LMAX Disruptor
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Log4j-Users-Guide.Pdf
...................................................................................................................................... Apache Log4j 2 v. 2.2 User's Guide ...................................................................................................................................... The Apache Software Foundation 2015-02-22 T a b l e o f C o n t e n t s i Table of Contents ....................................................................................................................................... 1. Table of Contents . i 2. Introduction . 1 3. Architecture . 3 4. Log4j 1.x Migration . 10 5. API . 16 6. Configuration . 18 7. Web Applications and JSPs . 48 8. Plugins . 56 9. Lookups . 60 10. Appenders . 66 11. Layouts . 120 12. Filters . 140 13. Async Loggers . 153 14. JMX . 167 15. Logging Separation . 174 16. Extending Log4j . 176 17. Extending Log4j Configuration . 184 18. Custom Log Levels . 187 © 2 0 1 5 , T h e A p a c h e S o f t w a r e F o u n d a t i o n • A L L R I G H T S R E S E R V E D . T a b l e o f C o n t e n t s ii © 2 0 1 5 , T h e A p a c h e S o f t w a r e F o u n d a t i o n • A L L R I G H T S R E S E R V E D . 1 I n t r o d u c t i o n 1 1 Introduction ....................................................................................................................................... 1.1 Welcome to Log4j 2! 1.1.1 Introduction Almost every large application includes its own logging or tracing API. In conformance with this rule, the E.U. -
RCU Usage in the Linux Kernel: One Decade Later
RCU Usage In the Linux Kernel: One Decade Later Paul E. McKenney Silas Boyd-Wickizer Jonathan Walpole Linux Technology Center MIT CSAIL Computer Science Department IBM Beaverton Portland State University Abstract unique among the commonly used kernels. Understand- ing RCU is now a prerequisite for understanding the Linux Read-copy update (RCU) is a scalable high-performance implementation and its performance. synchronization mechanism implemented in the Linux The success of RCU is, in part, due to its high perfor- kernel. RCU’s novel properties include support for con- mance in the presence of concurrent readers and updaters. current reading and writing, and highly optimized inter- The RCU API facilitates this with two relatively simple CPU synchronization. Since RCU’s introduction into the primitives: readers access data structures within RCU Linux kernel over a decade ago its usage has continued to read-side critical sections, while updaters use RCU syn- expand. Today, most kernel subsystems use RCU. This chronization to wait for all pre-existing RCU read-side paper discusses the requirements that drove the devel- critical sections to complete. When combined, these prim- opment of RCU, the design and API of the Linux RCU itives allow threads to concurrently read data structures, implementation, and how kernel developers apply RCU. even while other threads are updating them. This paper describes the performance requirements that 1 Introduction led to the development of RCU, gives an overview of the RCU API and implementation, and examines how ker- The first Linux kernel to include multiprocessor support nel developers have used RCU to optimize kernel perfor- is not quite 20 years old. -
A Thread Synchronization Model for the PREEMPT RT Linux Kernel
A Thread Synchronization Model for the PREEMPT RT Linux Kernel Daniel B. de Oliveiraa,b,c, R^omulo S. de Oliveirab, Tommaso Cucinottac aRHEL Platform/Real-time Team, Red Hat, Inc., Pisa, Italy. bDepartment of Systems Automation, UFSC, Florian´opolis, Brazil. cRETIS Lab, Scuola Superiore Sant'Anna, Pisa, Italy. Abstract This article proposes an automata-based model for describing and validating sequences of kernel events in Linux PREEMPT RT and how they influence the timeline of threads' execu- tion, comprising preemption control, interrupt handling and control, scheduling and locking. This article also presents an extension of the Linux tracing framework that enables the trac- ing of kernel events to verify the consistency of the kernel execution compared to the event sequences that are legal according to the formal model. This enables cross-checking of a kernel behavior against the formalized one, and in case of inconsistency, it pinpoints possible areas of improvement of the kernel, useful for regression testing. Indeed, we describe in details three problems in the kernel revealed by using the proposed technique, along with a short summary on how we reported and proposed fixes to the Linux kernel community. As an example of the usage of the model, the analysis of the events involved in the activation of the highest priority thread is presented, describing the delays occurred in this operation in the same granularity used by kernel developers. This illustrates how it is possible to take advantage of the model for analyzing the preemption model of Linux. Keywords: Real-time computing, Operating systems, Linux kernel, Automata, Software verification, Synchronization. -
High Performance & Low Latency Complex Event Processor
International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Impact Factor (2012): 3.358 Lightning CEP - High Performance & Low Latency Complex Event Processor Vikas Kale1, Kishor Shedge2 1, 2Sir Visvesvaraya Institute of Technology, Chincholi, Nashik, 422101, India Abstract: Number of users and devices connected to internet is growing exponentially. Each of these device and user generate lot of data which organizations want to analyze and use. Hadoop like batch processing are evolved to process such big data. Batch processing systems provide offline data processing capability. There are many businesses which requires real-time or near real-time processing of data for faster decision making. Hadoop and batch processing system are not suitable for this. Stream processing systems are designed to support class of applications which requires fast and timely analysis of high volume data streams. Complex event processing, or CEP, is event/stream processing that combines data from multiple sources to infer events or patterns that suggest more complicated circumstances. In this paper I propose “Lightning - High Performance & Low Latency Complex Event Processor” engine. Lightning is based on open source stream processor WSO2 Siddhi. Lightning retains most of API and Query Processing of WSO2 Siddhi. WSO2 Siddhi core is modified using low latency technique such as Ring Buffer, off heap data store and other techniques. Keywords: CEP, Complex Event Processing, Stream Processing. 1. Introduction of stream applications include automated stock trading, real- time video processing, vital-signs monitoring and geo-spatial Number of users and devices connected to internet is trajectory modification. Results produced by such growing exponentially. -
Need a Title Here
Equilibrium Behaviour of Double-Sided Queueing System with Dependent Matching Time by Cheryl Yang A thesis submitted to the Faculty of Graduate and Postdoctoral Affairs in partial fulfillment of the requirements for the degree of Master of Science in Probability and Statistics Carleton University Ottawa, Ontario @ 2020 Cheryl Yang ii Abstract In this thesis, we consider the equilibrium behaviour of a double-ended queueing system with dependent matching time in the context of taxi-passenger systems at airport terminal pickup. We extend the standard taxi-passenger model by considering random matching time between taxis and passengers in an airport terminal pickup setting. For two types of matching time distribution, we examine this model through analysis of equilibrium behaviour and optimal strategies. We demonstrate in detail how to derive the equilibrium joining strategies for passen- gers arriving at the terminal and the existence of a socially optimal strategies for partially observable and fully observable cases. Numerical experiments are used to examine the behaviour of social welfare and compare cases. iii Acknowledgements I would like to give my deepest gratitude to my supervisor, Dr. Yiqiang Zhao, for his tremendous support throughout my graduate program. Dr. Zhao gave me the opportunity to explore and be exposed to various applications of queueing theory and offered invaluable guidance and advice in my thesis research. Without his help, this thesis would not be possible. I am grateful for his patience, the time he has spent advising me, and his moral support that guided me through my studies at Carleton University. I would also like to thank Zhen Wang of Nanjing University of Science and Tech- nology for his gracious help and patience. -
Review Der Linux Kernel Sourcen Von 4.9 Auf 4.10
Review der Linux Kernel Sourcen von 4.9 auf 4.10 Reviewed by: Tested by: stecan stecan Period of Review: Period of Test: From: Thursday, 11 January 2018 07:26:18 o'clock +01: From: Thursday, 11 January 2018 07:26:18 o'clock +01: To: Thursday, 11 January 2018 07:44:27 o'clock +01: To: Thursday, 11 January 2018 07:44:27 o'clock +01: Report automatically generated with: LxrDifferenceTable, V0.9.2.548 Provided by: Certified by: Approved by: Account: stecan Name / Department: Date: Friday, 4 May 2018 13:43:07 o'clock CEST Signature: Review_4.10_0_to_1000.pdf Page 1 of 793 May 04, 2018 Review der Linux Kernel Sourcen von 4.9 auf 4.10 Line Link NR. Descriptions 1 .mailmap#0140 Repo: 9ebf73b275f0 Stephen Tue Jan 10 16:57:57 2017 -0800 Description: mailmap: add codeaurora.org names for nameless email commits ----------- Some codeaurora.org emails have crept in but the names don't exist for them. Add the names for the emails so git can match everyone up. Link: http://lkml.kernel.org/r/[email protected] 2 .mailmap#0154 3 .mailmap#0160 4 CREDITS#2481 Repo: 0c59d28121b9 Arnaldo Mon Feb 13 14:15:44 2017 -0300 Description: MAINTAINERS: Remove old e-mail address ----------- The ghostprotocols.net domain is not working, remove it from CREDITS and MAINTAINERS, and change the status to "Odd fixes", and since I haven't been maintaining those, remove my address from there. CREDITS: Remove outdated address information ----------- This address hasn't been accurate for several years now. -
Varon-T Documentation Release 2.0.1-Dev-5-G376477b
Varon-T Documentation Release 2.0.1-dev-5-g376477b RedJack, LLC June 21, 2016 Contents 1 Contents 3 1.1 Introduction...............................................3 1.2 Disruptor queue management......................................3 1.3 Value objects...............................................5 1.4 Producers.................................................6 1.5 Consumers................................................7 1.6 Yield strategies..............................................9 1.7 Example: Summing integers.......................................9 2 Indices and tables 15 i ii Varon-T Documentation, Release 2.0.1-dev-5-g376477b This is the documentation for Varon-T 2.0.1-dev-5-g376477b, last updated June 21, 2016. Contents 1 Varon-T Documentation, Release 2.0.1-dev-5-g376477b 2 Contents CHAPTER 1 Contents 1.1 Introduction Message passing is currently a popular approach for implementing concurrent data processing applications. In this model, you decompose a large processing task into separate steps that execute concurrently and communicate solely by passing messages or data items between one another. This concurrency model is an intuitive way to structure a large processing task to exploit parallelism in a shared memory environment without incurring the complexity and overhead costs associated with multi-threaded applications. In order to use a message passing model, you need an efficient data structure for passing messages between the processing elements of your application. A common approach is to utilize queues for storing and retrieving messages. Varon-T is a C library that implements a disruptor queue (originally implemented in the Disruptor Java library), which is a particularly efficient FIFO queue implementation. Disruptor queues achieve their efficiency through a number of related techniques: • Objects are stored in a ring buffer, which uses a fixed amount of memory regardless of the number of data records processed. -
Linux Foundation Collaboration Summit 2010
Linux Foundation Collaboration Summit 2010 LTTng, State of the Union Presentation at: http://www.efficios.com/lfcs2010 E-mail: [email protected] Mathieu Desnoyers April 15th, 2010 1 > Presenter ● Mathieu Desnoyers ● EfficiOS Inc. ● http://www.efficios.com ● Author/Maintainer of ● LTTng, LTTV, Userspace RCU ● Ph.D. in computer engineering ● Low-Impact Operating System Tracing Mathieu Desnoyers April 15th, 2010 2 > Plan ● Current state of LTTng ● State of kernel tracing in Linux ● User requirements ● Vertical vs Horizontal integration ● LTTng roadmap for 2010 ● Conclusion Mathieu Desnoyers April 15th, 2010 3 > Current status of LTTng ● LTTng dual-licensing: GPLv2/LGPLv2.1 ● UST user-space tracer – Userspace RCU (LGPLv2.1) ● Eclipse Linux Tools Project LTTng Integration ● User-space static tracepoint integration with gdb ● LTTng kernel tracer – maintainance-mode in 2009 (finished my Ph.D.) – active development restarting in 2010 Mathieu Desnoyers April 15th, 2010 4 > LTTng dual-licensing GPLv2/LGPLv2.1 ● LGPLv2.1 license is required to share code with user-space tracer library. ● License chosen to allow tracing of non-GPL applications. ● Headers are licensed under BSD: – Demonstrates that these headers can be included in non-GPL code. ● Applies to: – LTTng, Tracepoints, Kernel Markers, Immediate Values Mathieu Desnoyers April 15th, 2010 5 > User-space Tracing (UST) (1) ● LTTng port to user-space ● Re-uses Tracepoints and LTTng ring buffer ● Uses Userspace RCU for control synchronization ● Shared memory map with consumer daemon ● Per-process per-cpu ring buffers Mathieu Desnoyers April 15th, 2010 6 > User-space Tracing (UST) (2) ● The road ahead – Userspace trace clock for more architectures ● Some require Linux kernel vDSO support for trace clock – Utrace ● Provide information about thread creation, exec(), etc.. -
Read-Copy Update (RCU)
COMP 790: OS Implementation Read-Copy Update (RCU) Don Porter COMP 790: OS Implementation Logical Diagram Binary Memory Threads Formats Allocators User Today’s Lecture System Calls Kernel RCU File System Networking Sync Memory Device CPU Management Drivers Scheduler Hardware Interrupts Disk Net Consistency COMP 790: OS Implementation RCU in a nutshell • Think about data structures that are mostly read, occasionally written – Like the Linux dcache • RW locks allow concurrent reads – Still require an atomic decrement of a lock counter – Atomic ops are expensive • Idea: Only require locks for writers; carefully update data structure so readers see consistent views of data COMP 790: OS Implementation Motivation (from Paul McKenney’s Thesis) 35 "ideal" "global" 30 "globalrw" 25 20 Performance of RW 15 lock only marginally 10 better than mutex 5 lock Hash Table Searches per Microsecond 0 1 2 3 4 # CPUs COMP 790: OS Implementation Principle (1/2) • Locks have an acquire and release cost – Substantial, since atomic ops are expensive • For short critical regions, this cost dominates performance COMP 790: OS Implementation Principle (2/2) • Reader/writer locks may allow critical regions to execute in parallel • But they still serialize the increment and decrement of the read count with atomic instructions – Atomic instructions performance decreases as more CPUs try to do them at the same time • The read lock itself becomes a scalability bottleneck, even if the data it protects is read 99% of the time COMP 790: OS Implementation Lock-free data structures -
On New Approaches of Assessing Network Vulnerability: Hardness and Approximation Thang N
1 On New Approaches of Assessing Network Vulnerability: Hardness and Approximation Thang N. Dinh, Ying Xuan, My T. Thai, Member, IEEE, Panos M. Pardalos, Member, IEEE, Taieb Znati, Member, IEEE Abstract—Society relies heavily on its networked physical infrastructure and information systems. Accurately assessing the vulnerability of these systems against disruptive events is vital for planning and risk management. Existing approaches to vulnerability assessments of large-scale systems mainly focus on investigating inhomogeneous properties of the underlying graph elements. These measures and the associated heuristic solutions are limited in evaluating the vulnerability of large-scale network topologies. Furthermore, these approaches often fail to provide performance guarantees of the proposed solutions. In this paper, we propose a vulnerability measure, pairwise connectivity, and use it to formulate network vulnerability assessment as a graph-theoretical optimization problem, referred to as β-disruptor. The objective is to identify the minimum set of critical network elements, namely nodes and edges, whose removal results in a specific degradation of the network global pairwise connectivity. We prove the NP-Completeness and inapproximability of this problem, and propose an O(log n log log n) pseudo- approximation algorithm to computing the set of critical nodes and an O(log1:5 n) pseudo-approximation algorithm for computing the set of critical edges. The results of an extensive simulation-based experiment show the feasibility of our proposed vulnerability assessment framework and the efficiency of the proposed approximation algorithms in comparison with other approaches. Index Terms—Network vulnerability, Pairwise connectivity, Hardness, Approximation algorithm. F 1 INTRODUCTION ONNECTIVITY plays a vital role in network per- tal a network element is to the survivability of the net- formance and is fundamental to vulnerability C work when faced with disruptive events. -
Connect User Guide Armv8-A Memory Systems
ARMv8-A Memory Systems ConnectARMv8- UserA Memory Guide VersionSystems 0.1 Version 1.0 Copyright © 2016 ARM Limited or its affiliates. All rights reserved. Page 1 of 17 ARM 100941_0100_en ARMv8-A Memory Systems Revision Information The following revisions have been made to this User Guide. Date Issue Confidentiality Change 28 February 2017 0100 Non-Confidential First release Proprietary Notice Words and logos marked with ® or ™ are registered trademarks or trademarks of ARM® in the EU and other countries, except as otherwise stated below in this proprietary notice. Other brands and names mentioned herein may be the trademarks of their respective owners. Neither the whole nor any part of the information contained in, or the product described in, this document may be adapted or reproduced in any material form except with the prior written permission of the copyright holder. The product described in this document is subject to continuous developments and improvements. All particulars of the product and its use contained in this document are given by ARM in good faith. However, all warranties implied or expressed, including but not limited to implied warranties of merchantability, or fitness for purpose, are excluded. This document is intended only to assist the reader in the use of the product. ARM shall not be liable for any loss or damage arising from the use of any information in this document, or any error or omission in such information, or any incorrect use of the product. Where the term ARM is used it means “ARM or any of its subsidiaries as appropriate”. Confidentiality Status This document is Confidential. -
Data Structures of Big Data: How They Scale
DATA STRUCTURES OF BIG DATA: HOW THEY SCALE Dibyendu Bhattacharya Manidipa Mitra Principal Technologist, EMC Principal Software Engineer, EMC [email protected] [email protected] Table of Contents Introduction ................................................................................................................................ 3 Hadoop: Optimization for Local Storage Performance................................................................ 3 Kafka: Scaling Distributed Logs using OS Page Cache .............................................................10 MongoDB: Memory Mapped File to Store B-Tree Index and Data Files ....................................15 HBase: Log Structured Merged Tree – Write Optimized B-Tree ................................................19 Storm: Efficient Lineage Tracking for Guaranteed Message Processing ...................................25 Conclusion ................................................................................................................................33 Disclaimer: The views, processes, or methodologies published in this article are those of the authors. They do not necessarily reflect EMC Corporation’s views, processes, or methodologies. 2014 EMC Proven Professional Knowledge Sharing 2 Introduction We are living in the data age. Big data—information of extreme volume, diversity, and complexity—is everywhere. Enterprises, organizations, and institutions are beginning to recognize that this huge volume of data can potentially deliver high value to their