11

SERVOGrid and Grids for Real-time and Streaming Applications

Grid School Vico Equense

July 21 2005

Geoffrey Fox

Computer Science, Informatics, Physics

Pervasive Technology Laboratories

Indiana University Bloomington IN 47401

http://grids.ucs.indiana.edu/ptliupages/presentations/GridSchool2005/gcf@indiana.edu

http://www.infomall.org

22

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!

31: Container and Run Time (Hosting) Environment

2: System Services and FeaturesHandlers like WS-RM, Security, Programming Models like BPEL

or Registries like UDDI

3: Generally Useful Services and FeaturesSuch as “Access a Database” or “Submit a Job” or “ManageCluster” or “Support a Portal” or “Collaborative Visualization”

4: Application or Community of InterestSpecific Services

such as “Run BLAST” or “Look at Houses for sale”

OGSAand otherGGF/W3C/ ………

WS-* fromOASIS/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 MissionsSERVOGrid 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)

t1

t2

change(t1)

(t2)

t1

t2

B

A1

A2

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

ComputationalScience

Informatics

Data Deluged Science Computing Paradigm

1212

Database Database

Analysis and VisualizationPortal

RepositoriesFederated Databases

Data Filter

Services

Field Trip DataStreaming Data

Sensors

?DiscoveryServices

SERVOGrid

ResearchSimulations

Research Education

CustomizationServices

From Research

to Education

EducationGrid ComputerFarmGrid of Grids: Research Grid and Education Grid

GISGrid

Sensor GridDatabase 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

1414

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

1515

Critical Infrastructure (CI) Grids built as Grids of Grids

Gas Servicesand Filters

Physical Network

Registry Metadata

Flood Servicesand Filters

Flood CIGrid Gas CIGrid… Electricity CIGrid …

Data Access/Storage

Security WorkflowNotification Messaging

Portals Visualization GridCollaboration Grid

Sensor Grid Compute GridGIS Grid

Core Grid Services

16

QuakeSim Portal Shots

17

1000 Years of Simulated Earthquakes

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

1818

SERVOGrid Apps and Their DataSERVOGrid Apps and Their Data GeoFEST:GeoFEST: Three-dimensional viscoelastic finite element model for Three-dimensional viscoelastic finite element model for

calculating nodal displacements and tractions. Allows for realistic calculating nodal displacements and tractions. Allows for realistic fault geometry and characteristics, material properties, and body fault geometry and characteristics, material properties, and body forces. forces. • Relies upon fault models with geometric and material Relies upon fault models with geometric and material

properties.properties. Virtual California:Virtual California: Program to simulate interactions between Program to simulate interactions between

vertical strike-slip faults using an elastic layer over a viscoelastic vertical strike-slip faults using an elastic layer over a viscoelastic half-space.half-space.• Relies upon fault and fault friction models.Relies upon fault and fault friction models.

Pattern Informatics:Pattern Informatics: Calculates regions of enhanced probability for Calculates regions of enhanced probability for future seismic activity based on the seismic record of the regionfuture seismic activity based on the seismic record of the region• Uses seismic data archivesUses seismic data archives

RDAHMM:RDAHMM: Time series analysis program based on Hidden Markov Time series analysis program based on Hidden Markov Modeling. Produces feature vectors and probabilities for Modeling. Produces feature vectors and probabilities for transitioning from one class to another. 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.

1919

Pattern Informatics (PI)Pattern Informatics (PI) PI is a technique developed by john rundle at PI is a technique developed by john rundle at

University of California, Davis for analyzing University of California, Davis for analyzing earthquake seismic records to forecast regions earthquake seismic records to forecast regions with high future seismic activity.with high future seismic activity.• They have correctly forecasted the locations of 15 of last They have correctly forecasted the locations of 15 of last

16 earthquakes with magnitude > 5.0 in California.16 earthquakes with magnitude > 5.0 in California. See Tiampo, K. F., Rundle, J. B., McGinnis, S. A., & See Tiampo, K. F., Rundle, J. B., McGinnis, S. A., &

Klein, W. Pattern dynamics and forecast methods Klein, W. Pattern dynamics and forecast methods in seismically active regions. in seismically active regions. Pure Ap. Geophys. Pure Ap. Geophys. 159, 2429-2467 (2002). 159, 2429-2467 (2002). • http://citebase.eprints.org/cgi-bin/fulltext?format=applichttp://citebase.eprints.org/cgi-bin/fulltext?format=applic

ation/pdf&identifier=oai%3AarXiv.org%3Acond-mat%2F0ation/pdf&identifier=oai%3AarXiv.org%3Acond-mat%2F0102032102032

PI is being applied other regions of the world, and PI is being applied other regions of the world, and has gotten a lot of press.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, 2514-2521, Feb 19, 2002; KF Tiampo, KF Tiampo, JB Rundle, S. McGinnis, S. Gross and W. Klein, Europhys. Lett., 60, 481-487, 2002

Plot of Log10 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, 2001

2. Coso, M = 5.1, July 17, 2001

3. Anza, M = 5.1, Oct 31, 2001

4. Baja, M = 5.7, Feb 22, 2002

5. Gilroy, M=4.9 - 5.1, May 13, 2002

6. Big Bear II, M=5.4, Feb 22, 2003

7. San Simeon, M = 6.5, Dec 22, 2003

21

World-Wide Earthquakes, M > 5, 1965-2000World-Wide Forecast Hotspot Map for Likely Locations ofGreat Earthquakes M 7.0 For the Decade 2000-2010

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, 2004 - Present

Dec. 23 M ~ 8.1Macquarie Island

Dec. 26 M ~ 9.0Northern Sumatra

2222

Pattern Informatics in a Grid Pattern Informatics in a Grid EnvironmentEnvironment

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 CenterSouthern California Earthquake Data Center Advanced National Seismic System (ANSS) catalogsAdvanced National Seismic System (ANSS) catalogs

• Code location is unimportant, can be a service through remote Code location is unimportant, can be a service through remote executionexecution

• 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) How do we provide programming interfaces (not just user interfaces)

to the above catalogs?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 archivesWeb Feature Service for data archives Web Map Service for generating mapsWeb Map Service for generating maps

• Use HPSearch tool to tie together and manage the distributed data Use HPSearch tool to tie together and manage the distributed data sources and code.sources and code.

2323

Japan

2424

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

2525

A Screen Shot From the WMS Client

2626

WMS uses WFS that uses data sources

Railroads

RiversBridges

Interstate Highways

90

WFS Server

SQL Query

Railroads

[a-b]

SQ

L Q

uery

Riv

er [a

-d]

Bri

dge

[1-5

]

SQL QueryHigway [12-18]

`

ClientWMS

GetFeature

FeatureCollection

Get

Feat

ure

Feat

ureC

olle

ctio

n

<gml:featureMember> <fault> <name> Northridge2 </name> <segment> Northridge2

</segment> <author> Wald D. J.</author> <gml:lineStringProperty> <gml:LineString

srsName="null"> <gml:coordinates>

-118.72,34.243 -118.591,34.176 </gml:coordinates>

</gml:LineString> </gml:lineStringProperty> </fault> </gml:featureMember> Can add Google or Yahoo Map WMS Web Services

2727

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

2828

Position MessagesPosition Messages

SOPAC provides 1-2Hz real-time SOPAC provides 1-2Hz real-time position messages from various GPS position messages from various GPS networks in a binary format called networks in a binary format called RYO.RYO.

Position messages are broadcasted Position messages are broadcasted through RTD server ports.through RTD server ports.

We have implemented tools to We have implemented tools to convert RYO messages into ASCII text convert RYO messages into ASCII text and another that converts ASCII and another that converts ASCII messages into GML.messages into GML.

2929

SOPAC GPS Services SOPAC GPS Services We implemented services to provide real-time We implemented services to provide real-time

access to GPS position messages collected access to GPS position messages collected from several SOPAC networks.from several SOPAC networks.

Data Philosophy: post all data before any Data Philosophy: post all data before any transformations; post transformed datatransformations; post transformed data

Data are streams and not files; they can be Data are streams and not files; they can be archived to filesarchived to files

Then we couple data assimilation tools (such Then we couple data assimilation tools (such as RDAHMM) to real-time streaming GPS data.as RDAHMM) to real-time streaming GPS data.

Next steps include a Sensor Collection Service Next steps include a Sensor Collection Service to provide metadata about GPS sensors in to provide metadata about GPS sensors in SensorML.SensorML.

WS WS WSXXX X Stream

WS Data-mining, Archiving Web Services X Pub-Sub Queued Stream Control

3030

Real-Time Access to Position Real-Time Access to Position MessagesMessages

We have a Forwarder tool that connects to We have a Forwarder tool that connects to RTD server port to forward RYO messages to a RTD server port to forward RYO messages to a NB topic.NB topic.

RYO to ASCII converter service subscribes this RYO to ASCII converter service subscribes this topic to collect binary messages and converts topic to collect binary messages and converts them to ASCII. Then it publishes ASCII them to ASCII. Then it publishes ASCII messages to another NB topic.messages to another NB topic.

ASCII to GML converter service subscribes this ASCII to GML converter service subscribes this topic and publishes GML messages to another topic and publishes GML messages to another topic.topic.

3131

RDAHMM GPS Signal AnalysisCourtesy of Robert Granat, JPL

EarthquakeDrainReservoir

3232

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

3333

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

3434

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 messagesand streams

NB role for Grid isSimilar toMPI role for MPP

Queues

36

Multiple protocol transport supportIn publish-subscribeParadigm with differentProtocols 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 RoutingAllows Highest performance transport

Subscription Formats Subscription 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 payloadsFragmentation and Coalescing of payloads

Messaging Related Compliance

Java Message Service (JMS) 1.0.2b compliant Support for routing P2P JXTA interactions.

Grid Feature Support NaradaBrokering enhanced Grid-FTP. Bridge to Globus GT3.

Web Services supported

Implementations of WS-ReliableMessaging, WS-Reliability and WS-Eventing.

Traditional NaradaBrokering Features

3737

Features for July 12 2005 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

3838

hop-3

0

1

2

3

4

5

6

7

8

9

100 1000

Tra

nsit

Del

ay

(Mill

isec

onds

)

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 RAM100 Mbps LAN

JRE 1.3 Linux

3939

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1000 1500 2000 2500 3000 3500 4000 4500 5000

Sta

nd

ard

De

via

tion

(M

illis

eco

nd

s)

Message Payload Size (Bytes)

Standard Deviation for message samples in NaradaBrokering Different communication hops - Internal Machines

hop-2hop-3hop-5hop-7

4040

Consequences of Rule of the Millisecond Useful to remember critical time scales

• 1) 0.000001 ms – CPU does a calculation• 2a) 0.001 to 0.01 ms – Parallel Computing MPI latency• 2b) 0.001 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

4141

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

4242

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

4343

NaradaBrokering topicsNaradaBrokering topics

4444

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

4545

Publish-Subscribe Streaming Publish-Subscribe Streaming Workflow: HPSearchWorkflow: HPSearch

HPSearchHPSearch is an engine for orchestrating is an engine for orchestrating distributed Web Service interactionsdistributed Web Service interactions• It uses an event system and supports both file It uses an event system and supports both file

transfers and transfers and data streams.data streams.• Legacy nameLegacy name

HPSearch flows can be scripted with JavaScriptHPSearch flows can be scripted with JavaScript• HPSearch engine binds the flow to a particular set of HPSearch engine binds the flow to a particular set of

remote services and executes the script.remote services and executes the script. HPSearch engines are Web Services, can be HPSearch engines are Web Services, can be

distributed interoperate for load balancing.distributed interoperate for load balancing.• Boss/Worker modelBoss/Worker model

ProxyWebService:ProxyWebService: a wrapper class that adds a wrapper class that adds notification and streaming support to a Web notification and streaming support to a Web Service.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 3rd part repository (Context Service)

WMS submits script execution request (URI of script, parameters)

4747

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

H1 H4H3H2 Body F1 F2 F3 F4 Service

Container Handlers

Container Workflow

4848

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 forsmall sensor clients andexcept for complex security)

But network transmissiontime is often 100ms or worse

Thus no performance reasonnot to mix up places processingdone

IP

TCP

SOAP

App

49

Bit levelInternet

(OSI Stack)

Layered Architecture for Web Services and Grids

Base Hosting EnvironmentProtocol HTTP FTP DNS …

Presentation XDR …Session SSH …

Transport TCP UDP …Network IP …

Data Link / Physical

ServiceInternet

Application Specific GridsGenerally Useful Services and Grids

Workflow WSFL/BPELService Management (“Context etc.”)

Service Discovery (UDDI) / InformationService Internet Transport Protocol

Service Interfaces WSDL

ServiceContext

HigherLevelServices

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

5151

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 <content>value</content> notation

H1 H4H3H2 Body

bp1 bp2 bp3hp1 hp2 hp3 hp4 hp5

Infoset representssemantic structureof message and itsparts

5252

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

5353

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

F1 F2 F3 F4

Container Handlers

5454

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;

H1 H4H3H2 Body

Service

Conversation ContextURI-S, URI-R, URI-T

Replicated Message Header

Transported Message Handler Message View

ServiceMessage View

Container Handlers

Ft Fr F3 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.

http://quakesim.jpl.nasa.govhttp://quakesim.jpl.nasa.govhttp://servogrid.orghttp://servogrid.org

5656

GlobalMMCS Web Service Architecture

SIP H323 Access Grid Native XGSPAdmire

Gateways convert to uniform XGSP Messaging

High Performance (RTP)and XML/SOAP and ..

Media ServersFilters

Session ServerXGSP-based Control

NaradaBrokeringAll Messaging

Use Multiple Media servers to scale to many codecs and manyversions of audio/video mixing

NB Scales asdistributed

WebServices

NaradaBrokering

5757

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)

5858

XGSP Example: New Session <CreateAppSession>

<ConferenceID> GameRoom </ConferenceID><ApplicationID> chess </ApplicationID><AppSessionID> chess-0 </AppSessionID><AppSession-Creator> John </AppSession-Creator><Private> false </Private>

</CreateAppSession><SetAppRole>

<AppSessionID> chess-0 </AppSessionID><UserID> Bob </UserID><RoleDescription> black </RoleDescription>

</SetAppRole><SetAppRole>

<AppSessionID> chess-0 </AppSessionID><UserID> Jack </UserID><RoleDescription> white </RoleDescription>

</SetAppRole>

59

Average Video Delays for one broker – Performance scales proportional to number of brokers

Latency ms

# Receivers

One sessionMultiple 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

Totalusers

Broker1(ms)

Broker2(ms)

Broker3(ms)

Broker4(ms)

40 1600 3.34 6.93 8.43 8.37

48 1920 3.93 8.46 14.62 10.59

60 2400 9.04 170.04 89.97 25.83

Number of Meetings

Total users

Broker1(%)

Broker2(%)

Broker3(%)

Broker4(%)

40 1600 0.00 0.00 0.00 0.00

48 1920 0.12 0.29 0.50 0.50

60 2400 0.16 1.30 2.51 2.82

Latency for meeting size 40

loss rates

6161

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

PDA

Desktop

6262

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

VideoAnnotated Video Frame

e-Sports Project