1 SERVOGrid and Grids for Real- time and Streaming Applications Grid School Vico Equense July Geoffrey Fox Computer Science, Informatics, Physics Pervasive Technology Laboratories Indiana University Bloomington IN

2 Thank you SERVOGrid and iSERVO are major collaborations In the USA, JPL leads project involving UC Davis and Irvine, USC and Indiana university Australia, China, Japan and USA are current international partners This talk takes material from talks by Andrea Donnellan Marlon Pierce John Rundle Thank you!

3 1: Container and Run Time (Hosting) Environment 2: System Services and Features Handlers like WS-RM, Security, Programming Models like BPEL or Registries like UDDI 3: Generally Useful Services and Features Such as “Access a Database” or “Submit a Job” or “Manage Cluster” or “Support a Portal” or “Collaborative Visualization” 4: Application or Community of Interest Specific Services such as “Run BLAST” or “Look at Houses for sale” OGSA and other GGF/W3C/ ……… WS-* from OASIS/W3C/ Industry Apache Axis.NET etc. We will discuss some items at layer 4 and some at layer 1(and perhaps 2) Grid and Web Service Institutional Hierarchy

4 Motivating Challenges 1.What is the nature of deformation at plate boundaries and what are the implications for earthquake hazards? 2.How do tectonics and climate interact to shape the Earth’s surface and create natural hazards? 3.What are the interactions among ice masses, oceans, and the solid Earth and their implications for sea level change? 4.How do magmatic systems evolve and under what conditions do volcanoes erupt? 5.What are the dynamics of the mantle and crust and how does the Earth’s surface respond? 6.What are the dynamics of the Earth’s magnetic field and its interactions with the Earth system? From NASA’s Solid Earth Science Working Group Report, Living on a Restless Planet, Nov. 2002

5 US Earthquake Hazard Map US Annualized losses from earthquakes are $4.4 B/yr

6 Characteristics of Solid Earth Science Widely distributed heterogeneous datasets Multiplicity of time and spatial scales Decomposable problems requiring interoperability for full models Distributed models and expertise Enabled by Grids and Networks

7 Facilitating Future Missions SERVOGrid develops the necessary infrastructure for future spaceborne missions such as gravity or InSAR (interferometric Synthetic Aperture Radar) Satellite. This can measure land deformation by comparing samples

8 Interferometry Basics z Single Pass (Topography) Repeat Pass (Topographic Change) t 1 t 2  change  ( t 1 )  ( t 2 ) t 1 t 2 B A 1 A 2  h

9 The Northridge Earthquake was Observed with InSAR 1993–1995 Interferogram The Mountains grew 40 cm as a result of the Northridge earthquake.

10 Objective Develop real-time, large-scale, data assimilation grid implementation for the study of earthquakes that will: Assimilate (means integrate data with model) distributed data sources and complex models into a parallel high- performance earthquake simulation and forecasting system Real-time sensors (support high performance streams) Simplify data discovery, access, and usage from the scientific user point of view (using portals) Support flexible efficient data mining (Web Services)

11 Data Information Ideas Simulation Model Assimilation Reasoning Datamining Computational Science Informatics Data Deluged Science Computing Paradigm

12 Database Analysis and Visualization Portal Repositories Federated Databases Data Filter Services Field Trip Data Streaming Data Sensors ? Discovery Services SERVOGrid Research Simulations ResearchEducation Customization Services From Research to Education Education Grid Computer Farm Grid of Grids: Research Grid and Education Grid GIS Grid Sensor Grid Database Grid Compute Grid

13 Solid Earth Research Virtual Observatory Web-services and portal based Problem Solving Environment Couples data with simulation, pattern recognition software, and visualization software Enable investigators to seamlessly merge multiple data sets and models, and create new queries. Data Space-based observational data Ground-based sensor data (GPS, seismicity) Simulation data Published/historical fault measurements Analysis Software Earthquake fault Lithospheric modeling Pattern recognition software

14 Component Grids We build collections of Web Services which we package as component Grids Visualization Grid Sensor Grid Management Grid Utility Computing Grid Collaboration Grid Earthquake Simulation Grid Control Room Grid Crisis Management Grid Intelligence Data-mining Grid We build bigger Grids by composing component Grids using the Service Internet

15 Critical Infrastructure (CI) Grids built as Grids of Grids Gas Services and Filters Physical Network RegistryMetadata Flood Services and Filters Flood CIGrid Gas CIGrid … Electricity CIGrid … Data Access/Storage SecurityWorkflowNotificationMessaging Portals Visualization GridCollaboration Grid Sensor GridCompute GridGIS Grid Core Grid Services

16 QuakeSim Portal Shots

Years of Simulated Earthquakes Simulations show clustering of earthquakes in space and time similar to what is observed.

18 SERVOGrid Apps and Their Data GeoFEST: Three-dimensional viscoelastic finite element model for calculating nodal displacements and tractions. Allows for realistic fault geometry and characteristics, material properties, and body forces. GeoFEST: Three-dimensional viscoelastic finite element model for calculating nodal displacements and tractions. Allows for realistic fault geometry and characteristics, material properties, and body forces. Relies upon fault models with geometric and material properties.Relies upon fault models with geometric and material properties. Virtual California: Program to simulate interactions between vertical strike-slip faults using an elastic layer over a viscoelastic half-space. Virtual California: Program to simulate interactions between vertical strike-slip faults using an elastic layer over a viscoelastic half-space. Relies upon fault and fault friction models.Relies upon fault and fault friction models. Pattern Informatics: Calculates regions of enhanced probability for future seismic activity based on the seismic record of the region Pattern Informatics: Calculates regions of enhanced probability for future seismic activity based on the seismic record of the region Uses seismic data archivesUses seismic data archives RDAHMM: Time series analysis program based on Hidden Markov Modeling. Produces feature vectors and probabilities for transitioning from one class to another. RDAHMM: Time series analysis program based on Hidden Markov Modeling. Produces feature vectors and probabilities for transitioning from one class to another. Used to analyze GPS and seismic catalog archives.Used to analyze GPS and seismic catalog archives. Can be adapted to detect state change events in real time.Can be adapted to detect state change events in real time.

19 Pattern Informatics (PI) PI is a technique developed by john rundle at University of California, Davis for analyzing earthquake seismic records to forecast regions with high future seismic activity. PI is a technique developed by john rundle at University of California, Davis for analyzing earthquake seismic records to forecast regions with high future seismic activity. They have correctly forecasted the locations of 15 of last 16 earthquakes with magnitude > 5.0 in California.They have correctly forecasted the locations of 15 of last 16 earthquakes with magnitude > 5.0 in California. See Tiampo, K. F., Rundle, J. B., McGinnis, S. A., & Klein, W. Pattern dynamics and forecast methods in seismically active regions. Pure Ap. Geophys. 159, (2002). See Tiampo, K. F., Rundle, J. B., McGinnis, S. A., & Klein, W. Pattern dynamics and forecast methods in seismically active regions. Pure Ap. Geophys. 159, (2002). bin/fulltext?format=application/pdf&identifier=oai%3Aar Xiv.org%3Acond-mat%2F http://citebase.eprints.org/cgi- bin/fulltext?format=application/pdf&identifier=oai%3Aar Xiv.org%3Acond-mat%2F http://citebase.eprints.org/cgi- bin/fulltext?format=application/pdf&identifier=oai%3Aar Xiv.org%3Acond-mat%2F http://citebase.eprints.org/cgi- bin/fulltext?format=application/pdf&identifier=oai%3Aar Xiv.org%3Acond-mat%2F PI is being applied other regions of the world, and has gotten a lot of press. PI is being applied other regions of the world, and has gotten a lot of press. Google “John Rundle UC Davis Pattern Informatics”Google “John Rundle UC Davis Pattern Informatics”

20 Real-time Earthquake Forecast JB Rundle, KF Tiampo, W. Klein, JSS Martins, PNAS, v99, Supl 1, , Feb 19, 2002; KF Tiampo, KF Tiampo, JB Rundle, S. McGinnis, S. Gross and W. Klein, Europhys. Lett., 60, , 2002 Plot of Log 10  P(x) Potential for large earthquakes, M  5, ~ 2000 to 2010 Seven large events with M  5 have occurred on anomalies, or within the margin of error: 1.Big Bear I, M = 5.1, Feb 10, Coso, M = 5.1, July 17, Anza, M = 5.1, Oct 31, Baja, M = 5.7, Feb 22, Gilroy, M= , May 13, Big Bear II, M=5.4, Feb 22, San Simeon, M = 6.5, Dec 22, 2003

21 World-Wide Earthquakes, M > 5, World-Wide Forecast Hotspot Map for Likely Locations of Great Earthquakes M  7.0 For the Decade Green Circles = Large Earthquakes M  7 from Jan 1, 2000 – Dec 1, 2004 World-Wide Forecast Hotspot Map Green Circles = Large Earthquakes M  7 from Jan 1, 2000 – Dec 1, 2004 Blue Circles: Large Earthquakes from December 1, Present Dec. 23 M ~ 8.1 Macquarie Island Dec. 26 M ~ 9.0 Northern Sumatra

22 Pattern Informatics in a Grid Environment PI in a Grid environment: PI in a Grid environment: Hotspot forecasts are made using publicly available seismic records.Hotspot forecasts are made using publicly available seismic records. Southern California Earthquake Data Center Southern California Earthquake Data Center Advanced National Seismic System (ANSS) catalogs Advanced National Seismic System (ANSS) catalogs Code location is unimportant, can be a service through remote executionCode location is unimportant, can be a service through remote execution Results need to be stored, shared, modifiedResults need to be stored, shared, modified Grid/Web Services can provide these capabilitiesGrid/Web Services can provide these capabilities Problems: Problems: How do we provide programming interfaces (not just user interfaces) to the above catalogs?How do we provide programming interfaces (not just user interfaces) to the above catalogs? How do we connect remote data sources directly to the PI code.How do we connect remote data sources directly to the PI code. How do we automate this for the entire planet?How do we automate this for the entire planet? Solutions: Solutions: Use GIS services to provide the input data, plot the output dataUse GIS services to provide the input data, plot the output data Web Feature Service for data archives Web Feature Service for data archives Web Map Service for generating maps Web Map Service for generating maps Use HPSearch tool to tie together and manage the distributed data sources and code.Use HPSearch tool to tie together and manage the distributed data sources and code.

23 Japan

24 GIS and Sensor Grids OGC has defined a suite of data structures and services to support Geographical Information Systems and Sensors GML Geography Markup language defines specification of geo-referenced data SensorML and O&M (Observation and Measurements) define meta-data and data structure for sensors Services like Web Map Service, Web Feature Service, Sensor Collection Service define services interfaces to access GIS and sensor information Grid workflow links services that are designed to support streaming input and output messages We are building Grid (Web) service implementations of these specifications for NASA’s SERVOGrid

25 A Screen Shot From the WMS Client

26 WMS uses WFS that uses data sources Northridge2 Wald D. J , , Can add Google or Yahoo Map WMS Web Services

27 SOPAC GPS Sensor Services The Scripps Orbit and Permanent Array Center (SOPAC) GPS station network data published in RYO format is converted to ASCII and GML

28 Position Messages SOPAC provides 1-2Hz real-time position messages from various GPS networks in a binary format called RYO. SOPAC provides 1-2Hz real-time position messages from various GPS networks in a binary format called RYO. Position messages are broadcasted through RTD server ports. Position messages are broadcasted through RTD server ports. We have implemented tools to convert RYO messages into ASCII text and another that converts ASCII messages into GML. We have implemented tools to convert RYO messages into ASCII text and another that converts ASCII messages into GML.

29 SOPAC GPS Services We implemented services to provide real- time access to GPS position messages collected from several SOPAC networks. We implemented services to provide real- time access to GPS position messages collected from several SOPAC networks. Data Philosophy: post all data before any transformations; post transformed data Data Philosophy: post all data before any transformations; post transformed data Data are streams and not files; they can be archived to files Data are streams and not files; they can be archived to files Then we couple data assimilation tools (such as RDAHMM) to real-time streaming GPS data. Then we couple data assimilation tools (such as RDAHMM) to real-time streaming GPS data. Next steps include a Sensor Collection Service to provide metadata about GPS sensors in SensorML. Next steps include a Sensor Collection Service to provide metadata about GPS sensors in SensorML. WS XXXX Stream WS Data-mining, Archiving Web Services X Pub-Sub Queued Stream Control

30 Real-Time Access to Position Messages We have a Forwarder tool that connects to RTD server port to forward RYO messages to a NB topic. We have a Forwarder tool that connects to RTD server port to forward RYO messages to a NB topic. RYO to ASCII converter service subscribes this topic to collect binary messages and converts them to ASCII. Then it publishes ASCII messages to another NB topic. RYO to ASCII converter service subscribes this topic to collect binary messages and converts them to ASCII. Then it publishes ASCII messages to another NB topic. ASCII to GML converter service subscribes this topic and publishes GML messages to another topic. ASCII to GML converter service subscribes this topic and publishes GML messages to another topic.

31 RDAHMM GPS Signal Analysis Courtesy of Robert Granat, JPL EarthquakeDrain Reservoir

32 Handling Streams in Web Services Do not open a socket – hand message to messaging system Use Publish-Subscribe as overhead negligible Model is totally asynchronous and event based Messaging system is a distributed set of “SOAP Intermediaries” (message brokers) which manage distributed queues and subscriptions Streams are ordered sets of messages whose common processing is both necessary and an opportunity for efficiency Manage messages and streams to ensure reliable delivery, fast replay, transmission through firewalls, multicast, custom transformations

33 Different ways of Thinking Services and Messages – NOT Jobs and Files Service Internet: Packets replaced by Messages The BitTorrent view of Files Files are chunked into messages which are scattered around the Grid Chunks are re-assembled into contiguous files Streams replace files by message queues Queues are labeled by topics System MIGHT chose to backup queues to disk but you just think of messages on distributed queuestimes Note typical time to worry about is a Millisecond Schedule stream-based services NOT jobs

34 DoD Data Strategy Only Handle Information Once (OHIO) – Data is posted in a manner that facilitates re-use without the need for replicating source data. Focus on re-use of existing data repositories. Smart Pull (vice Smart Push) – Applications encourage discovery; users can pull data directly from the net or use value added discovery services (search agents and other “smart pull techniques). Focus on data sharing, with data stored in accessible shared space and advertised (tagged) for discovery. Post at once in Parallel – Process owners make their data available on the net as soon as it is created. Focus on data being tagged and posted before processing (and after processing).

35 NaradaBrokering Stream NB supports messages and streams NB role for Grid is Similar to MPI role for MPP Queues

36 Multiple protocol transport support In publish-subscribe Paradigm with different Protocols on each link Transport protocols supported include TCP, Parallel TCP streams, UDP, Multicast, SSL, HTTP and HTTPS. Communications through authenticating proxies/firewalls & NATs. Network QoS based Routing Allows Highest performance transport Subscription FormatsSubscription can be Strings, Integers, XPath queries, Regular Expressions, SQL and tag=value pairs. Reliable delivery Robust and exactly-once delivery in presence of failures Ordered delivery Producer Order and Total Order over a message type. Time Ordered delivery using Grid-wide NTP based absolute time Recovery and Replay Recovery from failures and disconnects. Replay of events/messages at any time. Buffering services. Security Message-level WS-Security compatible security Message Payload options Compression and Decompression of payloads Fragmentation and Coalescing of payloads Messaging Related Compliance Java Message Service ( JMS ) 1.0.2b compliant Support for routing P2P JXTA interactions. Grid Feature SupportNaradaBrokering enhanced Grid-FTP. Bridge to Globus GT3. Web Services supportedImplementations of WS-ReliableMessaging, WS-Reliability and WS-Eventing. Traditional NaradaBrokering Features

37 Features for July Releases Production implementations of WS-Eventing, WS-RM and WS-Reliability. WS-Notification when specification agreed SOAP message support and NaradaBrokers viewed as SOAP Intermediaries Active replay support: Pause and Replay live streams. Stream Linkage: can link permanently multiple streams – using in annotating real-time video streams Replicated storage support for fault tolerance and resiliency to storage failures. Management: HPSearch Scripting Interface to streams and services Broker Discovery: Locate appropriate brokers

38 hop Transit Delay (Milliseconds) Message Payload Size (Bytes) Mean transit delay for message samples in NaradaBrokering: Different communication hops hop-2 hop-5 hop-7 Pentium-3, 1GHz, 256 MB RAM 100 Mbps LAN JRE 1.3 Linux

39

40 Consequences of Rule of the Millisecond Useful to remember critical time scales 1) ms – CPU does a calculation 2a) to 0.01 ms – Parallel Computing MPI latency 2b) to 0.01 ms – Overhead of a Method Call 3) 1 ms – wake-up a thread or process (do simple things on a PC) 4) 10 to 1000 ms – Internet delay 2a), 4) implies geographically distributed metacomputing can’t in general compete with parallel systems 3) << 4) implies a software overlay network is possible without significant overhead We need to explain why it adds value of course! 2b) versus 3) and 4) describes regions where method and message based programming paradigms important

41 Possible NaradaBrokering Futures Support for replicated storages within the system. In a system with N replicas the scheme can sustain the loss of N-1 replicas. Clarification and expansion of NB Broker to act as a WS container and SOAP Intermediary Integration with Axis 2.0 as Message Oriented Middleware infrastructure Support for High Performance transport and representation for Web Services Needs Context catalog under development Performance based routing The broker network will dynamically respond to changes in the network based on metrics gathered at individual broker nodes. Replicated publishers for fault tolerance Pure client P2P implementation (originally we linked to JXTA) Security Enhancements for fine-grain topic authorization, multi- cast keys, Broker attacks

42 Controlling Streaming Data NaradaBrokering capabilities can be accessed by messages (as in WS-*) and by a scripting interface that allows topics to be created and linked to external services Firewall traversal algorithms and network link performance data can be accessed HPSearch offers this via JavaScript This scripting engine provides a simple workflow environment that is useful for setting up Sensor Grids Should be made compatible with Web Service workflow (BPEL) and streaming workflow models Triana and Kepler Also link to WS-Management

43 NaradaBrokering topics

44 Role of WS-Context There are many WS-* specifications addressing meta- data and both many approaches and many trade-offs There are Distributed Hash Tables (Chord) to achieve scalability in large scale networks Managed dynamic workflows as in sensor integration and collaboration require Fault-tolerance Ability to support dynamic changes with few millisecond delay But only a modest number of involved services (up to 1000’s) We are building a WS-Context compliant metadata catalog supporting distributed or central paradigms Use for OGC Web catalog service with UDDI for slowly varying meta-data

45 Publish-Subscribe Streaming Workflow: HPSearch HPSearch is an engine for orchestrating distributed Web Service interactions HPSearch is an engine for orchestrating distributed Web Service interactions It uses an event system and supports both file transfers and data streams.It uses an event system and supports both file transfers and data streams. Legacy nameLegacy name HPSearch flows can be scripted with JavaScript HPSearch flows can be scripted with JavaScript HPSearch engine binds the flow to a particular set of remote services and executes the script.HPSearch engine binds the flow to a particular set of remote services and executes the script. HPSearch engines are Web Services, can be distributed interoperate for load balancing. HPSearch engines are Web Services, can be distributed interoperate for load balancing. Boss/Worker modelBoss/Worker model ProxyWebService: a wrapper class that adds notification and streaming support to a Web Service. ProxyWebService: a wrapper class that adds notification and streaming support to a Web Service.

Data Filter (Danube) PI Code Runner (Danube)  Accumulate Data  Run PI Code  Create Graph  Convert RAW -> GML WFS (Gridfarm001) WMS HPSearch (TRex) HPSearch (Danube) HPSearch hosts an AXIS service for remote deployment of scripts GML (Danube) WS Context (Tambora) NaradaBroker network: Used by HPSearch engines as well as for data transfer Actual Data flow HPSearch controls the Web services Final Output pulled by the WMS HPSearch Engines communicate using NB Messaging infrastructure Virtual Data flow Data can be stored and retrieved from the 3 rd part repository (Context Service) WMS submits script execution request (URI of script, parameters)

47 SOAP Message Structure I SOAP Message consists of headers and a body Headers could be for Addressing, WSRM, Security, Eventing etc. Headers are processed by handlers or filters controlled by container as message enters or leaves a service Body processed by Service itself The header processing defines the “Web Service Distributed Operating System” Containers queue messages; control processing of headers and offer convenient (for particular languages) service interfaces Handlers are really the core Operating system services as they receive and give back messages like services; they just process and perhaps modify different elements of SOAP Message H1H4H3H2Body F1F2F3 F4 Service Container Handlers Container Workflow

48 Merging the OSI Levels All messages pass through multiple operating systems and each O/S thinks of message as a header and a body Important message processing is done at Network Client (UNIX, Windows, J2ME etc) Web Service Header Application EACH is < 1ms (except for small sensor clients and except for complex security) But network transmission time is often 100ms or worse Thus no performance reason not to mix up places processing done IP TCP SOAP App

49 Bit level Internet (OSI Stack) Layered Architecture for Web Services and Grids Base Hosting Environment Protocol HTTP FTP DNS … Presentation XDR … Session SSH … Transport TCP UDP … Network IP … Data Link / Physical Service Internet Application Specific Grids Generally Useful Services and Grids Workflow WSFL/BPEL Service Management (“Context etc.”) Service Discovery (UDDI) / Information Service Internet Transport  Protocol Service Interfaces WSDL Service Context Higher Level Services

WS-* implies the Service Internet We have the classic (CISCO, Juniper ….) Internet routing the flood of ordinary packets in OSI stack architecture Web Services build the “Service Internet” or IOI (Internet on Internet) with Routing via WS-Addressing not IP header Fault Tolerance (WS-RM not TCP) Security (WS-Security/SecureConversation not IPSec/SSL) Data Transmission by WS-Transfer not HTTP Information Services (UDDI/WS-Context not DNS/Configuration files) At message/web service level and not packet/IP address level Software-based Service Internet possible as computers “fast” Familiar from Peer-to-peer networks and built as a software overlay network defining Grid (analogy is VPN) SOAP Header contains all information needed for the “Service Internet” (Grid Operating System) with SOAP Body containing information for Grid application service

51 SOAP Message Structure II Content of individual headers and the body is defined by XML Schema associated with WS-* headers and the service WSDL SOAP Infoset captures header and body structure XML Infoset for individual headers and the body capture the details of each message part Web Service Architecture requires that we capture Infoset structure but does not require that we represent XML in angle bracket value notation H1H4H3H2 Body bp1bp2 bp3 hp1hp2hp3hp4hp5 Infoset represents semantic structure of message and its parts

52 High Performance XML I There are many approaches to efficient “binary” representations of XML Infosets MTOM, XOP, Attachments, Fast Web Services DFDL is one approach to specifying a binary format Assume URI-S labels Scheme and URI-R labels realization of Scheme for a particular message i.e. URI- R defines specific layout of information in each message Assume we are interested in conversations where a stream of messages is exchanged between two services or between a client and a service i.e. two end-points Assume that we need to communicate fast between end- points that understand scheme URI-S but must support conventional representation if one end-point does not understand URI-S

53 High Performance XML II First Handler Ft=F1 handles Transport protocol; it negotiates with other end-point to establish a transport conversation which uses either HTTP (default) or a different transport such as UDP with WSRM implementing reliability URI-T specifies transport choice Second Handler Fr=F2 handles representation and it negotiates a representation conversation with scheme URI-S and realization URI-R Negotiation identifies parts of SOAP header that are present in all messages in a stream and are ONLY transmitted ONCE Fr needs to negotiate with Service and other handlers illustrated by F3 and F4 below to decide what representation they will process F1F2F3 F4 Container Handlers

54 High Performance XML III Filters controlled by Conversation Context convert messages between representations using permanent context (metadata) catalog to hold conversation context Different message views for each end point or even for individual handlers and service within one end point Conversation Context is fast dynamic metadata service to enable conversions NaradaBrokering will implement Fr and Ft using its support of multiple transports, fast filters and message queuing; H1H4H3H2Body Service Conversation Context URI-S, URI-R, URI-T Replicated Message Header Transported Message Handler Message View Service Message View Container Handlers FtFrF3 F4

55 In Summary… Measurement of crustal deformation and new computational methods will refine hazard maps from 100 km and 50 years to 10 km and 5 years.

56 GlobalMMCS Web Service Architecture SIPH323 Access GridNative XGSP Admire Gateways convert to uniform XGSP Messaging High Performance (RTP) and XML/SOAP and.. Media Servers Filters Session Server XGSP-based Control NaradaBrokering All Messaging Use Multiple Media servers to scale to many codecs and many versions of audio/video mixing NB Scales as distributed Web Services NaradaBrokering

57 GlobalMMCS Architecture Event Messaging Service (NaradaBrokering) XGSP Conference Control Service Audio Video Web Service Instant Messaging Web Service Shared Display Web Service Shared …. Web Service Non-WS collaboration control protocols are “gatewayed” to XGSP NaradaBrokering supports TCP (chat, control, shared display, PowerPoint etc.) and UDP (Audio-Video conferencing)

58 XGSP Example: New Session GameRoom chess chess-0 John false chess-0 Bob black chess-0 Jack white

59 Average Video Delays for one broker – Performance scales proportional to number of brokers Latency ms # Receivers One session Multiple sessions 30 frames/sec

60 Multiple Brokers – Multiple Meetings 4 brokers can support 48 meetings with 1920 users in total with excellent quality. This number is higher than the single video meeting tests in which four brokers supported up to 1600 users. When we repeated the same test with meeting size 20, 1400 participants can be supported. Number of Meetings Total users Broker1 (ms) Broker2 (ms) Broker3 (ms) Broker4 (ms) Number of Meetings Total users Broker1 (%) Broker2 (%) Broker3 (%) Broker4 (%) Latency for meeting size 40 loss rates

61 PDA Download video (using 4- way video mixer service) PDA Desktop

62 Linked Stream applications Note NaradaBrokering supports composite streams linking “atomic streams” Support hybrid codecs that mix TCP (lossless) and RTP (lossy) algorithms Supports time-stamped annotations of sensor streams Atomic and composite streams can be archived and replayed Video Annotated Video Frame e-Sports Project