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RTI Data-Distribution Service (DDS) Master Class - 2010

The document discusses DDS (Data Distribution Service), a middleware standard for distributed real-time systems. It provides an overview of DDS technology including its data-centric publish-subscribe model, quality of service capabilities, and ability to integrate external systems. The document also discusses RTI's implementation of the DDS standard and how it provides high performance, scalability and other benefits for building distributed real-time systems.

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DDS: A Next-Generation Approach to Building Distributed Real-Time Systems Gerardo Pardo-Castellote, Ph.D. Co-chair OMG DDS SIG CTO, Real-Time Innovations gerardo.pardo@rti.com The Real-Time 2010 Quickstart Middleware Experts http://www.rti.com
Outline Overview of Technology – Background – Applications – Data-Centric Pub-Sub – Quality of Service – Add-on components Application development cycle Architecting data-centric systems & modeling the Data Protocol, Performance & Scalability. Integrating external and legacy systems. Future directions and Standards:
Challenge: More Data, More Speed, More Sources TRENDS: Growing Information Volume Lowering Decision Latency Increasing System Availability Accelerating technology insertion and deployment Next-generation systems needs: Performance Scalability Robustness & Availability Platform Integration & Evolution Safety-Critical Certification Security © 2009 Real-Time Innovations, Inc. 3
Solution: Standards-based Integration Infrastructure for Real-Time Applications Streaming Sensors Events Data Data Distribution Service Real-Time Enterprise Actuators Applications Applications © 2009 Real-Time Innovations, Inc. 4
Architecture for the next-generation systems Existing technologies are reaching robustness/performance/scalability limits RTI DDS brings a fundamental new architecture and approach – Fully decentralized, peer-to-peer, “no bottlenecks” architecture – Superior Wire Protocol – Powerful data-centric model – Built-in Robustness and High-Availability – Standards-based, multi-platform Brokers as choke-points RTI Approach Single-lane traffic No prioritization
History: DDS the Standards Data Distribution Service for Real-Time Systems – API for Data-Centric Publish-Subscribe distributed systems – Adopted in June 2003 – Revisions: 2004, 2005, 2006 – Spec version 1.2: formal/2007-07-01 Interoperability wire protocol – Adopted in July 2006 – Revised in July 2007 – Spec version 2.1: formal/2009-01-05 Related specifications – UML Profile for DDS – DDS for Light-Weight CCM – Extensible Topics for DDS(*) Multiple (7+) Implementations 6
Open Architecture Cross-vendor portability Vendor independent – API for portability – Wire protocol for interoperability Multiple implementations DDS API – 7 of API – 4 support RTPS (+1 non-DDS) Middleware Heterogeneous – C, C++, Java, .NET (C#, C++/CLI) Real-Time – Linux, Windows, VxWorks, other Publish-Subscribe embedded & real-time Wire Protocol (RTPS) Loosely coupled Cross-vendor interoperability © 2009 Real-Time Innovations, Inc. 7
RTI DDS Application Examples Aegis Weapon System ScanEagle UAV Lockheed Martin Boeing Radar, weapons, displays, Sensors, ground station C2 Advanced Cockpit Ground B-1B Bomber Control Station Boeing Predator and SkyWarrior UAS C2, communications, General Atomics weapons Telemetry data, multiple workstations Common Link Integration Processing (CLIP) Northrop Grumman RoboScout Standards-compliant Base10 interface to legacy and new tactical data links Internal data bus and link to communications center Air Force, Navy, B-1B and B-52 © 2009 Real-Time Innovations, Inc. 8
RTI DDS Application Examples Driver safety Multi-ship simulator Volkswagen FORCE Technology vision systems, analysis, driver Controls, simulation display information systems Medical imaging Mobile asset tracking NMR and MRI Wi-Tronix Sensors, RF generators, user GPS, operational status interface, control computers over wireless links Automated trading Automated Trading Desk (ATD, Highway traffic monitoring now Citigroup) City of Tokyo Market data feed handlers, pricing engines, algorithmic Roadway sensors, roadside trading applications kiosks, control center © 2009 Real-Time Innovations, Inc. 9
RTI DDS Application Examples Full-immersion simulation Signal Processing National Highway Transportation PLATH GMBH Safety Authority RTI supports modular Migrated from CORBA, DCOM programming across for performance product line Air-Traffic Management INDRA. Deployed in Large Telescopes UK, Germany, Spain European Southern Standards, Performance, Observatory Scalability Performance & Scalability 1000 mirrors, 1sec loop Radar Systems Industrial Control AWACS upgrade Schneider Electric Evolvability, Mainteinability, VxWorks-based PLCs and supportability communicate via RTI-DDS © 2009 Real-Time Innovations, Inc. 10
Standards Focus Object Management Group – Board of Directors member – Authored DDS and RTPS specs, co-chair SIG Open Group Network Centric Operations Industry Consortium (NCOIC) – Chair Open Standards and Patterns Working Group STAC Benchmark Council Support and integrate with: – DDS, RTPS, JMS, SQL, Web Services, CORBA, UML, HLA, JAUS, Eclipse, IPv6… 11 © 2008 Real-Time Innovations, Inc.
Corporate Background Founded by Stanford researchers Focused on real-time middleware Solid financials – 16-year track record of growth Real-Time Market Leader – #1 market share in embedded middleware of all types1 – 70+% worldwide share of DDS market2 50/50 software and services 1Embedded Market Forecasters 2VDC Analyst Report 12
RTI Supports all Phases of Development Services Engagement Description Capabilities Timeline Workshop 2 days Introduction to RTI products and capabilities QuickStart 2+ days In-depth training on RTI DDS API, QoS policies, and common architecture patterns Support On-Demand Web-portal, phone and email customer lauded support Architecture Study 3-4 weeks Custom design review, risk analysis and architecture recommendations Design Support 4+ weeks Support hardware & software integration, architecture design, Package performance tuning, on-site debugging, implementation support Integration & SOW supported Custom feature, tool and software development support Development Ports As needed RTI tools and software on your special, purpose built hardware
RTI Global Presence
Benefits of the DDS approach Simple & Powerful Data-Centric Pub-Sub Model Reduces Risk and Development/Integration Time Enhances effective performance by delivering the right data to the right place with the right QoS Standards-based: API and Protocol 1. Unsurpassed Performance and Scalability Priority-aware no choke-points architecture 2. Builds higher quality systems and lowers TCO Built-in high-value capabilities Handles Availability & other Messaging Event Database “hard problems” & Caching Processing Bridge SQL Easy to maintain and Evolve Persistence Redundancy Recording & Durability & Failover Leverage multicore © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 15
Data-Centric Pub-Sub Model Essentially a virtual, decentralized global data space Source (key) Latitude Longitude Altitude UAV1 37.4 -122.0 500.0 UAV2 40.7 -74.0 250.0 UAV3 50.2 -0.7 2000.0 Persistence Recording Service Service © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 16
example Data-Centric Model “Global Data Space” generalizes Subject-Based Addressing – Data objects addressed by DomainId, Topic and Key – Domains provide a level of isolation – Topic groups homogeneous subjects (same data-type & meaning) – Key is a generalization of subject • Key can be any set of fields, not limited to a “x.y.z …” formatted string Data Reader Data Writer Topic (subject) Key Data Object Data Reader Data Writer Data Reader Data Writer
ShapesDemo Demo: Publish-Subscribe © 2009 Real-Time Innovations, Inc. 18
example DDS communications model Data Domain Data Domain New Writer Participant Reader Participant “Alarm” Got new “Alarm” subscriber! data Offered Requested Listener QoS Listener QoS Participants scope the global data space (domain) Topics define the data-objects (collections of subjects) Writers publish data on Topics Readers subscribe to data on Topics QoS Policies are used configure the system Listeners are used to notify the application of events
ShapesDemo Demo: Real-Time Quality of Service Content filter Time-based filter History Deadline Analyzer © 2009 Real-Time Innovations, Inc. 20
Real-Time Quality of Service (QoS) QoS Policy QoS Policy DURABILITY USER DATA User QoS HISTORY TOPIC DATA Volatility READER DATA LIFECYCLE GROUP DATA WRITER DATA LIFECYCLE PARTITION Presentation LIFESPAN PRESENTATION Infrastructure ENTITY FACTORY DESTINATION ORDER RESOURCE LIMITS OWNERSHIP Redundancy Transport RELIABILITY OWNERSHIP STRENGTH Delivery TIME BASED FILTER LIVELINESS DEADLINE LATENCY BUDGET CONTENT FILTERS TRANSPORT PRIORITY © 2009 Real-Time Innovations, Inc. 21
20X Faster than JMS / Broker-based solutions RTI DDS is about 20X faster than JMS 60,000 Throughput with a single publisher ( 2KB messages ) 50,000 Point-to-Point Updates per Second 45 40 RTI DDS 40,000 1-1 1-10 JMS [1000's sample/s] 35 30,000 1-24 30 25 20,000 20 10,000 15 10 0 16 32 64 128 256 512 1024 5 Message/Data Size (bytes - without batching) 0 2 4 6 9 11 18 CPU load [%] RTI DDS reliable multicast exhibits near perfect scalability Platform: Linux 2.6 on AMD Athlon, Dual core, 2.2 GHz © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 22
DDS Is Scalable Going from 1 to 888 Ultra-low latency and jitter subscribers of the same – Deterministic – No intermediaries data has only a 10% impact on throughput http://www.rti.com/products/dds/benchmarks-cpp-linux.html © 2009 Real-Time Innovations, Inc. 23
Realizing Performance & Scalability RTI DDS Approach Others: Broker-based middleware AMQP RTPS unspecified DDS operates peer-to-peer, without brokers DDS uses RTPS, an Advanced Multi-Session protocol supporting Reliable Multicast © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 24
DDS Enables Higher quality, Lower TCO Systems Pre-built components address many challenging use-cases Presence Discovery Historical Cache Durable Data Availability Redundancy & Failover Recording Database Connectivity Web Accessibility Transformation Messaging Event Database & Caching Processing Bridge SQL Event Processing Redundancy WAN Routing Recording Persistence & Durability & Failover Security Guard Hooks © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 25
Outline Overview of Technology Application development cycle – How to begin. Hello world example. – Defining data in XML and XSD – Development and Run-time Tools: Ping, Spy, Analyzer, Wireshark, Excel – Discovery and Builtin-Topics – Configuring QoS via XML files Architecting data-centric systems & modeling the Data Protocol, Performance & Scalability. Integrating external and legacy systems. Future directions and Standards:
Hands-on Example (C++) Type Three minutes to a running app!! Definition 1. Define your data 2. Create your project MyType.idl 3. Build 4. Run: publisher subscriber MyType.sln rtiddsgen MyType.h MyTypePublisher.cpp Aux: MyTypeSupport.c File Browser MyTypeSubscriber.cpp Console compiler Delete Files rtiddsspy Publisher.exe Subscriber.exe
Alternatives beyond IDL and CDR Type Representation Type Representation “Foo” schema “Foo” schema IDL, XSD, … IDL, XSD, … rtiddsgen DDS API DDS-API DDS-API Foo.h, Foo.h, DataWriter<Foo> DataReader<Foo> Foo.java Foo.java Language Language Bindings ddsgen Bindings Data Representation Data Representation 10110011… 10110011… CDR, TLV, CDR, TLV, JSON, XML JSON, XML DDS-RTPS Wire Interoperability
Alternative Type Description Languages rtiddsgen supports 4 alternative ways to define types: – All are equivalent Exercise: – You can convert between all these formats - Start with an IDL Type - Convert to XML IDL - Convert to XSD – + Simple, Compact, Similar to C/C++/Java - Start with an XML-defined type – + Allows type sharing with CORBA - Convert to IDL – - Perceived as “legacy” – - Limited tool support - Convert to XSD XML – + Good tool support and syntax validation – + Familiar to a large community. Fashionable – - More verbose. Custom Syntax XSD – + Good tool support – + Commonly used as a type-description language – - Cumbersome syntax for certain types. Not human friendly WSDL – + Same as XSD and allows type sharing with Web-Services – - Same as XSD
rtiddsgen Details rtiddsgen [-d <outdir>] [-language <C|C++|Java|C++/CLI|C#>] [-namespace] [-package <packagePrefix>] [-example <arch>] [-replace] [-debug] [-corba [client header file]] [-optimization <level of optimization>] [-stringSize <Unbounded strings size>] [-sequenceSize <Unbounded sequences size>] [-notypecode] [-ppDisable] [-ppPath <path to the preprocessor>] [-ppOption <option>] [-D <name>[=<value>]] [-U <name>] [-I <directory>] [-noCopyable] [-use42eAlignment] [-help] [-version] [-convertToIdl | -convertToXml | -convertToXsd | -convertToWsdl] [[-inputIdl] <IDLInputFile.idl> | [-inputXml] <XMLInputFile.xml> | [-inputXsd] <XSDInputFile.xsd> | [-inputWsdl] <WSDLInputFile.wsdl>] DefinitionFile can be IDL, XSD and XML file -example generates example pub/sub apps and makefiles for compilation. -replace replaces everything that’s generated. Use if the data type definition has changed. Always use with caution if you’ve made modifications.
IDL vs. XML: IDL Example struct MemberStruct{ short sData; } typedef MemberStructType; //@top-level false 31
IDL vs. XML: XML Example <?xml version="1.0“ encoding="UTF-8"?> <types xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="../rti_dds_topic_types.xsd"> <struct name="MemberStruct" topLevel="false"> <member name="sData“ type="short"/> </struct> <typedef name="MemberStructType" type="nonBasic“ nonBasicTypeName="MemberStruct“ topLevel="false"/> </types> 32
IDL vs. XSD: XSD Example <?xml version="1.0" encoding="UTF-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:dds="http://www.omg.org/dds" xmlns:tns="http://www.omg.org/IDL- Mapped/" targetNamespace="http://www.omg.org/IDL-Mapped/"> <xsd:import namespace="http://www.omg.org/dds" schemaLocation="rti_dds_topic_types_common.xsd"/> <xsd:complexType name="MemberStruct"> <xsd:sequence> <xsd:element name="sData" minOccurs="1" maxOccurs="1" type="xsd:short"/> </xsd:sequence> </xsd:complexType> <!-- @topLevel false --> <xsd:complexType name="MemberStructType"> <xsd:complexContent> <xsd:restriction base="tns:MemberStruct"> <xsd:sequence> <xsd:element name="sData" type="xsd:short" minOccurs="1" maxOccurs="1"/> </xsd:sequence> </xsd:restriction> </xsd:complexContent> </xsd:complexType> <!-- @topLevel false --> 33 </xsd:schema>
ShapesDemo Data Persistence Persistence_svc A standalone service that persists data outside of the context of a DataWriter Data Data Writer Data Can be configured for: Writer Reader • Redundancy • Load balancing Data • Direct for performance Reader Global Data Space • Relay/Transactional • Redundant/ Fault-tolerant Persistence Persistence Service Service Permanent Permanent Storage Storage © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 34
Data Persistence A standalone service that persists data outside of the context of a DataWriter Data Can be configured for: Data Writer Data Writer Reader • Redundancy • Load balancing Data Global Reader Data Space Demo: 1. PersistenceService 2. ShapesDemo 3. Application failure Persistence Persistence 4. Application re-start Service Service 5. Persistence Svc failure Permanent Permanent 6. Application re-start Storage Storage © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 35
ShapesDemo Ownership and High Availability Producer / Writer K1 Pri strength=10 mary Topic T1 Producer / Writer K1 Backup strength=5 K2 Primary K1 K2 Producer / Writer K2 Backup strength=1 Owner determined per Topic and Key Only writer with highest strength can publish a Key Automatic failover when highest strength writer: – Loses liveliness – Misses a deadline – Stops writing the subject Shared Ownership allows any writer to update any object © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 36
Outline Overview of Technology Application development cycle Architecting data-centric systems & modeling the Data – Examples: News example, Data Streaming, Commands, Video – Data Persistence with Examples – Using DynamicData Protocol, Performance & Scalability. Integrating external and legacy systems. Future directions and Standards:
Components/Mechanics of the GDS Application Application GDS Definition Discovery Application Application Listener Listener Protocol Cache Management Cache Management
Components/Mechanics of the GDS Application Application Domain Modeling & Sys. Design GDS Definition Discovery Application Config & Application Qos Listener Listener Protocol Cache Management Cache Management
Designing a Data-Centric System Define/Model the Global Data Space Configure the Cache Management Configure Discovery Configure the Protocol Configure/Use hooks for – Fault detection – Controlled access
Global Data Space / Global State Identify the number of domains Domain Information model – Topics – Types – Keys – Ownership
Domain and Domain Participants N4 App 4 • Container for N1 App 1 Pub/Sub Pub/Sub applications that (A,B/C,D) (D/C,E,F) want to communicate • Applications can N2 App 2 N4 App 5 join or leave a Subscribe Domain Publish domain in any order (C) (C) • New Applications are “Auto-Discovered” N3 App 3 N5 App 6 • An application that Pub/Sub Subscribe (E,F/A,C) (B,C) has joined a domain is also called a “Domain Participant” Single ‘Domain’ System
Domain and Domain Participants Using Multiple domains for Scalability, Modularity & Isolation Node 1 - App 1 Node 4 - App 1 Pub/Sub Domain A Pub/Sub Node 2 - App 1 Node 4 - App 2 Subscribe Publish Domain B Node 3 - App 1 Node 5 - App 1 Pub/Sub Subscribe Domain C Node 5 - App 2 Added Func. Node 6 - App 1 Pub/Sub Pub/Sub demo_domain_0 Multiple Domain System demo_domain_1
Topics & Datatypes, Keys & Subjects Topic “MarketData” Data-type (name-type-value pairs) source type symbol Exchange volume bid ask OPRA IBM NYSE 200000 118.30 118.36 OPRA AAPL NASDAQ 171.20 171.28 RTFP EQ Key fields Subject Additional fields (payload) Topic “OrderEntry” Exchange type Symbol Order num number limit stop expiration NYSE BUY IBM 11956 500 120 - DAY NYSE BUY IBM 11957 1000 124.5 124 DAY NASDAQ SELL AAPL 11958 400 - 160 DAY Subject Key fields demo_filters
QoS: Ownership Specifies whether more than one DataWriter can update the same instance of a data-object Ownership = EXCLUSIVE Ownership = SHARED “Only highest-strength “All data-writers can data writer can update each update data- each data-instance” instance” Data Data Data Data Data Data Writer Writer Writer Writer Writer Writer Data- Data- Instance Instance Provides fast, robust, transparent replacement for fail- over and/or take-over.
demo_ownership QoS: Ownership Strength Specifies which DataWriter is allowed to update the values of data-objects OWNERSHIP_STRENGTH “Integer to specify the ORDER strength of an instance” Strength = 1 Strength = 4 Domain Participant Data AfterData Expires QoS Data “RIGHT” Writer Writer Reader “LEFT” - Deadline “RIGHT” “LEFT” - Liveliness Publisher Publisher Subscriber S S Note: Only applies to Topics with Ownership = Exclusive
Configure the Cache Management Cache State Content – History Writer Application – Lifespan – Persistence – Resources Reader Cache View – Partitions – Content-Based Filter – Time-Based Filter – Order Reader Application
QoS: History – Last x or All KEEP_ALL: KEEP_LAST: “depth” integer for Publisher: keep all until delivered the number of samples to keep at Subscriber: keep each sample until the any one time application processes that instance demo_history S7 S7 Data Data S7 Data S6 S6 Writer Writer S6 Reader S5 S5 S4 S4 S3 Publisher S2 Publisher Subscriber S1 Keep All Keep Last 2 Keep Last 4 S7 S6 S5 S4 S3 S2 S1
lifespan_pub QoS: Lifespan lifespan_sub User can set lifespan duration Manages samples in the history queues, attached to each Sample Topic S1 Data Data Writer Reader S2 S3 S4 S5 Perm. Publisher Subscriber S6 Storage S7 S1 S2 S4 S3 S2 S1
Content-Based Filtering Instance 1 Value = 249 Content Filtered Value = 230 Topic Instance 2 Instance 3 Value = 275 Topic Instance 4 Value = 262 “Filter Expression ” Instance 5 Value = 258 Ex. Value > 260 Instance 6 Value = 261 Instance 7 Value = 259 The Filter Expression and Expression Params will determine which instances of the Topic will be received by the subscriber. content_filter_example
QoS: TIME_BASED_FILTER time_filter_example Topic “minimum_separation”: Data Reader does not want to receive data Domain faster than the min_separation time Participant Data Data Writer Reader Publisher Discarded Subscriber samples S SSS S S S minimum separation Data Samples
Cache Management in Action Topics – Square, Circle, Triangle – Attributes Data types (schemas) – Shape (color, x, y, size) • Color is instance Key – Key • Color field used for key QoS – History, Partition – Time-Based Filter – Content-Based Filter demo
Configure the Protocol Discovery Reliability Writer Application Batching Liveliness Flow Control Asynchronous write Network Configuration – Enabled Transports + transport properties – Multicast addresses – Transport Priority OS settings – Threads Reader Application – Memory
Tunable Reliability Protocol Configurable AckNack reply times Performance can be tracked by to eliminate storms senders and recipients – Configurable high/low watermark, Fully configurable to bound Buffer full latency and overhead Flexible handling of slow – Heartbeats, delays, buffer sizes recipients – Dynamically remove slow receivers S7 S1 Data S6 Reliable Data S2 Writer Reader S5 •Guaranteed S3 S4 Ordered Delivery S4 S3 S5 Publisher •“Best effort” also Subscriber S6 S2 S1 supported S7 S8 S7 S3 S5 S4 S2 S1 NACK #6 S6
Configure Notifications, Fault Detection & Management Writer Application Listeners Deadline Qos Liveliness Qos Built-in Readers Notification of matching Reader Application
QoS: Deadline deadline_example Topic DEADLINE “deadline period” Commits Failed to to provide get data Data data each Data Writer deadline Listener Reader period. Publisher Expects data every Subscriber deadline period. deadline S X S S S S S
QoS: Liveliness – liveliness_example Type and Duration Type: Controls who is responsible for issues of ‘liveliness packets’ AUTOMATIC = Infrastructure Managed MANUAL = Application Managed kill_apps Topic Topic Failed to renew lease Data Data Writer Listener Reader Publisher Subscriber lease_duration LP LP LP S Liveliness Message
Exercise: How could “chat rooms” be implemented? Different Topics for each Chat room? Map to Partitions? Add field to the message and use content- filtered Topics? Same as before and also make room part of the Key? Others? Discuss pros and cons of each approach
Exercise: How could we implement Ground control stations that monitor UAVs Different Topics for each UAV? – Or use Keys? Different Domains for each Ground Station? – Or Partitions? How to control multiple UAVs from the same ground station? How to switch the ground station that controls the UAV? How to do failover between ground stations? How to direct a message to one or all UAVs? How to detect loss of connection to an UAV? Discuss pros and cons of each approach
Outline Overview of Technology Application development cycle Architecting data-centric systems & modeling the Data Protocol, Performance & Scalability. – Details on Reliable Protocol – Latency and Throughput – Using RTI’s LatencyTest and Perftest – Batching – Asynchronous writes & FlowController – Maximizing latency and Throughput Integrating external and legacy systems. Future directions and Standards:
Performance & Scalability RTI DDS is about 20X faster than JMS 60,000 Throughput with a single publisher ( 2KB messages ) 50,000 Point-to-Point Updates per Second 45 40 RTI DDS 40,000 1-1 1-10 JMS [1000's sample/s] 35 30,000 1-24 30 25 20,000 20 10,000 15 10 0 16 32 64 128 256 512 1024 5 Message/Data Size (bytes - without batching) 0 2 4 6 9 11 18 CPU load [%] RTI DDS reliable multicast exhibits near perfect scalability Platform: Linux 2.6 on AMD Athlon, Dual core, 2.2 GHz © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 61
Extremely low latency and jitter Reliable, ordered delivery over Gigabit Ethernet between 2.4 GHz Core 2 Quad processors running 32-bit Red Hat Enterprise Linux 5.0 © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 62
Orders of magnitude more scalable than broker-based solutions Going from 1 to 888 New topics can be added to a subscribers of the same system without impacting the data has only a 10% impact latency and throughput on on throughput other topics Throughput with 8 topics is 8x the throughput with 1 topic http://www.rti.com/products/dds/benchmarks-cpp-linux.html © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 63
Realizing Performance & Scalability RTI Approach Others: Broker-based middleware AMQP RTPS unspecified RTI operates peer-to-peer, without brokers RTI uses RTPS, an Advanced Multi-Session protocol supporting Reliable Multicast © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 64
Advanced Scalability & Performance Techniques Latency and Priority Aware message batching Content-Aware multi-channel reliable multicast Enhanced Reliable Protocol – Selective ACKs (SACKs) for Confirmed Reliability – NACK-only Reliable Protocol for Massive Scalability Smart caching integrated with the message protocol Content-Filtering at the source © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 65
Message Batching Send queue Receive queue write() sender receiver Without batching each message is separately sent. For small messages protocol headers might be bigger than payload Send queue Receive queue With batching messages are held a little and Transparent: write() combined into larger Receiver still batches maximizing sees individual sender throughout and minimizing messages CPU receiver © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 66
Reliability with Batching Reliability must work even when messages are batched ACK or NACK of individual samples would negate some of the benefits of batching… => Protocol must be batch aware so that it can ACK/NACK complete batches! write() B1 B1 sender B1 B2 B2 B2 B3 B3 B3 receiver ACK(B3), NACK(B2) Repair B2 © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 67
Batching is hard but it pays! RTI DDS 4.3b perftest results 1000 900 800 Throughput (Mbps) 700 Linux Baseline 600 Linux 10Kb Batch 500 400 300 200 100 0 0 1000 2000 3000 4000 5000 Intel Core2Duo Single-CPU Dual-Core 2.4GHz, 4MB cache Sample size (bytes) 32-bit CentOS 5 (RHEL 5), 2GB memory, Intel E1000 NIC © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 68
Classic (TCP Style) Reliable Protocol No packet loss situation 01 01 01 02 02 02 03 03 03 04 04, HB ACK 1-4 04 05 05 05 06 06 06 07 07 07 08 08, HB ACK 1-8 08 ShapesDemo Company Confidential © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 69
Classic (TCP Style) Reliable Protocol with some packet loss 01 01 01 02 02 02 03 03 X 04 04, HB X ACK 1-2, NACK 3 05 05 X 03 06 04 06 07 06 05 07 08 07 08 08, HB ACK 1-8 Packets 04 and 05 are received but the protocol drops them because a prior packet 03 is missing. This wastes valuable bandwidth © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 70
RTI DDS Reliability (Reader Cache + SACK) improves performance when packet loss occurs 01 01 01 02 02 02 03 03 X 04 04, HB ACK 1-2, SACK 3 04 05 05 03 05 06 06 06 07 07 07 08 08, HB ACK 1-8 08 Packets 04 and 05 are received and cached waiting for the repair of 03. No bandwidth is wasted. © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 71
RTI DDS NACK-only reliability eliminates ACK traffic if there no packet loss 01 01 01 02 02 02 03 03 03 04 04, HB 04 05 05 05 06 06 06 07 07 07 08 08, HB 08 No ACK traffic under normal operating conditions © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 72
RTI DDS NACK-only reliability greatly reduces traffic even with packet loss 01 01 01 02 02 02 03 03 X 04 04, HB NACK 3 04 05 05 03 05 06 06 06 07 07 07 08 08, HB 08 Negative Acknowledgments sent only when some message is lost This approach is far more scalable when there are many subscribers © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 73
Asynchronous Publishing & Flow COntroller synchronous send path: asynchronous send path: Return To User 1. rapid write() return User Thread write() Data Queue 3. coalesce data samples 2. network traffic shaping Flow Flow Publishing Thread Net I/O Wire Data Out Controller
Qos Policies DDS_PublishModeQosPolicy – kind • DDS_SYNCHRONOUS_PUBLISH_MODE_QOS • DDS_ASYNCHRONOUS_PUBLISH_MODE_QOS – flow controller name DDS_AsynchronousPublisherQosPolicy – disable_asynchronous_write <FALSE> – thread – disable_asynchronous_batch <FALSE> – asynchronous_batch_thread
AP in Participant’s World Participant Publishing Thread D Publisher D Publishing Thread A Publishing Thread B Publishing Thread C Publisher A Publisher B Publisher C
Flow Controller Token Distribution basic token bucket – steady-state traffic • tokens_added_per_period • token_period – max burst control • max_tokens – Additional controls • tokens_leaked_per_period • all values can be DDS_LENGTH_UNLIMITED – piggyback discount (Token Exchange Rate) • bytes_per_token scheduling policy – round-robin (RR) – earliest-deadline-first (EDF) • deadline = time of write + DDS_DataWriterQos::latency_budget
Using Asynchronous Publishing DataWriter-side: DDS_FlowControllerProperty_t property; property.sched_policy = DDS_EDF_FLOW_CONTROLLER_SCHED_POLICY; property.token_bucket.max_tokens = A; //[0, DDS_LENGTH_UNLIMITED] property.token_bucket.tokens_added_per_period = B; //[0, DDS_LENGTH_UNLIMITED] property.token_bucket.tokens_leaked_per_period = C; //[0, DDS_LENGTH_UNLIMITED] property.token_bucket.bytes_per_token = D; //[1024, DDS_LENGTH_UNLIMITED] property.token_bucket.period = E; //[0, DDS_DURATION_INFINITE] ... DDSFlowController *controller = participant->create_flowcontroller("superflow", property); ... writer_qos.publish_mode.kind = DDS_ASYNCHRONOUS_PUBLISH_MODE_QOS; writer_qos.publish_mode.flow_controller_name = "superflow"; // // Set up History queue size to hold deferred issues!!!!!. writer_qos.history.kind = DDS_KEEP_LAST_HISTORY_QOS; writer_qos.history.depth = z; // <<<<<<<<<<<<<!!!!!!!!!!!!!!<<<<< ... writer->write(data_sample, ...); ... // Optional wait for pipe to empty writer->wait_for_asynchronous_publishing(timeout); ... // Optional On-Demand trigger controller->trigger_flow(); ... // Optional Modify controller properties controller->set_property(); No changes on DataReader-side!!
Flow Controller Design Challenge Requirements; – Large 1 mbyte issue. – Transmit over period of 10 seconds – Low priority transmission – Transport buffer size set to 32K CONTROLLER: property.sched_policy = ??; property.token_bucket.max_tokens = ?? property.token_bucket.tokens_added_per_period = ?? property.token_bucket.tokens_leaked_per_period = ??; property.token_bucket.bytes_per_token = ??; property.token_bucket.period = ??
Flow Controller Design Challenge Requirements; – Large 1 mbyte issue. – Transmit over period of 10 seconds – Low priority transmission – Transport buffer size set to 32K – Cannot loose any issues CONTROLLER: property.sched_policy = DDS_RR_FLOW_CONTROLLER_POLICY; property.token_bucket.max_tokens = 1 property.token_bucket.tokens_added_per_period = 1 property.token_bucket.tokens_leaked_per_period = unlimited; property.token_bucket.bytes_per_token = 32k; property.token_bucket.period = 200ms Extra Credit Discussion: What about reliable protocol properties?
Outline Overview of Technology Application development cycle Architecting data-centric systems & modeling the Data Protocol, Performance & Scalability. Integrating external and legacy systems. – Routing Service – Systems of Systems – Cross Domain Solutions – Accessing Data over a WAN – Database Connectivity – Access over the Web Future directions and Standards:
Real-Time Recording Service Applications: Record high-rate data – Future analysis and arriving in real-time debugging Non-intrusive – multicast – Post-mortem reception – Compliance checking – Replay for testing and simulation purposes Demo: 1. Start RecordingService 2. Start ShapesDemo 3. See output files 4. Convert to: HTML XML 5. View Data: HTML XML sqlite stop_all © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 82
Relational Database Integration Publish-Subscribe Action Relational Actions Write() ShapesDemo UPDATE [2,3] & INSERT Read() & Take() SELECT Dispose() DELETE Wait() & Listener sql_gui sql_shell Topic T1 Table T1 RTI I1 Real-Time I1 I2 I3 Connect I2 I3 2. start RTC 1. start mysql stop_all Event driven – The fastest way to observe database changes! © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 83
COTS tools: Excel – Interacting with your data Display live RTI DDS Data in Excel Perform real-time computations and charts Publish RTI DDS data from Excel ShapesDemo blank saved demo
RTI Routing Service Selective, real-time data forwarding and transformation Can Change Topic Name and Topic Schema – Allows for custom transformations via “plugin” – Can filter/guard data QoS managed, can cache last-known value for data Dynamically configured Location independent deployment start Topic A Topic B DDS Router ShapesDemo_0 ShapesDemo_1 XFORM GUARD stop_all
Global Scalability: LAN to WAN… …without sacrificing Performance and Security Site A DDS DDS Router Router Site C Topics: Topics: Site Status Site Status Alarms WAN /  Proc Sensor Data Health Logs Result Data Internet Sensor Data Alarms Proc Sensor Data TCP/TLS/SSL Site B DDS DDS Site D Router Router Topics: Topics: Site Status Site Status Sensor Data Result Data © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 86
Web Accessibility Direct access to real-time data from Web-Based Applications simulated tracks Tactical Real-Time Data Web Web Enabled Web Enabled Recorded DDS Enabled Data DDS DDS DDS Recording 2. start Service 1. start replay & router Recorded Recorded Track Files view maps Data Files GUARD 87 stop_all
Outline Overview of Technology Application development cycle Architecting data-centric systems & modeling the Data Protocol, Performance & Scalability. Integrating external and legacy systems. Future directions and Standards: – Extensible Topics for DDS – Web Enabled DDS – Standard C++ PSM for DDS Q&A
Extensible Dynamic Types Submission class Ov erv iew +type TypeModel::Type Data * 1 1 1 1 1 +type * TypeRepresentation:: LanguageBinding:: LanguageBinding:: DataRepresentation:: TypeRepresentation TypeLanguageBinding DataLanguageBinding DataRepresentation
Example: Current mechanisms Type Definition IDL: Foo.idl struct Foo { Language string<> name; Binding long ssn; }; IDL to Language Mapping: Foo.h Foo.cpp Data FooTypeSupport.cpp Representation struct Foo { IDL to CDR: char *name; int ssn; 00000006 }; 68656C6C 6F000000 Foo f = {“hello”, 2”}; 00000002
Type-Definition – Language Representation – Serialized encapsulation ... Each offers options Type Representation Type Representation “Foo” schema “Foo” schema IDL, XSD, … IDL, XSD, … ddsgen DDS API DDS-API DDS-API Foo.h, Foo.h, DataWriter<Foo> DataReader<Foo> Foo.java Foo.java Language Language Bindings ddsgen Bindings Data Representation Data Representation 10110011… 10110011… CDR, TLV, CDR, TLV, JSON, XML JSON, XML DDS-RTPS Wire Interoperability
Web-Enabled Data-Centric Global Data Space App App Web DDS HTTP App RTPS Stateless access of data via Global Data application appropriate technologies and protocols Space Global Data Space Not a bridge, broker, or message router App App 92
Web Enabled DDS App Std. Web Web Enabled DDS App protocols Web Web DDS DDS HTTP HTTP RTPS RTPS <<conceptually>> Global Data Space A service that exposes DDS Global Data over Web Protocols: Applications can interact with DDS directly over the Web No need for bridges or special bindings for scripting languages 93
Day 2: Exercises Gerardo Pardo-Castellote, Ph.D. Co-chair OMG DDS SIG CTO, Real-Time Innovations gerardo.pardo@rti.com The Real-Time Middleware Experts http://www.rti.com
Preparations Install RTI DDS: – Windows • Unzip: RTI_Masterclass2GO.zip – into directory C:RTI • Execute: install_actions.bat – Linux • Boot your computer from the USB Test you can do the following – rtiddsgen -help
Install VisualStudio from the ISO’s Copy the VS2008 and WindowsSDK ISO’s Install DaemonTools Mount the ISOs as virtual drives Proceed with the installation – 1st the VS2008 – 2nd the Platform SDK Test the installation by creating a “hello world” project compiling and running it
Exercise #0 - Hello World Define you data type: Create a directory “HelloWorld” Create a file called hello.idl and open it in VisualStudio Add the following contents: const long MSG_LEN=256; struct HelloMsg { string<MSG_LEN> user; //@key string<MSG_LEN> msg; };
Run rtiddsgen (for C++) rtiddsgen hello.idl -language C++ -example i86Win32VS2005 -replace -ppDisable rtiddsgen hello.idl -language Java -example i86Win32jdk -replace -ppDisable Look at the directory you should see: – hello-vs2005.sln – And Several other files… Open the Solution File (type hello-vs2005.sln on the console) – Look at HelloMsgPublisher.cxx – Look at HelloMsgSubscriber.cxx Build the Solution
Run rtiddsgen (for Java) rtiddsgen hello.idl -language Java -example i86Win32jdk -replace -ppDisable Look at the directory you should see: – makefile_hello_i86Win32jdk – And Several other files… • Look at HelloMsgPublisher.java • Look at HelloMsgSubscriber.java You can use the makefile to build and the Java programs: – gmake –f makefile_hello_i86Win32jdk
Execute the program C++: – On one window run: • objsi86Win32VS2005HelloMsgPublisher.exe – On another window run: • objsi86Win32VS2005HelloMsgSubscriber.exe Java – On one window run: • gmake –f makefile_hello_i86Win32jdk HelloMsgPublisher – On another window run: • gmake –f makefile_hello_i86Win32jdk HelloMsgSubscriber You should see the subscribers getting an empty string…
Modify the program to produce something C++: Open HelloMsgPublisher.cxx in VisualStudio Java: Open HelloMsgPublisher.java in your preferred tool Look for the comment: /* Modify the data to be sent here */ Add the line: strcpy_s(instance->msg, MSG_LEN, "Hello this is gerardo"); Use your own name instead of “gerardo” Kill the Publisher, Rebuild the publisher and run it again
Playing with rtiddsspy Run rtiddsspy while the other applications are running Start and stop applications. What do you see in rtiddsspy
Exercise #1 – Shapes Publisher Create a new directory Shapes In the Directory create a file called ShapeType.idl Edit the file to have the following content: const long COLOR_LEN=64; struct ShapeType { string<COLOR_LEN>color; //@key long x; long y; long shapesize; }; Run: rtiddsgen ShapeType.idl -language C++ -example i86Win32VS2005 –replace -ppNotRun
Exercise #2 – Using keys Create a new directory Chat In the Directory create a file called chat.idl Edit the file to have the following content: const long NAME_LEN=64; const long MSG_LEN=256; struct ChatMsg { string<NAME_LEN>name; //@key long age; string<MSG_LEN> chatRoom; string<MSG_LEN> msg; }; Run: rtiddsgen chat.idl -language C++ -example i86Win32VS2005 – replace -ppNotRun
Edit the chat_publisher.cxx Go to the line with comment: /* Main loop */ – Add the line: strcpy_s(instance->name, NAME_LEN, "Gerardo Pardo"); (Use your own name) Go to the line with comment: – /* Modify the data to be sent here */ – Add the lines: instance->age = count; strcpy_s(instance->msg, NAME_LEN, “Como va todo?"); (Use your age and personalized message) Rebuild and execute
Exercise #3 Use Qos Set RELIABILITY Set HISTORY to KEEP_LAST or KEEP_ALL – Test different depths Use Partitions – Create several Partitions: • E.g. by ChatRoomName – Publish in your ChatRoom – Subscribe to one or more ChatRooms
Exercise #4 Use content filters Edit the chat_subscriber.cxx Add the lines: DDSContentFilteredTopic *cftopic; DDS_StringSeq filter_params; filter_params.maximum(0); cfTopic = participant-> create_contentfilteredtopic( "Selected Chats", topic, "age > 4", filter_params); Look of the call to create_datareader – Replace “topic” with “cfTopic” in the paramater list.
Exercise #5 Use Exclusive Ownership Set up in pairs edit the chat_publisher.cxx and use the same “name” for both of you Re-run the publisher application you will see mixed messages. Edit the chat_publisher.cxx Before creating the data writer add the lines publisher->get_default_datawriter_qos(dwq); dwq.ownership.kind = DDS_EXCLUSIVE_OWNERSHIP_QOS; dwq.ownership_strength.value = 10; Replace DDS_DATAWRITER_QOS_DEFAULT with dwq In the create_datawriter() call Edit the chat_subscriber.cxx Before creating the data reader add the lines DDS_DataReaderQos drq; subscriber->get_default_datareader_qos(drq); drq.ownership = DDS_EXCLUSIVE_OWNERSHIP_QOS; Replace DDS_DATAWRITER_QOS_DEFAULT with drq in the create_datareader() call
Summary Reduces software lifecycle Most widely proven and costs mature implementation – Loose coupling Highest performance – Replaces need for custom middleware in Industry-leading expertise high-performance, and services capability real-time applications Free trial, research and Reduces risk IR&D licenses – Standards-compliant API and wire protocol Comprehensive VxWorks – Multiple implementations support – Widely adopted

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RTI Data-Distribution Service (DDS) Master Class - 2010

  • 1.
    DDS: A Next-GenerationApproach to Building Distributed Real-Time Systems Gerardo Pardo-Castellote, Ph.D. Co-chair OMG DDS SIG CTO, Real-Time Innovations gerardo.pardo@rti.com The Real-Time 2010 Quickstart Middleware Experts http://www.rti.com
  • 2.
    Outline Overviewof Technology – Background – Applications – Data-Centric Pub-Sub – Quality of Service – Add-on components Application development cycle Architecting data-centric systems & modeling the Data Protocol, Performance & Scalability. Integrating external and legacy systems. Future directions and Standards:
  • 3.
    Challenge: More Data, More Speed, More Sources TRENDS: Growing Information Volume Lowering Decision Latency Increasing System Availability Accelerating technology insertion and deployment Next-generation systems needs: Performance Scalability Robustness & Availability Platform Integration & Evolution Safety-Critical Certification Security © 2009 Real-Time Innovations, Inc. 3
  • 4.
    Solution: Standards-based Integration Infrastructure for Real-Time Applications Streaming Sensors Events Data Data Distribution Service Real-Time Enterprise Actuators Applications Applications © 2009 Real-Time Innovations, Inc. 4
  • 5.
    Architecture for thenext-generation systems Existing technologies are reaching robustness/performance/scalability limits RTI DDS brings a fundamental new architecture and approach – Fully decentralized, peer-to-peer, “no bottlenecks” architecture – Superior Wire Protocol – Powerful data-centric model – Built-in Robustness and High-Availability – Standards-based, multi-platform Brokers as choke-points RTI Approach Single-lane traffic No prioritization
  • 6.
    History: DDS the Standards Data Distribution Service for Real-Time Systems – API for Data-Centric Publish-Subscribe distributed systems – Adopted in June 2003 – Revisions: 2004, 2005, 2006 – Spec version 1.2: formal/2007-07-01 Interoperability wire protocol – Adopted in July 2006 – Revised in July 2007 – Spec version 2.1: formal/2009-01-05 Related specifications – UML Profile for DDS – DDS for Light-Weight CCM – Extensible Topics for DDS(*) Multiple (7+) Implementations 6
  • 7.
    Open Architecture Cross-vendor portability Vendor independent – API for portability – Wire protocol for interoperability Multiple implementations DDS API – 7 of API – 4 support RTPS (+1 non-DDS) Middleware Heterogeneous – C, C++, Java, .NET (C#, C++/CLI) Real-Time – Linux, Windows, VxWorks, other Publish-Subscribe embedded & real-time Wire Protocol (RTPS) Loosely coupled Cross-vendor interoperability © 2009 Real-Time Innovations, Inc. 7
  • 8.
    RTI DDS ApplicationExamples Aegis Weapon System ScanEagle UAV Lockheed Martin Boeing Radar, weapons, displays, Sensors, ground station C2 Advanced Cockpit Ground B-1B Bomber Control Station Boeing Predator and SkyWarrior UAS C2, communications, General Atomics weapons Telemetry data, multiple workstations Common Link Integration Processing (CLIP) Northrop Grumman RoboScout Standards-compliant Base10 interface to legacy and new tactical data links Internal data bus and link to communications center Air Force, Navy, B-1B and B-52 © 2009 Real-Time Innovations, Inc. 8
  • 9.
    RTI DDS ApplicationExamples Driver safety Multi-ship simulator Volkswagen FORCE Technology vision systems, analysis, driver Controls, simulation display information systems Medical imaging Mobile asset tracking NMR and MRI Wi-Tronix Sensors, RF generators, user GPS, operational status interface, control computers over wireless links Automated trading Automated Trading Desk (ATD, Highway traffic monitoring now Citigroup) City of Tokyo Market data feed handlers, pricing engines, algorithmic Roadway sensors, roadside trading applications kiosks, control center © 2009 Real-Time Innovations, Inc. 9
  • 10.
    RTI DDS ApplicationExamples Full-immersion simulation Signal Processing National Highway Transportation PLATH GMBH Safety Authority RTI supports modular Migrated from CORBA, DCOM programming across for performance product line Air-Traffic Management INDRA. Deployed in Large Telescopes UK, Germany, Spain European Southern Standards, Performance, Observatory Scalability Performance & Scalability 1000 mirrors, 1sec loop Radar Systems Industrial Control AWACS upgrade Schneider Electric Evolvability, Mainteinability, VxWorks-based PLCs and supportability communicate via RTI-DDS © 2009 Real-Time Innovations, Inc. 10
  • 11.
    Standards Focus Object Management Group – Board of Directors member – Authored DDS and RTPS specs, co-chair SIG Open Group Network Centric Operations Industry Consortium (NCOIC) – Chair Open Standards and Patterns Working Group STAC Benchmark Council Support and integrate with: – DDS, RTPS, JMS, SQL, Web Services, CORBA, UML, HLA, JAUS, Eclipse, IPv6… 11 © 2008 Real-Time Innovations, Inc.
  • 12.
    Corporate Background Foundedby Stanford researchers Focused on real-time middleware Solid financials – 16-year track record of growth Real-Time Market Leader – #1 market share in embedded middleware of all types1 – 70+% worldwide share of DDS market2 50/50 software and services 1Embedded Market Forecasters 2VDC Analyst Report 12
  • 13.
    RTI Supports allPhases of Development Services Engagement Description Capabilities Timeline Workshop 2 days Introduction to RTI products and capabilities QuickStart 2+ days In-depth training on RTI DDS API, QoS policies, and common architecture patterns Support On-Demand Web-portal, phone and email customer lauded support Architecture Study 3-4 weeks Custom design review, risk analysis and architecture recommendations Design Support 4+ weeks Support hardware & software integration, architecture design, Package performance tuning, on-site debugging, implementation support Integration & SOW supported Custom feature, tool and software development support Development Ports As needed RTI tools and software on your special, purpose built hardware
  • 14.
  • 15.
    Benefits of theDDS approach Simple & Powerful Data-Centric Pub-Sub Model Reduces Risk and Development/Integration Time Enhances effective performance by delivering the right data to the right place with the right QoS Standards-based: API and Protocol 1. Unsurpassed Performance and Scalability Priority-aware no choke-points architecture 2. Builds higher quality systems and lowers TCO Built-in high-value capabilities Handles Availability & other Messaging Event Database “hard problems” & Caching Processing Bridge SQL Easy to maintain and Evolve Persistence Redundancy Recording & Durability & Failover Leverage multicore © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 15
  • 16.
    Data-Centric Pub-Sub Model Essentiallya virtual, decentralized global data space Source (key) Latitude Longitude Altitude UAV1 37.4 -122.0 500.0 UAV2 40.7 -74.0 250.0 UAV3 50.2 -0.7 2000.0 Persistence Recording Service Service © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 16
  • 17.
    example Data-Centric Model “Global DataSpace” generalizes Subject-Based Addressing – Data objects addressed by DomainId, Topic and Key – Domains provide a level of isolation – Topic groups homogeneous subjects (same data-type & meaning) – Key is a generalization of subject • Key can be any set of fields, not limited to a “x.y.z …” formatted string Data Reader Data Writer Topic (subject) Key Data Object Data Reader Data Writer Data Reader Data Writer
  • 18.
    ShapesDemo Demo: Publish-Subscribe © 2009 Real-Time Innovations, Inc. 18
  • 19.
    example DDS communications model Data Domain Data Domain New Writer Participant Reader Participant “Alarm” Got new “Alarm” subscriber! data Offered Requested Listener QoS Listener QoS Participants scope the global data space (domain) Topics define the data-objects (collections of subjects) Writers publish data on Topics Readers subscribe to data on Topics QoS Policies are used configure the system Listeners are used to notify the application of events
  • 20.
    ShapesDemo Demo: Real-Time Quality of Service Content filter Time-based filter History Deadline Analyzer © 2009 Real-Time Innovations, Inc. 20
  • 21.
    Real-Time Quality ofService (QoS) QoS Policy QoS Policy DURABILITY USER DATA User QoS HISTORY TOPIC DATA Volatility READER DATA LIFECYCLE GROUP DATA WRITER DATA LIFECYCLE PARTITION Presentation LIFESPAN PRESENTATION Infrastructure ENTITY FACTORY DESTINATION ORDER RESOURCE LIMITS OWNERSHIP Redundancy Transport RELIABILITY OWNERSHIP STRENGTH Delivery TIME BASED FILTER LIVELINESS DEADLINE LATENCY BUDGET CONTENT FILTERS TRANSPORT PRIORITY © 2009 Real-Time Innovations, Inc. 21
  • 22.
    20X Faster thanJMS / Broker-based solutions RTI DDS is about 20X faster than JMS 60,000 Throughput with a single publisher ( 2KB messages ) 50,000 Point-to-Point Updates per Second 45 40 RTI DDS 40,000 1-1 1-10 JMS [1000's sample/s] 35 30,000 1-24 30 25 20,000 20 10,000 15 10 0 16 32 64 128 256 512 1024 5 Message/Data Size (bytes - without batching) 0 2 4 6 9 11 18 CPU load [%] RTI DDS reliable multicast exhibits near perfect scalability Platform: Linux 2.6 on AMD Athlon, Dual core, 2.2 GHz © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 22
  • 23.
    DDS Is Scalable Going from 1 to 888 Ultra-low latency and jitter subscribers of the same – Deterministic – No intermediaries data has only a 10% impact on throughput http://www.rti.com/products/dds/benchmarks-cpp-linux.html © 2009 Real-Time Innovations, Inc. 23
  • 24.
    Realizing Performance &Scalability RTI DDS Approach Others: Broker-based middleware AMQP RTPS unspecified DDS operates peer-to-peer, without brokers DDS uses RTPS, an Advanced Multi-Session protocol supporting Reliable Multicast © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 24
  • 25.
    DDS Enables Higherquality, Lower TCO Systems Pre-built components address many challenging use-cases Presence Discovery Historical Cache Durable Data Availability Redundancy & Failover Recording Database Connectivity Web Accessibility Transformation Messaging Event Database & Caching Processing Bridge SQL Event Processing Redundancy WAN Routing Recording Persistence & Durability & Failover Security Guard Hooks © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 25
  • 26.
    Outline Overviewof Technology Application development cycle – How to begin. Hello world example. – Defining data in XML and XSD – Development and Run-time Tools: Ping, Spy, Analyzer, Wireshark, Excel – Discovery and Builtin-Topics – Configuring QoS via XML files Architecting data-centric systems & modeling the Data Protocol, Performance & Scalability. Integrating external and legacy systems. Future directions and Standards:
  • 27.
    Hands-on Example (C++) Type Three minutes to a running app!! Definition 1. Define your data 2. Create your project MyType.idl 3. Build 4. Run: publisher subscriber MyType.sln rtiddsgen MyType.h MyTypePublisher.cpp Aux: MyTypeSupport.c File Browser MyTypeSubscriber.cpp Console compiler Delete Files rtiddsspy Publisher.exe Subscriber.exe
  • 28.
    Alternatives beyond IDLand CDR Type Representation Type Representation “Foo” schema “Foo” schema IDL, XSD, … IDL, XSD, … rtiddsgen DDS API DDS-API DDS-API Foo.h, Foo.h, DataWriter<Foo> DataReader<Foo> Foo.java Foo.java Language Language Bindings ddsgen Bindings Data Representation Data Representation 10110011… 10110011… CDR, TLV, CDR, TLV, JSON, XML JSON, XML DDS-RTPS Wire Interoperability
  • 29.
    Alternative Type DescriptionLanguages rtiddsgen supports 4 alternative ways to define types: – All are equivalent Exercise: – You can convert between all these formats - Start with an IDL Type - Convert to XML IDL - Convert to XSD – + Simple, Compact, Similar to C/C++/Java - Start with an XML-defined type – + Allows type sharing with CORBA - Convert to IDL – - Perceived as “legacy” – - Limited tool support - Convert to XSD XML – + Good tool support and syntax validation – + Familiar to a large community. Fashionable – - More verbose. Custom Syntax XSD – + Good tool support – + Commonly used as a type-description language – - Cumbersome syntax for certain types. Not human friendly WSDL – + Same as XSD and allows type sharing with Web-Services – - Same as XSD
  • 30.
    rtiddsgen Details rtiddsgen [-d<outdir>] [-language <C|C++|Java|C++/CLI|C#>] [-namespace] [-package <packagePrefix>] [-example <arch>] [-replace] [-debug] [-corba [client header file]] [-optimization <level of optimization>] [-stringSize <Unbounded strings size>] [-sequenceSize <Unbounded sequences size>] [-notypecode] [-ppDisable] [-ppPath <path to the preprocessor>] [-ppOption <option>] [-D <name>[=<value>]] [-U <name>] [-I <directory>] [-noCopyable] [-use42eAlignment] [-help] [-version] [-convertToIdl | -convertToXml | -convertToXsd | -convertToWsdl] [[-inputIdl] <IDLInputFile.idl> | [-inputXml] <XMLInputFile.xml> | [-inputXsd] <XSDInputFile.xsd> | [-inputWsdl] <WSDLInputFile.wsdl>] DefinitionFile can be IDL, XSD and XML file -example generates example pub/sub apps and makefiles for compilation. -replace replaces everything that’s generated. Use if the data type definition has changed. Always use with caution if you’ve made modifications.
  • 31.
    IDL vs. XML:IDL Example struct MemberStruct{ short sData; } typedef MemberStructType; //@top-level false 31
  • 32.
    IDL vs. XML:XML Example <?xml version="1.0“ encoding="UTF-8"?> <types xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="../rti_dds_topic_types.xsd"> <struct name="MemberStruct" topLevel="false"> <member name="sData“ type="short"/> </struct> <typedef name="MemberStructType" type="nonBasic“ nonBasicTypeName="MemberStruct“ topLevel="false"/> </types> 32
  • 33.
    IDL vs. XSD:XSD Example <?xml version="1.0" encoding="UTF-8"?> <xsd:schema xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:dds="http://www.omg.org/dds" xmlns:tns="http://www.omg.org/IDL- Mapped/" targetNamespace="http://www.omg.org/IDL-Mapped/"> <xsd:import namespace="http://www.omg.org/dds" schemaLocation="rti_dds_topic_types_common.xsd"/> <xsd:complexType name="MemberStruct"> <xsd:sequence> <xsd:element name="sData" minOccurs="1" maxOccurs="1" type="xsd:short"/> </xsd:sequence> </xsd:complexType> <!-- @topLevel false --> <xsd:complexType name="MemberStructType"> <xsd:complexContent> <xsd:restriction base="tns:MemberStruct"> <xsd:sequence> <xsd:element name="sData" type="xsd:short" minOccurs="1" maxOccurs="1"/> </xsd:sequence> </xsd:restriction> </xsd:complexContent> </xsd:complexType> <!-- @topLevel false --> 33 </xsd:schema>
  • 34.
    ShapesDemo Data Persistence Persistence_svc A standalone service that persists data outside of the context of a DataWriter Data Data Writer Data Can be configured for: Writer Reader • Redundancy • Load balancing Data • Direct for performance Reader Global Data Space • Relay/Transactional • Redundant/ Fault-tolerant Persistence Persistence Service Service Permanent Permanent Storage Storage © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 34
  • 35.
    Data Persistence A standalone service that persists data outside of the context of a DataWriter Data Can be configured for: Data Writer Data Writer Reader • Redundancy • Load balancing Data Global Reader Data Space Demo: 1. PersistenceService 2. ShapesDemo 3. Application failure Persistence Persistence 4. Application re-start Service Service 5. Persistence Svc failure Permanent Permanent 6. Application re-start Storage Storage © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 35
  • 36.
    ShapesDemo Ownership and High Availability Producer / Writer K1 Pri strength=10 mary Topic T1 Producer / Writer K1 Backup strength=5 K2 Primary K1 K2 Producer / Writer K2 Backup strength=1 Owner determined per Topic and Key Only writer with highest strength can publish a Key Automatic failover when highest strength writer: – Loses liveliness – Misses a deadline – Stops writing the subject Shared Ownership allows any writer to update any object © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 36
  • 37.
    Outline Overviewof Technology Application development cycle Architecting data-centric systems & modeling the Data – Examples: News example, Data Streaming, Commands, Video – Data Persistence with Examples – Using DynamicData Protocol, Performance & Scalability. Integrating external and legacy systems. Future directions and Standards:
  • 38.
    Components/Mechanics of theGDS Application Application GDS Definition Discovery Application Application Listener Listener Protocol Cache Management Cache Management
  • 39.
    Components/Mechanics of theGDS Application Application Domain Modeling & Sys. Design GDS Definition Discovery Application Config & Application Qos Listener Listener Protocol Cache Management Cache Management
  • 40.
    Designing a Data-CentricSystem Define/Model the Global Data Space Configure the Cache Management Configure Discovery Configure the Protocol Configure/Use hooks for – Fault detection – Controlled access
  • 41.
    Global Data Space/ Global State Identify the number of domains Domain Information model – Topics – Types – Keys – Ownership
  • 42.
    Domain and DomainParticipants N4 App 4 • Container for N1 App 1 Pub/Sub Pub/Sub applications that (A,B/C,D) (D/C,E,F) want to communicate • Applications can N2 App 2 N4 App 5 join or leave a Subscribe Domain Publish domain in any order (C) (C) • New Applications are “Auto-Discovered” N3 App 3 N5 App 6 • An application that Pub/Sub Subscribe (E,F/A,C) (B,C) has joined a domain is also called a “Domain Participant” Single ‘Domain’ System
  • 43.
    Domain and DomainParticipants Using Multiple domains for Scalability, Modularity & Isolation Node 1 - App 1 Node 4 - App 1 Pub/Sub Domain A Pub/Sub Node 2 - App 1 Node 4 - App 2 Subscribe Publish Domain B Node 3 - App 1 Node 5 - App 1 Pub/Sub Subscribe Domain C Node 5 - App 2 Added Func. Node 6 - App 1 Pub/Sub Pub/Sub demo_domain_0 Multiple Domain System demo_domain_1
  • 44.
    Topics & Datatypes,Keys & Subjects Topic “MarketData” Data-type (name-type-value pairs) source type symbol Exchange volume bid ask OPRA IBM NYSE 200000 118.30 118.36 OPRA AAPL NASDAQ 171.20 171.28 RTFP EQ Key fields Subject Additional fields (payload) Topic “OrderEntry” Exchange type Symbol Order num number limit stop expiration NYSE BUY IBM 11956 500 120 - DAY NYSE BUY IBM 11957 1000 124.5 124 DAY NASDAQ SELL AAPL 11958 400 - 160 DAY Subject Key fields demo_filters
  • 45.
    QoS: Ownership Specifieswhether more than one DataWriter can update the same instance of a data-object Ownership = EXCLUSIVE Ownership = SHARED “Only highest-strength “All data-writers can data writer can update each update data- each data-instance” instance” Data Data Data Data Data Data Writer Writer Writer Writer Writer Writer Data- Data- Instance Instance Provides fast, robust, transparent replacement for fail- over and/or take-over.
  • 46.
    demo_ownership QoS: Ownership Strength Specifieswhich DataWriter is allowed to update the values of data-objects OWNERSHIP_STRENGTH “Integer to specify the ORDER strength of an instance” Strength = 1 Strength = 4 Domain Participant Data AfterData Expires QoS Data “RIGHT” Writer Writer Reader “LEFT” - Deadline “RIGHT” “LEFT” - Liveliness Publisher Publisher Subscriber S S Note: Only applies to Topics with Ownership = Exclusive
  • 47.
    Configure the CacheManagement Cache State Content – History Writer Application – Lifespan – Persistence – Resources Reader Cache View – Partitions – Content-Based Filter – Time-Based Filter – Order Reader Application
  • 48.
    QoS: History –Last x or All KEEP_ALL: KEEP_LAST: “depth” integer for Publisher: keep all until delivered the number of samples to keep at Subscriber: keep each sample until the any one time application processes that instance demo_history S7 S7 Data Data S7 Data S6 S6 Writer Writer S6 Reader S5 S5 S4 S4 S3 Publisher S2 Publisher Subscriber S1 Keep All Keep Last 2 Keep Last 4 S7 S6 S5 S4 S3 S2 S1
  • 49.
    lifespan_pub QoS: Lifespan lifespan_sub User can set lifespan duration Manages samples in the history queues, attached to each Sample Topic S1 Data Data Writer Reader S2 S3 S4 S5 Perm. Publisher Subscriber S6 Storage S7 S1 S2 S4 S3 S2 S1
  • 50.
    Content-Based Filtering Instance1 Value = 249 Content Filtered Value = 230 Topic Instance 2 Instance 3 Value = 275 Topic Instance 4 Value = 262 “Filter Expression ” Instance 5 Value = 258 Ex. Value > 260 Instance 6 Value = 261 Instance 7 Value = 259 The Filter Expression and Expression Params will determine which instances of the Topic will be received by the subscriber. content_filter_example
  • 51.
    QoS: TIME_BASED_FILTER time_filter_example Topic “minimum_separation”: Data Reader does not want to receive data Domain faster than the min_separation time Participant Data Data Writer Reader Publisher Discarded Subscriber samples S SSS S S S minimum separation Data Samples
  • 52.
    Cache Management inAction Topics – Square, Circle, Triangle – Attributes Data types (schemas) – Shape (color, x, y, size) • Color is instance Key – Key • Color field used for key QoS – History, Partition – Time-Based Filter – Content-Based Filter demo
  • 53.
    Configure the Protocol Discovery Reliability Writer Application Batching Liveliness Flow Control Asynchronous write Network Configuration – Enabled Transports + transport properties – Multicast addresses – Transport Priority OS settings – Threads Reader Application – Memory
  • 54.
    Tunable Reliability Protocol Configurable AckNack reply times Performance can be tracked by to eliminate storms senders and recipients – Configurable high/low watermark, Fully configurable to bound Buffer full latency and overhead Flexible handling of slow – Heartbeats, delays, buffer sizes recipients – Dynamically remove slow receivers S7 S1 Data S6 Reliable Data S2 Writer Reader S5 •Guaranteed S3 S4 Ordered Delivery S4 S3 S5 Publisher •“Best effort” also Subscriber S6 S2 S1 supported S7 S8 S7 S3 S5 S4 S2 S1 NACK #6 S6
  • 55.
    Configure Notifications, FaultDetection & Management Writer Application Listeners Deadline Qos Liveliness Qos Built-in Readers Notification of matching Reader Application
  • 56.
    QoS: Deadline deadline_example Topic DEADLINE “deadline period” Commits Failed to to provide get data Data data each Data Writer deadline Listener Reader period. Publisher Expects data every Subscriber deadline period. deadline S X S S S S S
  • 57.
    QoS: Liveliness – liveliness_example Type and Duration Type: Controls who is responsible for issues of ‘liveliness packets’ AUTOMATIC = Infrastructure Managed MANUAL = Application Managed kill_apps Topic Topic Failed to renew lease Data Data Writer Listener Reader Publisher Subscriber lease_duration LP LP LP S Liveliness Message
  • 58.
    Exercise: How could“chat rooms” be implemented? Different Topics for each Chat room? Map to Partitions? Add field to the message and use content- filtered Topics? Same as before and also make room part of the Key? Others? Discuss pros and cons of each approach
  • 59.
    Exercise: How couldwe implement Ground control stations that monitor UAVs Different Topics for each UAV? – Or use Keys? Different Domains for each Ground Station? – Or Partitions? How to control multiple UAVs from the same ground station? How to switch the ground station that controls the UAV? How to do failover between ground stations? How to direct a message to one or all UAVs? How to detect loss of connection to an UAV? Discuss pros and cons of each approach
  • 60.
    Outline Overviewof Technology Application development cycle Architecting data-centric systems & modeling the Data Protocol, Performance & Scalability. – Details on Reliable Protocol – Latency and Throughput – Using RTI’s LatencyTest and Perftest – Batching – Asynchronous writes & FlowController – Maximizing latency and Throughput Integrating external and legacy systems. Future directions and Standards:
  • 61.
    Performance & Scalability RTI DDS is about 20X faster than JMS 60,000 Throughput with a single publisher ( 2KB messages ) 50,000 Point-to-Point Updates per Second 45 40 RTI DDS 40,000 1-1 1-10 JMS [1000's sample/s] 35 30,000 1-24 30 25 20,000 20 10,000 15 10 0 16 32 64 128 256 512 1024 5 Message/Data Size (bytes - without batching) 0 2 4 6 9 11 18 CPU load [%] RTI DDS reliable multicast exhibits near perfect scalability Platform: Linux 2.6 on AMD Athlon, Dual core, 2.2 GHz © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 61
  • 62.
    Extremely low latencyand jitter Reliable, ordered delivery over Gigabit Ethernet between 2.4 GHz Core 2 Quad processors running 32-bit Red Hat Enterprise Linux 5.0 © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 62
  • 63.
    Orders of magnitudemore scalable than broker-based solutions Going from 1 to 888 New topics can be added to a subscribers of the same system without impacting the data has only a 10% impact latency and throughput on on throughput other topics Throughput with 8 topics is 8x the throughput with 1 topic http://www.rti.com/products/dds/benchmarks-cpp-linux.html © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 63
  • 64.
    Realizing Performance &Scalability RTI Approach Others: Broker-based middleware AMQP RTPS unspecified RTI operates peer-to-peer, without brokers RTI uses RTPS, an Advanced Multi-Session protocol supporting Reliable Multicast © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 64
  • 65.
    Advanced Scalability &Performance Techniques Latency and Priority Aware message batching Content-Aware multi-channel reliable multicast Enhanced Reliable Protocol – Selective ACKs (SACKs) for Confirmed Reliability – NACK-only Reliable Protocol for Massive Scalability Smart caching integrated with the message protocol Content-Filtering at the source © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 65
  • 66.
    Message Batching Send queue Receive queue write() sender receiver Without batching each message is separately sent. For small messages protocol headers might be bigger than payload Send queue Receive queue With batching messages are held a little and Transparent: write() combined into larger Receiver still batches maximizing sees individual sender throughout and minimizing messages CPU receiver © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 66
  • 67.
    Reliability with Batching Reliability must work even when messages are batched ACK or NACK of individual samples would negate some of the benefits of batching… => Protocol must be batch aware so that it can ACK/NACK complete batches! write() B1 B1 sender B1 B2 B2 B2 B3 B3 B3 receiver ACK(B3), NACK(B2) Repair B2 © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 67
  • 68.
    Batching is hardbut it pays! RTI DDS 4.3b perftest results 1000 900 800 Throughput (Mbps) 700 Linux Baseline 600 Linux 10Kb Batch 500 400 300 200 100 0 0 1000 2000 3000 4000 5000 Intel Core2Duo Single-CPU Dual-Core 2.4GHz, 4MB cache Sample size (bytes) 32-bit CentOS 5 (RHEL 5), 2GB memory, Intel E1000 NIC © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 68
  • 69.
    Classic (TCP Style)Reliable Protocol No packet loss situation 01 01 01 02 02 02 03 03 03 04 04, HB ACK 1-4 04 05 05 05 06 06 06 07 07 07 08 08, HB ACK 1-8 08 ShapesDemo Company Confidential © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 69
  • 70.
    Classic (TCP Style)Reliable Protocol with some packet loss 01 01 01 02 02 02 03 03 X 04 04, HB X ACK 1-2, NACK 3 05 05 X 03 06 04 06 07 06 05 07 08 07 08 08, HB ACK 1-8 Packets 04 and 05 are received but the protocol drops them because a prior packet 03 is missing. This wastes valuable bandwidth © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 70
  • 71.
    RTI DDS Reliability(Reader Cache + SACK) improves performance when packet loss occurs 01 01 01 02 02 02 03 03 X 04 04, HB ACK 1-2, SACK 3 04 05 05 03 05 06 06 06 07 07 07 08 08, HB ACK 1-8 08 Packets 04 and 05 are received and cached waiting for the repair of 03. No bandwidth is wasted. © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 71
  • 72.
    RTI DDS NACK-onlyreliability eliminates ACK traffic if there no packet loss 01 01 01 02 02 02 03 03 03 04 04, HB 04 05 05 05 06 06 06 07 07 07 08 08, HB 08 No ACK traffic under normal operating conditions © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 72
  • 73.
    RTI DDS NACK-onlyreliability greatly reduces traffic even with packet loss 01 01 01 02 02 02 03 03 X 04 04, HB NACK 3 04 05 05 03 05 06 06 06 07 07 07 08 08, HB 08 Negative Acknowledgments sent only when some message is lost This approach is far more scalable when there are many subscribers © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 73
  • 74.
    Asynchronous Publishing &Flow COntroller synchronous send path: asynchronous send path: Return To User 1. rapid write() return User Thread write() Data Queue 3. coalesce data samples 2. network traffic shaping Flow Flow Publishing Thread Net I/O Wire Data Out Controller
  • 75.
    Qos Policies DDS_PublishModeQosPolicy – kind • DDS_SYNCHRONOUS_PUBLISH_MODE_QOS • DDS_ASYNCHRONOUS_PUBLISH_MODE_QOS – flow controller name DDS_AsynchronousPublisherQosPolicy – disable_asynchronous_write <FALSE> – thread – disable_asynchronous_batch <FALSE> – asynchronous_batch_thread
  • 76.
    AP in Participant’sWorld Participant Publishing Thread D Publisher D Publishing Thread A Publishing Thread B Publishing Thread C Publisher A Publisher B Publisher C
  • 77.
    Flow Controller TokenDistribution basic token bucket – steady-state traffic • tokens_added_per_period • token_period – max burst control • max_tokens – Additional controls • tokens_leaked_per_period • all values can be DDS_LENGTH_UNLIMITED – piggyback discount (Token Exchange Rate) • bytes_per_token scheduling policy – round-robin (RR) – earliest-deadline-first (EDF) • deadline = time of write + DDS_DataWriterQos::latency_budget
  • 78.
    Using Asynchronous Publishing DataWriter-side: DDS_FlowControllerProperty_t property; property.sched_policy = DDS_EDF_FLOW_CONTROLLER_SCHED_POLICY; property.token_bucket.max_tokens = A; //[0, DDS_LENGTH_UNLIMITED] property.token_bucket.tokens_added_per_period = B; //[0, DDS_LENGTH_UNLIMITED] property.token_bucket.tokens_leaked_per_period = C; //[0, DDS_LENGTH_UNLIMITED] property.token_bucket.bytes_per_token = D; //[1024, DDS_LENGTH_UNLIMITED] property.token_bucket.period = E; //[0, DDS_DURATION_INFINITE] ... DDSFlowController *controller = participant->create_flowcontroller("superflow", property); ... writer_qos.publish_mode.kind = DDS_ASYNCHRONOUS_PUBLISH_MODE_QOS; writer_qos.publish_mode.flow_controller_name = "superflow"; // // Set up History queue size to hold deferred issues!!!!!. writer_qos.history.kind = DDS_KEEP_LAST_HISTORY_QOS; writer_qos.history.depth = z; // <<<<<<<<<<<<<!!!!!!!!!!!!!!<<<<< ... writer->write(data_sample, ...); ... // Optional wait for pipe to empty writer->wait_for_asynchronous_publishing(timeout); ... // Optional On-Demand trigger controller->trigger_flow(); ... // Optional Modify controller properties controller->set_property(); No changes on DataReader-side!!
  • 79.
    Flow Controller DesignChallenge Requirements; – Large 1 mbyte issue. – Transmit over period of 10 seconds – Low priority transmission – Transport buffer size set to 32K CONTROLLER: property.sched_policy = ??; property.token_bucket.max_tokens = ?? property.token_bucket.tokens_added_per_period = ?? property.token_bucket.tokens_leaked_per_period = ??; property.token_bucket.bytes_per_token = ??; property.token_bucket.period = ??
  • 80.
    Flow Controller DesignChallenge Requirements; – Large 1 mbyte issue. – Transmit over period of 10 seconds – Low priority transmission – Transport buffer size set to 32K – Cannot loose any issues CONTROLLER: property.sched_policy = DDS_RR_FLOW_CONTROLLER_POLICY; property.token_bucket.max_tokens = 1 property.token_bucket.tokens_added_per_period = 1 property.token_bucket.tokens_leaked_per_period = unlimited; property.token_bucket.bytes_per_token = 32k; property.token_bucket.period = 200ms Extra Credit Discussion: What about reliable protocol properties?
  • 81.
    Outline Overviewof Technology Application development cycle Architecting data-centric systems & modeling the Data Protocol, Performance & Scalability. Integrating external and legacy systems. – Routing Service – Systems of Systems – Cross Domain Solutions – Accessing Data over a WAN – Database Connectivity – Access over the Web Future directions and Standards:
  • 82.
    Real-Time Recording Service Applications: Record high-rate data – Future analysis and arriving in real-time debugging Non-intrusive – multicast – Post-mortem reception – Compliance checking – Replay for testing and simulation purposes Demo: 1. Start RecordingService 2. Start ShapesDemo 3. See output files 4. Convert to: HTML XML 5. View Data: HTML XML sqlite stop_all © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 82
  • 83.
    Relational Database Integration Publish-SubscribeAction Relational Actions Write() ShapesDemo UPDATE [2,3] & INSERT Read() & Take() SELECT Dispose() DELETE Wait() & Listener sql_gui sql_shell Topic T1 Table T1 RTI I1 Real-Time I1 I2 I3 Connect I2 I3 2. start RTC 1. start mysql stop_all Event driven – The fastest way to observe database changes! © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 83
  • 84.
    COTS tools: Excel– Interacting with your data Display live RTI DDS Data in Excel Perform real-time computations and charts Publish RTI DDS data from Excel ShapesDemo blank saved demo
  • 85.
    RTI Routing Service Selective, real-time data forwarding and transformation Can Change Topic Name and Topic Schema – Allows for custom transformations via “plugin” – Can filter/guard data QoS managed, can cache last-known value for data Dynamically configured Location independent deployment start Topic A Topic B DDS Router ShapesDemo_0 ShapesDemo_1 XFORM GUARD stop_all
  • 86.
    Global Scalability: LANto WAN… …without sacrificing Performance and Security Site A DDS DDS Router Router Site C Topics: Topics: Site Status Site Status Alarms WAN /  Proc Sensor Data Health Logs Result Data Internet Sensor Data Alarms Proc Sensor Data TCP/TLS/SSL Site B DDS DDS Site D Router Router Topics: Topics: Site Status Site Status Sensor Data Result Data © 2009 Real-Time Innovations, Inc. COMPANY CONFIDENTIAL 86
  • 87.
    Web Accessibility Direct access to real-time data from Web-Based Applications simulated tracks Tactical Real-Time Data Web Web Enabled Web Enabled Recorded DDS Enabled Data DDS DDS DDS Recording 2. start Service 1. start replay & router Recorded Recorded Track Files view maps Data Files GUARD 87 stop_all
  • 88.
    Outline Overviewof Technology Application development cycle Architecting data-centric systems & modeling the Data Protocol, Performance & Scalability. Integrating external and legacy systems. Future directions and Standards: – Extensible Topics for DDS – Web Enabled DDS – Standard C++ PSM for DDS Q&A
  • 89.
    Extensible Dynamic TypesSubmission class Ov erv iew +type TypeModel::Type Data * 1 1 1 1 1 +type * TypeRepresentation:: LanguageBinding:: LanguageBinding:: DataRepresentation:: TypeRepresentation TypeLanguageBinding DataLanguageBinding DataRepresentation
  • 90.
    Example: Current mechanisms Type Definition IDL: Foo.idl struct Foo { Language string<> name; Binding long ssn; }; IDL to Language Mapping: Foo.h Foo.cpp Data FooTypeSupport.cpp Representation struct Foo { IDL to CDR: char *name; int ssn; 00000006 }; 68656C6C 6F000000 Foo f = {“hello”, 2”}; 00000002
  • 91.
    Type-Definition – LanguageRepresentation – Serialized encapsulation ... Each offers options Type Representation Type Representation “Foo” schema “Foo” schema IDL, XSD, … IDL, XSD, … ddsgen DDS API DDS-API DDS-API Foo.h, Foo.h, DataWriter<Foo> DataReader<Foo> Foo.java Foo.java Language Language Bindings ddsgen Bindings Data Representation Data Representation 10110011… 10110011… CDR, TLV, CDR, TLV, JSON, XML JSON, XML DDS-RTPS Wire Interoperability
  • 92.
    Web-Enabled Data-Centric GlobalData Space App App Web DDS HTTP App RTPS Stateless access of data via Global Data application appropriate technologies and protocols Space Global Data Space Not a bridge, broker, or message router App App 92
  • 93.
    Web Enabled DDS App Std. Web Web Enabled DDS App protocols Web Web DDS DDS HTTP HTTP RTPS RTPS <<conceptually>> Global Data Space A service that exposes DDS Global Data over Web Protocols: Applications can interact with DDS directly over the Web No need for bridges or special bindings for scripting languages 93
  • 94.
    Day 2: Exercises Gerardo Pardo-Castellote, Ph.D. Co-chair OMG DDS SIG CTO, Real-Time Innovations gerardo.pardo@rti.com The Real-Time Middleware Experts http://www.rti.com
  • 95.
    Preparations InstallRTI DDS: – Windows • Unzip: RTI_Masterclass2GO.zip – into directory C:RTI • Execute: install_actions.bat – Linux • Boot your computer from the USB Test you can do the following – rtiddsgen -help
  • 96.
    Install VisualStudio fromthe ISO’s Copy the VS2008 and WindowsSDK ISO’s Install DaemonTools Mount the ISOs as virtual drives Proceed with the installation – 1st the VS2008 – 2nd the Platform SDK Test the installation by creating a “hello world” project compiling and running it
  • 97.
    Exercise #0 -Hello World Define you data type: Create a directory “HelloWorld” Create a file called hello.idl and open it in VisualStudio Add the following contents: const long MSG_LEN=256; struct HelloMsg { string<MSG_LEN> user; //@key string<MSG_LEN> msg; };
  • 98.
    Run rtiddsgen (forC++) rtiddsgen hello.idl -language C++ -example i86Win32VS2005 -replace -ppDisable rtiddsgen hello.idl -language Java -example i86Win32jdk -replace -ppDisable Look at the directory you should see: – hello-vs2005.sln – And Several other files… Open the Solution File (type hello-vs2005.sln on the console) – Look at HelloMsgPublisher.cxx – Look at HelloMsgSubscriber.cxx Build the Solution
  • 99.
    Run rtiddsgen (forJava) rtiddsgen hello.idl -language Java -example i86Win32jdk -replace -ppDisable Look at the directory you should see: – makefile_hello_i86Win32jdk – And Several other files… • Look at HelloMsgPublisher.java • Look at HelloMsgSubscriber.java You can use the makefile to build and the Java programs: – gmake –f makefile_hello_i86Win32jdk
  • 100.
    Execute the program C++: – On one window run: • objsi86Win32VS2005HelloMsgPublisher.exe – On another window run: • objsi86Win32VS2005HelloMsgSubscriber.exe Java – On one window run: • gmake –f makefile_hello_i86Win32jdk HelloMsgPublisher – On another window run: • gmake –f makefile_hello_i86Win32jdk HelloMsgSubscriber You should see the subscribers getting an empty string…
  • 101.
    Modify the programto produce something C++: Open HelloMsgPublisher.cxx in VisualStudio Java: Open HelloMsgPublisher.java in your preferred tool Look for the comment: /* Modify the data to be sent here */ Add the line: strcpy_s(instance->msg, MSG_LEN, "Hello this is gerardo"); Use your own name instead of “gerardo” Kill the Publisher, Rebuild the publisher and run it again
  • 102.
    Playing with rtiddsspy Run rtiddsspy while the other applications are running Start and stop applications. What do you see in rtiddsspy
  • 103.
    Exercise #1 –Shapes Publisher Create a new directory Shapes In the Directory create a file called ShapeType.idl Edit the file to have the following content: const long COLOR_LEN=64; struct ShapeType { string<COLOR_LEN>color; //@key long x; long y; long shapesize; }; Run: rtiddsgen ShapeType.idl -language C++ -example i86Win32VS2005 –replace -ppNotRun
  • 104.
    Exercise #2 –Using keys Create a new directory Chat In the Directory create a file called chat.idl Edit the file to have the following content: const long NAME_LEN=64; const long MSG_LEN=256; struct ChatMsg { string<NAME_LEN>name; //@key long age; string<MSG_LEN> chatRoom; string<MSG_LEN> msg; }; Run: rtiddsgen chat.idl -language C++ -example i86Win32VS2005 – replace -ppNotRun
  • 105.
    Edit the chat_publisher.cxx Go to the line with comment: /* Main loop */ – Add the line: strcpy_s(instance->name, NAME_LEN, "Gerardo Pardo"); (Use your own name) Go to the line with comment: – /* Modify the data to be sent here */ – Add the lines: instance->age = count; strcpy_s(instance->msg, NAME_LEN, “Como va todo?"); (Use your age and personalized message) Rebuild and execute
  • 106.
    Exercise #3 UseQos Set RELIABILITY Set HISTORY to KEEP_LAST or KEEP_ALL – Test different depths Use Partitions – Create several Partitions: • E.g. by ChatRoomName – Publish in your ChatRoom – Subscribe to one or more ChatRooms
  • 107.
    Exercise #4 Usecontent filters Edit the chat_subscriber.cxx Add the lines: DDSContentFilteredTopic *cftopic; DDS_StringSeq filter_params; filter_params.maximum(0); cfTopic = participant-> create_contentfilteredtopic( "Selected Chats", topic, "age > 4", filter_params); Look of the call to create_datareader – Replace “topic” with “cfTopic” in the paramater list.
  • 108.
    Exercise #5 UseExclusive Ownership Set up in pairs edit the chat_publisher.cxx and use the same “name” for both of you Re-run the publisher application you will see mixed messages. Edit the chat_publisher.cxx Before creating the data writer add the lines publisher->get_default_datawriter_qos(dwq); dwq.ownership.kind = DDS_EXCLUSIVE_OWNERSHIP_QOS; dwq.ownership_strength.value = 10; Replace DDS_DATAWRITER_QOS_DEFAULT with dwq In the create_datawriter() call Edit the chat_subscriber.cxx Before creating the data reader add the lines DDS_DataReaderQos drq; subscriber->get_default_datareader_qos(drq); drq.ownership = DDS_EXCLUSIVE_OWNERSHIP_QOS; Replace DDS_DATAWRITER_QOS_DEFAULT with drq in the create_datareader() call
  • 109.
    Summary Reduces softwarelifecycle Most widely proven and costs mature implementation – Loose coupling Highest performance – Replaces need for custom middleware in Industry-leading expertise high-performance, and services capability real-time applications Free trial, research and Reduces risk IR&D licenses – Standards-compliant API and wire protocol Comprehensive VxWorks – Multiple implementations support – Widely adopted