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Title: SERVOGrid and Grids for Real-time and Streaming Applications


1
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/presentati
    ons/GridSchool2005/
  • gcf_at_indiana.edu
  • http//www.infomall.org

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
Grid and Web Service Institutional Hierarchy
  • We will discuss some items at layer 4 and some at
    layer 1(and perhaps 2)

4
Motivating Challenges
From NASAs 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
t
t
2
Single Pass (Topography)
1
Repeat Pass (Topographic Change)
A
2
B
A
r

d
r
r
(
)
t
1
2
r
t
(
)
D
r
1
change
h
t
t
1
2
z
9
The Northridge Earthquake was Observed with InSAR
The Mountains grew 40 cm as a result of the
Northridge earthquake.
19931995 Interferogram
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 Deluged Science Computing Paradigm
Informatics
ComputationalScience
12
RepositoriesFederated Databases
Streaming Data
Sensors
Database
Sensor Grid
Database Grid
Research
Education
SERVOGrid
Compute Grid
Customization Services From Researchto Education
Data FilterServices
ResearchSimulations
Analysis and VisualizationPortal
EducationGrid Computer Farm
Grid of Grids Research Grid and Education 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
16
QuakeSim Portal Shots
17
1000 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.
  • 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.
  • 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
  • Uses 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.
  • Used to analyze GPS and seismic catalog archives.
  • 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.
  • They have correctly forecasted the locations of
    15 of last 16 earthquakes with magnitude gt 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, 2429-2467 (2002).
  • http//citebase.eprints.org/cgi-bin/fulltext?forma
    tapplication/pdfidentifieroai3AarXiv.org3Acon
    d-mat2F0102032
  • PI is being applied other regions of the world,
    and has gotten a lot of press.
  • Google John Rundle UC Davis Pattern Informatics

20
Real-time Earthquake Forecast
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, M4.9 - 5.1, May 13, 2002
  6. Big Bear II, M5.4, Feb 22, 2003
  7. San Simeon, M 6.5, Dec 22, 2003

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
21
(No Transcript)
22
Pattern Informatics in a Grid Environment
  • PI in a Grid environment
  • Hotspot forecasts are made using publicly
    available seismic records.
  • Southern California Earthquake Data Center
  • Advanced National Seismic System (ANSS) catalogs
  • Code location is unimportant, can be a service
    through remote execution
  • Results need to be stored, shared, modified
  • Grid/Web Services can provide these capabilities
  • Problems
  • 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 automate this for the entire planet?
  • Solutions
  • Use GIS services to provide the input data, plot
    the output data
  • Web Feature Service for data archives
  • Web Map Service for generating maps
  • 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 OM (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
    NASAs SERVOGrid

25
A Screen Shot From the WMS Client
26
WMS uses WFS that uses data sources
ltgmlfeatureMembergt ltfaultgt ltnamegt
Northridge2 lt/namegt ltsegmentgt Northridge2
lt/segmentgt ltauthorgt Wald D. J.lt/authorgt
ltgmllineStringPropertygt
ltgmlLineString srsName"null"gt
ltgmlcoordinatesgt -118.72,34.243
-118.591,34.176 lt/gmlcoordinatesgt
lt/gmlLineStringgt lt/gmllineStringPropertygt
lt/faultgt lt/gmlfeatureMembergt
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.
  • 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.

29
SOPAC GPS Services
  • 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 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.
  • Next steps include a Sensor Collection Service to
    provide metadata about GPS sensors in SensorML.

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.
  • 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.

31
RDAHMM GPS Signal AnalysisCourtesy of Robert
Granat, JPL
Earthquake
DrainReservoir
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
Queues
Stream
NB supports messages and streams
NB role for Grid is Similar to MPI role for MPP
36
Traditional NaradaBrokering Features
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 Formats Subscription can be Strings, Integers, XPath queries, Regular Expressions, SQL and tagvalue 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 Support NaradaBrokering enhanced Grid-FTP. Bridge to Globus GT3.
Web Services supported Implementations of WS-ReliableMessaging, WS-Reliability and WS-Eventing.
37
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

38
Pentium-3, 1GHz, 256 MB RAM 100 Mbps LAN JRE 1.3
Linux
39
(No Transcript)
40
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 cant in general compete with
    parallel systems
  • 3) ltlt 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 1000s)
  • 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
  • It uses an event system and supports both file
    transfers and data streams.
  • Legacy name
  • HPSearch flows can be scripted with JavaScript
  • 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.
  • Boss/Worker model
  • ProxyWebService a wrapper class that adds
    notification and streaming support to a Web
    Service.

46
WS Context (Tambora)
Data can be stored and retrieved from the 3rd
part repository (Context Service)
WFS (Gridfarm001)
NaradaBroker network Used by HPSearch engines
as well as for data transfer
WMS
Data Filter (Danube)
Virtual Data flow
WMS submits script execution request (URI of
script, parameters)
HPSearch hosts an AXIS service for remote
deployment of scripts
  • PI Code Runner
  • (Danube)
  • Accumulate Data
  • Run PI Code
  • Create Graph
  • Convert RAW -gt GML

GML (Danube)
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

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 lt 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
App
SOAP
49
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
Higher Level Services
ServiceContext
ServiceInternet
Base Hosting Environment
Protocol HTTP FTP DNS Presentation XDR
Session SSH Transport TCP UDP Network IP
Data Link / Physical
Bit level Internet (OSI Stack)
Layered Architecture for Web Services and Grids
50
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 ltcontentgtvaluelt/con
    tentgt notation

Infoset representssemantic structure of message
and itsparts
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 FtF1 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 FrF2 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

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

Conversation ContextURI-S, URI-R,
URI-T Replicated Message Header
Transported Message
Handler Message View
ServiceMessage View
Service
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.gov http//servogrid.org
56
GlobalMMCS Web Service Architecture
Use Multiple Media servers to scale to many
codecs and many versions of audio/video mixing
WebServices
High Performance (RTP)and XML/SOAP and ..
NB Scales asdistributed
Gateways convert to uniform XGSP Messaging
NaradaBrokering
57
GlobalMMCS Architecture
  • 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
  • ltCreateAppSessiongt
  • ltConferenceIDgt GameRoom lt/ConferenceIDgt
  • ltApplicationIDgt chess lt/ApplicationIDgt
  • ltAppSessionIDgt chess-0 lt/AppSessionIDgt
  • ltAppSession-Creatorgt John lt/AppSession-Creatorgt
  • ltPrivategt false lt/Privategt
  • lt/CreateAppSessiongt
  • ltSetAppRolegt
  • ltAppSessionIDgt chess-0 lt/AppSessionIDgt
  • ltUserIDgt Bob lt/UserIDgt
  • ltRoleDescriptiongt black lt/RoleDescriptiongt
  • lt/SetAppRolegt
  • ltSetAppRolegt
  • ltAppSessionIDgt chess-0 lt/AppSessionIDgt
  • ltUserIDgt Jack lt/UserIDgt
  • ltRoleDescriptiongt white lt/RoleDescriptiongt
  • lt/SetAppRolegt

59
Average Video Delays for one broker Performance
scales proportional to number of brokers
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)
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
Latency for meeting size 40
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
loss rates
61
PDA Download video (using 4-way video mixer
service)
Desktop
PDA
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
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