 3
8 Field failures: Anomalous behavior (or crashes) of deployed software that occur on user machines 3
9 4
10 4 Crash logs
11 Crash logs User-provided information 4
12 Our solution 5
13 Our solution Record 5
14 Our solution Record Replay 5
15 Our solution Record Replay Minimize 5
16 Our solution Record Replay Minimize Debug 5
17 Usage Scenario In house Develop In the field Record (on line)!/" Replay / Debug Replay / Minimize (off line) Execution repository 6
18 Existing record / replay approaches Deterministic debugging (e.g. Chen et al. 01, King et al. 05, Narayanasamy et al. 05, Netzer and Weaver 94, Srinivasan et al. 04, VMWare) Regression testing (e.g. Elbaum et al. 06, Orso et al. 06, Orso and Kennedy 05, Saff et al. 05, Mercury WinRunner) Replay an entire execution by recording every component of an application Replay only a portion of an execution by recording events for specific subsystems Both types of techniques are not amenable to minimization and may cause unacceptable overhead 7
19 Outline Motivation & background Our technique record replay minimization Empirical evaluation Conclusion & future work 8
20 Record & Replay Goal: develop an approach that has low overhead and is amenable to minimization Key insight: avoid focusing on low-level (internal) events expensive (large number of events) not amenable to minimization (high interdependence) 9
21 Record & Replay Goal: develop an approach that has low overhead and is amenable to minimization Key insight: avoid focusing on low-level (internal) events expensive (large number of events) not amenable to minimization (high interdependence) Focus on high-level (external) interactions with the environment efficient (fewer, more expensive interactions) amenable to minimization (low interdependence) 10
22 Environment interactions 11
23 Environment interactions Streams 11
24 Environment interactions Streams Files 11
25 Environment interactions Streams Files 11
26 Environment interactions Streams Files Interaction events: FILE interaction with a file POLL checks for availability of data on a stream PULL read data from a stream 11
27 Event log: Environment data (streams): Environment data (files): 12
28 Event log: FILE foo.1 Environment data (streams): Environment data (files): foo.1 12
29 Event log: FILE foo.1 Environment data (streams): Environment data (files): foo.1 12
30 Event log: FILE foo.1 POLL KEYBOARD NOK Environment data (streams): Environment data (files): foo.1 12
31 Event log: FILE foo.1 POLL KEYBOARD NOK Environment data (streams): Environment data (files): foo.1 12
32 Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK Environment data (streams): KEYBOARD: {5680} Environment data (files): foo.1 12
33 Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK Environment data (streams): KEYBOARD: {5680} Environment data (files): foo.1 12
34 Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK PULL KEYBOARD 5 Environment data (streams): KEYBOARD: {5680}hello Environment data (files): foo.1 12
35 Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK PULL KEYBOARD 5 Environment data (streams): KEYBOARD: {5680}hello Environment data (files): foo.1 12
36 Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK PULL KEYBOARD 5 POLL NETWORK OK Environment data (streams): KEYBOARD: {5680}hello NETWORK: {3405} Environment data (files): foo.1 12
37 Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK PULL KEYBOARD 5 POLL NETWORK OK Environment data (streams): KEYBOARD: {5680}hello NETWORK: {3405} Environment data (files): foo.1 12
38 Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK PULL KEYBOARD 5 POLL NETWORK OK Environment data (streams): KEYBOARD: {5680}hello NETWORK: {3405} Environment data (files): foo.1 12
39 Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK PULL KEYBOARD 1 POLL NETWORK OK PULL NETWORK 1024 FILE bar.1 POLL NETWORK NOK POLL NETWORK OK FILE foo.2... PULL NETWORK 1024 FILE foo.2 POLL KEYBOARD NOK... Environment data (streams): KEYBOARD: {5680}hello {4056}c {300}... NETWORK: {3405}<html><body>... {202}... Environment data (files): foo.1 foo.2 bar.1 13
40 Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK PULL KEYBOARD 1 POLL NETWORK OK PULL NETWORK 1024 FILE bar.1 POLL NETWORK NOK POLL NETWORK OK FILE foo.2... PULL NETWORK 1024 FILE foo.2 POLL KEYBOARD NOK... Environment data (streams): KEYBOARD: {5680}hello {4056}c {300}... NETWORK: {3405}<html><body>... {202}... Environment data (files): foo.1 foo.2 bar.1 14
41 Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK PULL KEYBOARD 1 POLL NETWORK OK PULL NETWORK 1024 FILE bar.1 POLL NETWORK NOK POLL NETWORK OK FILE foo.2... PULL NETWORK 1024 FILE foo.2 POLL KEYBOARD NOK... Environment data (streams): KEYBOARD: {5680}hello {4056}c {300}... NETWORK: {3405}<html><body>... {202}... Environment data (files): foo.1 foo.2 bar.1 14
42 Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK PULL KEYBOARD 1 POLL NETWORK OK PULL NETWORK 1024 FILE bar.1 POLL NETWORK NOK POLL NETWORK OK FILE foo.2... PULL NETWORK 1024 FILE foo.2 POLL KEYBOARD NOK... Environment data (streams): KEYBOARD: {5680}hello {4056}c {300}... NETWORK: {3405}<html><body>... {202}... Environment data (files): foo.1 foo.2 bar.1 14
43 Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK PULL KEYBOARD 1 POLL NETWORK OK PULL NETWORK 1024 FILE bar.1 POLL NETWORK NOK POLL NETWORK OK FILE foo.2... PULL NETWORK 1024 FILE foo.2 POLL KEYBOARD NOK... Environment data (streams): KEYBOARD: {5680}hello {4056}c {300}... NETWORK: {3405}<html><body>... {202}... Environment data (files): foo.1 foo.2 bar.1 14
44 Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK PULL KEYBOARD 1 POLL NETWORK OK PULL NETWORK 1024 FILE bar.1 POLL NETWORK NOK POLL NETWORK OK FILE foo.2... PULL NETWORK 1024 FILE foo.2 POLL KEYBOARD NOK... Environment data (streams): KEYBOARD: {5680}hello {4056}c {300}... NETWORK: {3405}<html><body>... {202}... Environment data (files): foo.1 foo.2 bar.1 14
45 Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK PULL KEYBOARD 1 POLL NETWORK OK PULL NETWORK 1024 FILE bar.1 POLL NETWORK NOK POLL NETWORK OK FILE foo.2... PULL NETWORK 1024 FILE foo.2 POLL KEYBOARD NOK... Environment data (streams): KEYBOARD: {5680}hello {4056}c {300}... NETWORK: {3405}<html><body>... {202}... Environment data (files): foo.1 foo.2 bar.1 14
46 Minimize Goal: focus debugging effort 15
47 Minimize Goal: focus debugging effort Execution recording 15
48 Minimize Goal: focus debugging effort Execution recording Time minimization! 15
49 Minimize Goal: focus debugging effort Execution recording Execution recording Time minimization! 15
50 Minimize Goal: focus debugging effort Execution recording Execution recording Time minimization! Data minimization 15
51 Minimize Execution recording Goal: focus debugging effort Execution recording Execution recording Time minimization! Data minimization 15
52 Minimize: time Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK PULL KEYBOARD 1 POLL NETWORK OK PULL NETWORK 1024 FILE bar.1 POLL NETWORK NOK POLL NETWORK OK FILE foo.2 PULL NETWORK 1024 FILE foo.2 POLL KEYBOARD NOK Environment data (streams): KEYBOARD: {5680}hello {4056}c {300}... NETWORK: {3405}<html><body>... {202}... Environment data (files): 16
53 Minimize: time Event log: FILE foo.1 POLL KEYBOARD NOK POLL KEYBOARD OK PULL KEYBOARD 1 POLL NETWORK OK PULL NETWORK 1024 FILE bar.1 POLL NETWORK NOK POLL NETWORK OK FILE foo.2 PULL NETWORK 1024 FILE foo.2 POLL KEYBOARD NOK Environment data (streams): KEYBOARD: {5680}hello {4056}c {300}... NETWORK: {3405}<html><body>... {202}... Environment data (files): 16
54 Minimize: time Event log: FILE foo.1 POLL KEYBOARD OK PULL KEYBOARD 1 POLL NETWORK OK PULL NETWORK 1024 FILE bar.1 POLL NETWORK OK FILE foo.2 PULL NETWORK 1024 FILE foo.2 Environment data (streams): KEYBOARD: {5680}hello {4056}c {300}... NETWORK: {3405}<html><body>... {202}... Environment data (files): 17
55 Minimize: time Event log: FILE foo.1 POLL KEYBOARD OK PULL KEYBOARD 1 POLL NETWORK OK PULL NETWORK 1024 FILE bar.1 POLL NETWORK OK FILE foo.2 PULL NETWORK 1024 FILE foo.2 Environment data (streams): KEYBOARD: {5680}hello {4056}c {300}... NETWORK: {3405}<html><body>... {202}... Environment data (files): 17
56 Minimize: data Data minimization Whole entities Environment Chunks Atoms 18
57 Minimize: data Data minimization Whole entities Environment Chunks Atoms 18
58 Minimize: data Data minimization Whole entities! Environment Chunks Atoms 18
59 Minimize: data Data minimization Whole entities Environment Chunks Atoms 18
60 Minimize: data Data minimization Whole entities Environment Chunks Atoms 18
61 Minimize: data Data minimization Whole entities! Environment Chunks Atoms 18
62 Minimize: data Data minimization Whole entities Environment Chunks Atoms 18
63 Minimize: data Data minimization Whole entities Environment Chunks Atoms 18
64 Minimize: data Data minimization Whole entities Environment Chunks Atoms 18
65 Minimize: data Data minimization Whole entities Environment Chunks Atoms 18
66 Minimize: data Data minimization Whole entities Environment Chunks Atoms 18
67 Minimize: data Data minimization Whole entities Environment Chunks Atoms 18
68 Minimize: data Data minimization Whole entities Environment Chunks Atoms 18
69 Minimize: data Data minimization Whole entities Environment Chunks Atoms 18
70 The tool: ADDA Assisting the Debugging of Deployed Applications Record and Replay: Works on x86 (c-lib based) binaries Based on dynamic instrumentation (Pin) Maps c-library calls to interaction events Minimization: Set of extensible scripts 19
71 Limitations Two main limitations: Technique: May not replay non-deterministic failures Implementation: Does not handle window system events (yet) 20
72 Empirical evaluation Research questions Can ADDA produce minimized executions that can be used to debug the original failure? How much overhead does ADDA impose? Subject: Pine widely-used / news client Data: Two real field failures from Pine s history Set of 20 failing executions, 10 per failure 21
73 Empirical evaluation Research questions Can ADDA produce minimized executions that can be used to debug the original failure? How much overhead does ADDA impose? Subject: Pine widely-used / news client Data: Two real field failures from Pine s history Set of 20 failing executions, 10 per failure 22
74 Minimization results Average value after minimization 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Header-color fault Address book fault # entities streams size files size 23
75 Minimization results Average value after minimization 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Header-color fault # entities streams size files size 23 Address book fault Moreover, these results are conservative: recorded executions only contain the minimal amount of data needed to perform an action.
76 Minimization results Average value after minimization 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Header-color fault Overhead # entities streams size files size 23 Address book fault Offline: less than 75 minutes for minimization Online: negligible overhead while recording Moreover, these results are conservative: recorded executions only contain the minimal amount of data needed to perform an action.
77 Specific Example: Address Book Failure Complete execution 34 entities (files and streams) 800kb Minimized execution 5 partial entities (4 files,1 stream) 72kb
78 Future work More studies: additional applications and real users Extend technique / implementation Support windowing system Investigate ad-hoc minimization algorithms Include non-deterministic events (if needed) 25
79 Conclusions Novel approach that supports debugging field failures Prototype implementation for x86 binaries Preliminary empirical evaluation: for the cases considered, our technique can 1. minimize failing executions, 2. preserve their failing behavior, and 3. impose low overhead on users 26
80 Questions? 27
Effective Memory Protection Using Dynamic Tainting
Effective Memory Protection Using Dynamic Tainting James Clause Alessandro Orso (software) and Ioanis Doudalis Milos Prvulovic (hardware) College of Computing Georgia Institute of Technology Supported
MonDe: Safe Updating through Monitored Deployment of New Component Versions
MonDe: Safe Updating through Monitored Deployment of New Component Versions Alessandro Orso Georgia Institute of Technology Jonathan Cook New Mexico State University SPARC This work was supported in part
Scaling Regression Testing to Large Software Systems
Scaling Regression Testing to Large Software Systems Alessandro Orso Co-authors: Nanjuan Shi, Mary Jean Harrold College of Computing Georgia Institute of Technology Supported in part by National Science
(*Tiered Storage ARchitecture)
TSAR*: A Two Tier Sensor Storage Architecture Using Interval Skip Graphs (*Tiered Storage ARchitecture) Peter Desnoyers, Deepak Ganesan, and Prashant Shenoy University of Massachusetts, Amherst Department
Command-form Coverage for Testing DB Applications
Command-form Coverage for Testing DB Applications Alessandro Orso William G.J. Halfond Georgia Institute of Technology Supported by NSF awards CCR- 0205422 and CCR-0306372 to GA Tech and by DHS and US
An Empirical Study of High Availability in Stream Processing Systems
An Empirical Study of High Availability in Stream Processing Systems Yu Gu, Zhe Zhang, Fan Ye, Hao Yang, Minkyong Kim, Hui Lei, Zhen Liu Stream Processing Model software operators (PEs) Ω Unexpected machine
A Serializability Violation Detector for Shared-Memory Server Programs
A Serializability Violation Detector for Shared-Memory Server Programs Min Xu Rastislav Bodík Mark Hill University of Wisconsin Madison University of California, Berkeley Serializability Violation Detector:
Migratory TCP (MTCP) Transport Layer Support for Highly Available Network Services
Migratory TCP (MTCP) Transport Layer Support for Highly Available Network Services DisCo Lab Division of Computer and Information Sciences Rutgers University Nov. 29, 2001 CONS Light seminar 1 The People
Using Positive Tainting and Syntax-Aware Evaluation to Counter SQL Injection Attacks
Using Positive Tainting and Syntax-Aware Evaluation to Counter SQL Injection Attacks Alex Orso with William Halfond and Pete Manolios Georgia Institute of Technology {orso whalfond manolios}@cc.gatech.edu
A Case Study of Automatically Creating Test Suites from Web Application Field Data. Sara Sprenkle, Emily Gibson, Sreedevi Sampath, and Lori Pollock
A Case Study of Automatically Creating Test Suites from Web Application Field Data Sara Sprenkle, Emily Gibson, Sreedevi Sampath, and Lori Pollock Evolving Web Applications Code constantly changing Fix
On-line Anomaly Detection of Deployed Software: A Statistical Machine Learning Approach
On-line Anomaly Detection of Deployed Software: A Statistical Machine Learning Approach George K. Baah, Alexander Gray, and Mary Jean Harrold College of Computing Georgia Institute of Technology Atlanta,
A Scalable Approach to User-session-based Testing of Web Applications through Concept Analysis
A Scalable Approach to User-session-based Testing of Web Applications through Concept Analysis Sreedevi Sampath, University of Delaware Valentin Mihaylov, Drexel University Amie Souter, Drexel University
HARDWARE SUPPORT FOR SOFTWARE DEBUGGING IS CRITICAL. HARDWARE SUPPORT IS NECESSARY TO OBSERVE AND CAPTURE, WITH LITTLE OR NO
BUGNET: RECORDING APPLICATION-LEVEL EXECUTION FOR DETERMINISTIC REPLAY DEBUGGING WITH SOFTWARE S INCREASING COMPLEXITY, PROVIDING EFFICIENT HARDWARE SUPPORT FOR SOFTWARE DEBUGGING IS CRITICAL. HARDWARE
Silver + TestWeaver Tools for Simulation-Based Design System Test and Validation
Silver + TestWeaver Tools for Simulation-Based Design System Test and Validation Mugur Tatar QTronic GmbH, Berlin SESP 2008, Noordwijk Company Profile QTronic: Tools & Engineering for Simulation-Based
Deterministic Process Groups in
Deterministic Process Groups in Tom Bergan Nicholas Hunt, Luis Ceze, Steven D. Gribble University of Washington A Nondeterministic Program global x=0 Thread 1 Thread 2 t := x x := t + 1 t := x x := t +
Lessons Learned During the Development of the CapoOne Deterministic Multiprocessor Replay System
Lessons Learned During the Development of the CapoOne Deterministic Multiprocessor System Pablo Montesinos, Matthew Hicks, Wonsun Ahn, Samuel T. King and Josep Torrellas Department of Computer Science
Production-Run Software Failure Diagnosis via Hardware Performance Counters. Joy Arulraj, Po-Chun Chang, Guoliang Jin and Shan Lu
Production-Run Software Failure Diagnosis via Hardware Performance Counters Joy Arulraj, Po-Chun Chang, Guoliang Jin and Shan Lu Motivation Software inevitably fails on production machines These failures
Chimera: Hybrid Program Analysis for Determinism
Chimera: Hybrid Program Analysis for Determinism Dongyoon Lee, Peter Chen, Jason Flinn, Satish Narayanasamy University of Michigan, Ann Arbor - 1 - * Chimera image from http://superpunch.blogspot.com/2009/02/chimera-sketch.html
F3: Fault Localization for Field Failures
F3: Fault Localization for Field Failures Wei Jin Georgia Institute of Technology, USA weijin@gatech.edu Alessandro Orso Georgia Institute of Technology, USA orso@gatech.edu ABSTRACT Reproducing and debugging
Samsara: Efficient Deterministic Replay in Multiprocessor. Environments with Hardware Virtualization Extensions
Samsara: Efficient Deterministic Replay in Multiprocessor Environments with Hardware Virtualization Extensions Shiru Ren, Le Tan, Chunqi Li, Zhen Xiao, and Weijia Song June 24, 2016 Table of Contents 1
Automated test of the AMG Speedshift DCT control software
Automated test of the AMG Speedshift DCT control software M. Tatar QTronic GmbH, Berlin R. Schaich, T. Breitinger Mercedes-AMG GmbH, Affalterbach 9th International CTI Symposium Berlin, 2010 Outline Motivation
Lecture 10. Dynamic Analysis
Lecture 10. Dynamic Analysis Wei Le Thank Xiangyu Zhang, Michael Ernst, Tom Ball for Some of the Slides Iowa State University 2014.11 Outline What is dynamic analysis? Instrumentation Analysis Representing
Test Automation. 20 December 2017
Test Automation 20 December 2017 The problem of test automation Testing has repetitive components, so automation is justified The problem is cost-benefit evaluation of automation [Kaner] Time for: test
Improving Efficiency and Scalability of Formula-based Debugging
Improving Efficiency and Scalability of Formula-based Debugging Wei Jin and Alessandro Orso Georgia Institute of Technology {weijin orso}@gatech.edu arxiv:1409.1989v1 [cs.se] 6 Sep 2014 Abstract Formula-based
InsectJ: A Generic Instrumentation Framework for Collecting Dynamic Information within Eclipse
InsectJ: A Generic Instrumentation Framework for Collecting Dynamic Information within Eclipse Arjan Seesing and Alessandro Orso Georgia Institute of Technology This work was supported in part by an IBM
Separating Access Control Policy, Enforcement, and Functionality in Extensible Systems. Robert Grimm University of Washington
Separating Access Control Policy, Enforcement, and Functionality in Extensible Systems Robert Grimm University of Washington Extensions Added to running system Interact through low-latency interfaces Form
Wind River. All Rights Reserved.
1 Using Simulation to Develop and Maintain a System of Connected Devices Didier Poirot Simics Technical Account Manager THE CHALLENGES OF DEVELOPING CONNECTED ELECTRONIC SYSTEMS 3 Mobile Networks Update
Engineering Fault-Tolerant TCP/IP servers using FT-TCP. Dmitrii Zagorodnov University of California San Diego
Engineering Fault-Tolerant TCP/IP servers using FT-TCP Dmitrii Zagorodnov University of California San Diego Motivation Reliable network services are desirable but costly! Extra and/or specialized hardware
Simulative Evaluation of Internet Protocol Functions
Simulative Evaluation of Internet Protocol Functions Introduction Course Objectives & Introduction Performance Evaluation & Simulation A Manual Simulation Example Resources http://www.tu-ilmenau.de/fakia/simpro.html
Mobile and Heterogeneous databases Distributed Database System Transaction Management. A.R. Hurson Computer Science Missouri Science & Technology
Mobile and Heterogeneous databases Distributed Database System Transaction Management A.R. Hurson Computer Science Missouri Science & Technology 1 Distributed Database System Note, this unit will be covered
Path Optimization in Stream-Based Overlay Networks
Path Optimization in Stream-Based Overlay Networks Peter Pietzuch, prp@eecs.harvard.edu Jeff Shneidman, Jonathan Ledlie, Mema Roussopoulos, Margo Seltzer, Matt Welsh Systems Research Group Harvard University
CrashMonkey: A Framework to Systematically Test File-System Crash Consistency. Ashlie Martinez Vijay Chidambaram University of Texas at Austin
CrashMonkey: A Framework to Systematically Test File-System Crash Consistency Ashlie Martinez Vijay Chidambaram University of Texas at Austin Crash Consistency File-system updates change multiple blocks
MCTS Guide to Microsoft Windows Server 2008 Applications Infrastructure Configuration (Exam # ) Chapter One Introducing Windows Server 2008
MCTS Guide to Microsoft Windows Server 2008 Applications Infrastructure Configuration (Exam # 70-643) Chapter One Introducing Windows Server 2008 Objectives Distinguish among the different Windows Server
Optimistic Hybrid Analysis
Optimistic Hybrid Analysis David Devecsery, Peter M. Chen, Satish Narayanasamy, Jason Flinn University of Michigan Motivation Problem Dynamic analysis is essential Memory errors Buffer overflow, use-after-free,
Improving Error Checking and Unsafe Optimizations using Software Speculation. Kirk Kelsey and Chen Ding University of Rochester
Improving Error Checking and Unsafe Optimizations using Software Speculation Kirk Kelsey and Chen Ding University of Rochester Outline Motivation Brief problem statement How speculation can help Our software
JiST: Java in Simulation Time
JiST: Java in Simulation Time Transparent Parallel and Optimistic Execution of Discrete Event Simulations (PDES) of Mobile Ad hoc Networks (MANETs) Rimon Barr barr@cs.cornell.edu Wireless Network Lab Cornell
Motivation. Overview. Scalable Dynamic Analysis for Automated Fault Location and Avoidance. Rajiv Gupta. Program Execution
Motivation Scalable Dynamic Analysis for Automated Fault Location and Avoidance Rajiv Gupta Funded by NSF grants from CPA, CSR, & CRI programs and grants from Microsoft Research Software bugs cost the
Scalable Replay with Partial-Order Dependencies for Message-Logging Fault Tolerance
Scalable Replay with Partial-Order Dependencies for Message-Logging Fault Tolerance Jonathan Lifflander*, Esteban Meneses, Harshitha Menon*, Phil Miller*, Sriram Krishnamoorthy, Laxmikant V. Kale* jliffl2@illinois.edu,
The Google File System
The Google File System Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung Google* 정학수, 최주영 1 Outline Introduction Design Overview System Interactions Master Operation Fault Tolerance and Diagnosis Conclusions
DeLorean: Recording and Deterministically Replaying Shared Memory Multiprocessor Execution Efficiently
: Recording and Deterministically Replaying Shared Memory Multiprocessor Execution Efficiently, Luis Ceze* and Josep Torrellas Department of Computer Science University of Illinois at Urbana-Champaign
Load Shedding in Network Monitoring Applications
Load Shedding in Network Monitoring Applications Pere Barlet-Ros 1 Gianluca Iannaccone 2 Josep Sanjuàs 1 Diego Amores 1 Josep Solé-Pareta 1 1 Technical University of Catalonia (UPC) Barcelona, Spain 2
CMPSC 311 Exam 2. March 27, 2015
Name: Section: 11:15 1:25 CMPSC 311 Exam 2 March 27, 2015 Closed book, closed neighbor, no electronic tools or additional papers. You may not share or discuss exam questions with anyone. 1 Short Questions
Class 17. Discussion. Mutation analysis and testing. Problem Set 7 discuss Readings
Class 17 Questions/comments Graders for Problem Set 6 (4); Graders for Problem set 7 (2-3) (solutions for all); will be posted on T-square Regression testing, Instrumentation Final project presentations:
Distributed Systems. replication Johan Montelius ID2201. Distributed Systems ID2201
Distributed Systems ID2201 replication Johan Montelius 1 The problem The problem we have: servers might be unavailable The solution: keep duplicates at different servers 2 Building a fault-tolerant service
Spring semester 2008 June 24 th 2009
Spring semester 2008 June 24 th 2009 Introduction Suggested Algorithm Outline Implementation Summary Live Demo 2 Project Motivation and Domain High quality pedestrian detection and tracking system. Fixed
An Introduction to Software Architecture. David Garlan & Mary Shaw 94
An Introduction to Software Architecture David Garlan & Mary Shaw 94 Motivation Motivation An increase in (system) size and complexity structural issues communication (type, protocol) synchronization data
An Empirical Study of Reliable Multicast Protocols over Ethernet Connected Networks
An Empirical Study of Reliable Multicast Protocols over Ethernet Connected Networks Ryan G. Lane Daniels Scott Xin Yuan Department of Computer Science Florida State University Tallahassee, FL 32306 {ryanlane,sdaniels,xyuan}@cs.fsu.edu
Profiling Grid Data Transfer Protocols and Servers. George Kola, Tevfik Kosar and Miron Livny University of Wisconsin-Madison USA
Profiling Grid Data Transfer Protocols and Servers George Kola, Tevfik Kosar and Miron Livny University of Wisconsin-Madison USA Motivation Scientific experiments are generating large amounts of data Education
2012 LLVM Euro - Michael Spencer. lld. Friday, April 13, The LLVM Linker
lld Friday, April 13, 2012 The LLVM Linker What is lld? A system linker Produce final libraries and executables, no other tools or runtime required Understands platform ABI What is lld? A system linker
Entrance exam - Informatics
Entrance exam - Informatics Name and Surname fill in the field Application No. Test Sheet No. 15 Algorithms and data structures 1 Which of the listed data structures is most suitable (w.r.t. time and memory
Using Windows NT in a NetWare Environment Gilbert Held
Previous screen 50-20-40 Using Windows NT in a NetWare Environment Gilbert Held Payoff For companies that require Windows NT clients to work in a NetWare environment, getting the configuration setup right
Ordered Indices To gain fast random access to records in a file, we can use an index structure. Each index structure is associated with a particular search key. Just like index of a book, library catalog,
OPERATING SYSTEM TRANSACTIONS
OPERATING SYSTEM TRANSACTIONS Donald E. Porter, Owen S. Hofmann, Christopher J. Rossbach, Alexander Benn, and Emmett Witchel The University of Texas at Austin OS APIs don t handle concurrency 2 OS is weak
Deterministic Shared Memory Multiprocessing
Deterministic Shared Memory Multiprocessing Luis Ceze, University of Washington joint work with Owen Anderson, Tom Bergan, Joe Devietti, Brandon Lucia, Karin Strauss, Dan Grossman, Mark Oskin. Safe MultiProcessing
assertion-driven analyses from compile-time checking to runtime error recovery
assertion-driven analyses from compile-time checking to runtime error recovery sarfraz khurshid the university of texas at austin state of the art in software testing and analysis day, 2008 rutgers university
Real-World Buffer Overflow Protection in User & Kernel Space
Real-World Buffer Overflow Protection in User & Kernel Space Michael Dalton, Hari Kannan, Christos Kozyrakis Computer Systems Laboratory Stanford University http://raksha.stanford.edu 1 Motivation Buffer
Building an Android* command-line application using the NDK build tools
Building an Android* command-line application using the NDK build tools Introduction Libraries and test apps are often written in C/C++ for testing hardware and software features on Windows*. When these
Recovering from a Crash. Three-Phase Commit
Recovering from a Crash If INIT : abort locally and inform coordinator If Ready, contact another process Q and examine Q s state Lecture 18, page 23 Three-Phase Commit Two phase commit: problem if coordinator
Lecture 22. Path Spectra Change Impact Analysis. EE 382V Spring 2009 Software Evolution - Instructor Miryung Kim
Lecture 22 Path Spectra Change Impact Analysis Today s Agenda (1) Recap of RTS Presentation Reza (advocate) Xin (skeptic) Brief Discussion on Program Profiling Class activity on statement, branch and path
ANALYSIS OF INTER- MODULE ERROR PROPAGATION PATHS IN MONOLITHIC OPERATING SYSTEM KERNELS
ANALYSIS OF INTER- MODULE ERROR PROPAGATION PATHS IN MONOLITHIC OPERATING SYSTEM KERNELS Roberto J. Drebes Takashi Nanya University of Tokyo Canon Inc. MOTIVATION Operating System (OS): most critical component
CS 1044 Program 6 Summer I dimension ??????
Managing a simple array: Validating Array Indices Most interesting programs deal with considerable amounts of data, and must store much, or all, of that data on one time. The simplest effective means for
A distributed architecture to support infomobility services. University of Modena and Reggio Emilia
A distributed architecture to support infomobility services Claudia Canali Riccardo Lancellotti University of Modena and Reggio Emilia Motivation Web 1.0 Static Web pages Information repository Limited
IP: Routing and Subnetting
IP: outing and Network Protocols and Standards Autumn 2004-2005 Oct 28, 2004 CS573: Network Protocols and Standards 1 Issues in Addressing A large corporate/campus environment Large number of Local Area
AUDIO WITH DATA. User Guide
User Guide WELCOME TO AUDIO WITH DATA Audio With Data is your perfect everyday business conferencing tool. The award-winning technology integrates fully-automated voice conferencing with easy-to-use web
Commercial Real-time Operating Systems An Introduction. Swaminathan Sivasubramanian Dependable Computing & Networking Laboratory
Commercial Real-time Operating Systems An Introduction Swaminathan Sivasubramanian Dependable Computing & Networking Laboratory swamis@iastate.edu Outline Introduction RTOS Issues and functionalities LynxOS
Replaying and Isolating Failing Multi-Object Interactions. Martin Burger Andreas Zeller Saarland University
Replaying and Isolating Failing Multi-Object Interactions Martin Burger Andreas Zeller Saarland University e-mail client written in Java 100,200 LOC ~ 1,600 Java classes 17 developers Actively developed
Real-time Data Engineering in the Cloud Exercise Guide
Real-time Data Engineering in the Cloud Exercise Guide Jesse Anderson 2017 SMOKING HAND LLC ALL RIGHTS RESERVED Version 1.12.a9779239 1 Contents 1 Lab Notes 3 2 Kafka HelloWorld 6 3 Streaming ETL 8 4 Advanced
Execution Replay for Multiprocessor Virtual Machines
Execution Replay for Multiprocessor Virtual Machines George W. Dunlap, Dominic G. Lucchetti, Peter M. Chen Electrical Engineering and Computer Science Dept. University of Michigan Ann Arbor, MI 48109-2122
COREMU: a Portable and Scalable Parallel Full-system Emulator
COREMU: a Portable and Scalable Parallel Full-system Emulator Haibo Chen Parallel Processing Institute Fudan University http://ppi.fudan.edu.cn/haibo_chen Full-System Emulator Useful tool for multicore
Optimizing unit test execution in large software programs using dependency analysis
Optimizing unit test execution in large software programs using dependency analysis Taesoo Kim, Ramesh Chandra, and Nickolai Zeldovich MIT CSAIL Abstract TAO is a system that optimizes the execution of
EECS 481 Software Engineering Exam #1. You have 1 hour and 20 minutes to work on the exam.
EECS 481 Software Engineering Exam #1 Write your name and UM uniqname on the exam. There are ten (10) pages in this exam (including this one) and seven (7) questions, each with multiple parts. Some questions
20486: Developing ASP.NET MVC 4 Web Applications
20486: Developing ASP.NET MVC 4 Web Applications Length: 5 days Audience: Developers Level: 300 OVERVIEW In this course, students will learn to develop advanced ASP.NET MVC applications using.net Framework
Performance analysis basics
Performance analysis basics Christian Iwainsky Iwainsky@rz.rwth-aachen.de 25.3.2010 1 Overview 1. Motivation 2. Performance analysis basics 3. Measurement Techniques 2 Why bother with performance analysis
The salient features of WinRunner that makes it different from other tools are
Introduction First of all, I would like to give the brief description about WinRunner. It is a powerful automated testing tool developed by Mercury Interactive Company since it is basically used for functional
Information Systems. Software Engineering. MCQ - Part 2
Information Systems & Software Engineering MCQ - Part 2 Information Systems & Software Engineering MCQ - Part 2 Changes made to the system to reduce the future system failure chances is called Preventive
DMP Deterministic Shared Memory Multiprocessing
DMP Deterministic Shared Memory Multiprocessing University of Washington Joe Devietti, Brandon Lucia, Luis Ceze, Mark Oskin A multithreaded voting machine 2 thread 0 thread 1 while (more_votes) { load
Similarity-Based Test Case Prioritization Using Ordered Sequences of Program Entities
Noname manuscript No. (will be inserted by the editor) Similarity-Based Test Case Prioritization Using Ordered Sequences of Program Entities Chunrong Fang Zhenyu Chen Kun Wu Zhihong Zhao Received: date
THE FLEXIBLE DATA-STRUCTURE SERVER THAT COULD.
REDIS THE FLEXIBLE DATA-STRUCTURE SERVER THAT COULD. @_chriswhitten_ REDIS REDIS April 10, 2009; 6 years old Founding Author: Salvatore Sanfilippo Stable release: 3.0.3 / June 4, 2015; 3 months ago Fundamental
Test Plans & Test Results
Table of contents Dresser-Rand Rotor Cell Productivity Test Plans & Test Results Note: Once you complete each of the three sections, right click on the table below and select Update Field to update the
JiST Java in Simulation Time An efficient, unifying approach to simulation using virtual machines
JiST Java in Simulation Time An efficient, unifying approach to simulation using virtual machines Rimon Barr, Zygmunt Haas, Robbert van Renesse rimon@acm.org haas@ece.cornell.edu rvr@cs.cornell.edu. Cornell
The Google File System
The Google File System Sanjay Ghemawat, Howard Gobioff and Shun Tak Leung Google* Shivesh Kumar Sharma fl4164@wayne.edu Fall 2015 004395771 Overview Google file system is a scalable distributed file system
Programmation système
Programmation système Problems Problem 1 - Message from ATM to Server Message name HELLO {userid} PASSWORD {password} BALANCE WITHDRAWAL {amount} Purpose Let server know that there is a card in the ATM
Parallel Streaming Computation on Error-Prone Processors. Yavuz Yetim, Margaret Martonosi, Sharad Malik
Parallel Streaming Computation on Error-Prone Processors Yavuz Yetim, Margaret Martonosi, Sharad Malik Upsets/B muons/mb Average Number of Dopant Atoms Hardware Errors on the Rise Soft Errors Due to Cosmic
LabVIEW FPGA in Hardware-in-the-Loop Simulation Applications
LabVIEW FPGA in Hardware-in-the-Loop Simulation Applications Publish Date: Dec 29, 2008 38 Ratings 4.16 out of 5 Overview Hardware-in-the-loop (HIL) simulation is achieving a highly realistic simulation
Call: Hyperion Planning Course Content:35-40hours Course Outline Planning Overview
Hyperion Planning Course Content:35-40hours Course Outline Planning Overview Oracle's Enterprise Performance Management Planning Architecture Planning and Essbase Navigating Workspace Launching Workspace
InsECTJ: A Generic Instrumentation Framework for Collecting Dynamic Information within Eclipse
InsECTJ: A Generic Instrumentation Framework for Collecting Dynamic Information within Eclipse Arjan Seesing and Alessandro Orso College of Computing Georgia Institute of Technology a.c.seesing@ewi.tudelft.nl,
CAP6135: Programming Project 2 (Spring 2010)
CAP6135: Programming Project 2 (Spring 2010) This project is modified from the programming project 2 in Dr. Dawn Song s course CS161: computer security in Fall 2008: http://inst.eecs.berkeley.edu/~cs161/fa08/
An Extensive Study of Static Regression Test Selection in Modern Software Evolution
An Extensive Study of Static Regression Test Selection in Modern Software Evolution Owolabi Legunsen, Farah Hariri, August Shi, Yafeng Lu, Lingming Zhang, and Darko Marinov FSE 2016 Seattle, Washington
The Google File System
The Google File System Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung December 2003 ACM symposium on Operating systems principles Publisher: ACM Nov. 26, 2008 OUTLINE INTRODUCTION DESIGN OVERVIEW
Capo: A Software-Hardware Interface for Practical Deterministic Multiprocessor Replay
Capo: A Software-Hardware Interface for Practical Deterministic Multiprocessor Replay Pablo Montesinos Matthew Hicks Samuel T. King Josep Torrellas University of Illinois at Urbana-Champaign {pmontesi,
Distributed Systems
15-440 Distributed Systems 11 - Fault Tolerance, Logging and Recovery Tuesday, Oct 2 nd, 2018 Logistics Updates P1 Part A checkpoint Part A due: Saturday 10/6 (6-week drop deadline 10/8) *Please WORK hard
GENESYS MEETING CENTER ENTERPRISE EDITION. User Guide
GENESYS MEETING CENTER ENTERPRISE EDITION User Guide WELCOME TO GENESYS MEETING CENTER Genesys Meeting Center is your perfect everyday business conferencing tool. Our award-winning technology integrates
Load Shedding in Network Monitoring Applications
Load Shedding in Network Monitoring Applications P. Barlet-Ros 1 G. Iannaccone 2 J. Sanjuàs-Cuxart 1 D. Amores-López 1 J. Solé-Pareta 1 1 Technical University of Catalonia (UPC) Barcelona, Spain {pbarlet,
Virtualization. Application Application Application. MCSN - N. Tonellotto - Distributed Enabling Platforms OPERATING SYSTEM OPERATING SYSTEM
Virtualization lication lication lication lication lication lication OPERATING SYSTEM OPERATING SYSTEM VIRTUALIZATION 1 Basic Idea Observation Hardware resources are typically under-utilized Hardware resources
ReVirt: Enabling Intrusion Analysis through Virtual Machine Logging and Replay
ReVirt: Enabling Intrusion Analysis through Virtual Machine Logging and Replay Or We Can Remember It for You Wholesale (with apologies to Philip K. Dick) George Dunlap, Sam King, SukruCinar, MurtazaBasraiand
Modeling and Simulation of Quality of Service for Composite Web Services
Modeling and Simulation of Quality of Service for Composite Web Services Gregory A. Silver Angela Maduko Rabia Jafri John A. Miller Amit P. Sheth Department of Computer Science University of Georgia Athens,
Welcome to the Oracle Enterprise Manager Technical Webcast Series
Welcome to the Oracle Enterprise Manager Technical Webcast Series Please ensure that you are connected to the audio portion of this event which is available by dialing: Dial-in US/Canada : ( 888 ) 794-4691
ExNet: An Intelligent Network Management System
ExNet: An Intelligent Management System Yoonhee Kim*, and Salim Hariri** * Department of Electrical Engineering and Computer Science Syracuse University, Syracuse, New York 13244-4100 yhkim@cat.syr.edu
Empirical Evaluation of the Tarantula Automatic Fault-Localization Technique
Empirical Evaluation of the Tarantula Automatic Fault-Localization Technique James A. Jones and Mary Jean Harrold College of Computing, Georgia Institute of Technology Atlanta, Georgia, U.S.A. jjones@cc.gatech.edu,