Concurrent Web Map Cache Server A Vision for IndianaMap

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Concurrent Web Map Cache Server A
Vision for IndianaMap

• Zao Liu, Marlon Pierce, Geoffrey Fox
• Community Grids Laboratory
• Indiana University
• Neil Devadasan
• The Polis Center
• IUPUI
• October 27, 2006

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Where are we today?

• The current IndianaMap http//129.79.145.5/arcims/
  igic/viewer.htm uses data collected by the
  Indiana Geological Survey (IGS)
• IGS periodically collects the best available
  State and Federal data and authors the data on a
  central web server
• The web service includes the 2005 Statewide
  Orthophotography, INDOT and TIGER roads, USGS 10
  foot contours, and Census boundaries

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Where are we today?

• Most current detailed Geographic Information is
  located with local government systems.
• Key data includes parcels, addresses, roads, and
  infrastructure data
• This data is not readily available at a regional
  or statewide level for decision making because of
  technical limitations

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Comparison of state and county data

• 10 foot contours (1990) 1 foot contours (2006)
• Missing local roads Local roads (2006)
• No parcels Parcels (2006)
• No point addresses Point addresses (2006)
• Jurisdictional boundaries (2001) Jurisdictional
  boundaries (2006)

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The Polis Center Middleware Research

• Neil Devadasan
• The Polis Center
• IUPUI

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Many individual counties have web sites

• When connecting to the service you receive all
  data not the subset of data that you need
  (Indianapolis 100 layers)
• You have no control over the data that you
  retrieve and query

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Combining data from multiple web sites

• Depending on the characteristics of the web
  sites, combining data may cause problems.

• Leaking tanks (Indiana Geological Survey Atlas of
  Indiana) overlaid on Marion County Parcels
  (Indianapolis GIS Web site)

Public Land Survey Sections (Indiana Geological
Survey Atlas of Indiana) overlaid on Marion
County Parcels (Indianapolis GIS Web site) Note
Parcels are obliterated
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The Polis Centers Distributed Web GIS Middleware
Research Strategy

• To take advantage of this highly accurate local
  data for use statewide, a variety of technical
  issues must be overcome such as
• Projecting the information to a single coordinate
  system
• Standardizing symbology
• Retrieving individual Layers

.
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Upper White River Watershed Alliance
http//www.whiteriveralliance.org/
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IGIC IndianaMap Grant
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Performance is an issue and thus scalability may
be limited

• Performance is constrained by the performance of
  the Individual servers

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Federating, Tiling, and Caching Web Map Servers

• Zao Liu, Neil Devadasan, and Marlon Pierce

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Basic Problem Data Federation

• Integrated GIS systems have obvious benefits but
  inevitably systems are developed by various state
  and local government agencies.
• Bottom up rather than top down
• This tends to give excellent local information
  but it breaks down at the county boundary.

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Considerations

• We assume heterogeneity in GIS map and feature
  servers.
• Must find a way to federate existing services
• We must reconcile ESRI, OGC, Google Map, and
  other technical approaches.
• Make a clean distinction between clients and
  services
• Must try to take advantage of Google, ESRI, etc
  rather than compete.
• We must have good performance and interactivity.
• Servers must respond quickly--launching queries
  to 20 different map servers is very inefficient.
• Clients should have simplicity and interactivity
  of Google Maps and similar AJAX style
  applications.

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Two Phase Approach Caching and Tiling

• Federation through caching
• WMS and WFS resources are queried and results are
  stored on the cache servers.
• WMS images are stored as tiles.
• These can be assembled into new images on demand
  (c. f. Google Maps).
• Projections and styling can be reconciled.
• We can store multiple layers this way.
• We build adapters that can work with ESRI and OGC
  products tailor to specific counties.
• Tiling
• Client programs obtain images directly from our
  tile server.
• That is, dont go back to the original WMS for
  every request.
• Similar approaches can be used to mediate WFS
  requests.
• The tile server can re-cache and tile on demand
  if tile sections are missing.

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Some Technical Details
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Storage of caching entire state

• Takes about 2.5-3 TB to store the entire state to
  zoom level 13 this way.
• There are 48410476 tiles for zoom levels 0-13,
  162561384 tiles for 0-14 levels (nearly 12 TB).
• There are 10 layers for each scale
• Aerial photo layer tiles take 2530 KB
• Other layers (parcels, roads) are much smaller
  3036 KB for all remaining 9 layers per tile
• So we need almost 60KB 48410476 tiles to store
  all map data
• Layers from Google (Hybrids, Street, Google
  Satellite) dont need to
• be cached.
• This is large but possible.
• We can easily spread our caching server over
  multiple hosts to store even higher magnification
  scales.
• Efficient tiling storage can save disk space.

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Current Progress

• Supports ESRI and OGC servers
• Now 17 counties is being cached. (Marion, Monroe
  are fully cached for 13 zoom levels)
• 7 layers has been proved that they can be easily
  cached.
• Aerial photo layer, street , interstate layer,
  parcel, parcel ID, county boundary, school).
• 3 more layers can be easily shown in client
  without caching. (Google Map, Google Satellite,
  Hybrids).
• Querying parcel information across boundary. (
  MARION-HANCOCK boundary)
• Support Geocode querying.
• Higher resolution than Google Satellite.
• Google Map-like interaction.
• Performance and Reliability.
• Cache Server still work even the county server
  doesnt work.
• Much faster response to the client.

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Tradeoffs of Caching

• Cached images must be store somewhere.
• More zoom levels, much more disk space is needed.
• For 12 layers, 500-600 GB.
• For 13 layers, 2.5-3 TB.
• For 14 layers, about 12 TB. (It may be not
  necessary to cache this zoom level for all
  counties. We can cache this level for the
  requirement of some place.
• Difficulty of map re-projection.
• Latency of keeping update with county servers.
• Inconsistencies in available layers.

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Next Steps

• Caching more counties
• If county uses ESRI or OGC map server, current
  agent plugins can be used.
• We believe we can do the entire state
• We just dont have the data.
• Find a way to keep current with county servers,
  especially when the county server change layer
  id.
• Recent Monroe county example
• Establish a standard for layers. (Different
  county server use different name for the same
  layer)
• The tiling services should support multiple
  server styles
• URLs for REST/AJAX style clients
• WSDL and SOAP for formal Web Services
• Support OGC and ESRI clients.
• Collaborative clients, dynamic layers (i.e.
  weather is an obvious addition).

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Concurrent Web Map Cache Server

• Zao Liu, Marlon Pierce, Geoffrey Fox
• Community Grids Laboratory
• Indiana University

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Introduction

• Geographical Information Systems combine online
  dynamic maps and databases.
• Many GIS software packages exist
• GIS servers around state of Indiana
• ESRI ArcIMS and ArcMap Server (Marion,
  Vanderburgh, Hancock, Kosciusco, Huntington,
  Tippecanoe)
• Autodesk MapGuide (Hamilton, Hendricks, Monroe,
  Wayne)
• WTH Mapserver Web Mapping Application (Fulton,
  Cass, Daviess, City of Huntingburg) based on
  several Open Source projects .
• These are not compatible

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Map Server Federation

• Integrating GIS map servers is not trivial
• Our solution create a virtual map server to act
  as an agent server
• Translates map requests from generic format to
  the format expected by the specific map server.
• Provides a common language and programming
  interface for constructing clients
• The agent server by itself will work but
  performance is not good
• Must wait for slowest server to respond
• Failure prone a county server may not respond at
  all
• Adds additional overhead for combining images
• Combining the agent server with a caching server
  solves these problems.
• Caches images for greater performance

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Agent Server Architecture
County Server
Agent server
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Caching Server

• The agent server runs offline to harvest map
  images from county map servers.
• Images are stored as tiles.
• Tiles at county boundaries may be combined for
  greater storage and performance efficiency.
• Clients connect to the cache server instead of
  the agent server.
• The cache server constructs the requested image
  from pre-fetched tiles.
• Inspired by Google Maps approach
• Will enable more interactive clients (so-called
  AJAX programming)
• Image construction may be parallelized/multi-threa
  ded for greater performance.
• Potentially takes advantage of new multi-core
  server architectures from Sun, Intel, and AMD.

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Tiling Example
Agent server requests entire county maps for a
particular zoom level and then breaks up into
tiles.
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Tiling and Caching at County Boundaries
Bounding box requests across boundaries have many
empty tiles.
Hancock County
Marion County
Removing these empty tiles decreases storage
requirements and increases cache server
performance
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The combined map
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Caching and Tiling Layers

• Map servers typically contain base maps and
  optional layers
• Parcel boundaries, roads, and township boundaries
  are layers.
• We cache each layer separately.
• Layers and base maps are combined dynamically
  using Java Advanced Image libraries.
• Common techniques

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Tradeoffs of Caching

• Cached images must be stored somewhere.
• Currently, three counties (Hancock, Marion, and
  Cass) are cached at 11 different zoom levels.
• Photo images, layers
• Takes 100s-1000s GB of storage

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Caching the Entire State

• Takes about 500-600 GB to store the entire state
  to zoom level 10 this way.
• There are 6047804 tiles for zoom levels 0-10,
  15131920 tiles for 0-11 levels
• There are 10 layers for each scale
• Aerial photo layer tiles take 60 KB
• Other layers (parcels, roads) are much smaller
  36 KB for all remaining 9 layers per tile
• So we need 96KB 6047804 tiles to store all map
  data
• This is large but possible.
• Current commercial servers hosts like Sun T2000
  can have 1 TB external (RAID) storage.
• We can easily spread our caching server over
  multiple hosts to store even higher magnification
  scales.
• Efficient tiling storage can save disk space.

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Summary of Contributions

• Development of agent server to pre-fetch map
  images from county map servers.
• Stores images as tiles.
• Removes redundant/empty tiles.
• Supports ESRI and OGC servers
• Development of caching server
• Provides a uniform mechanism for clients to
  interact with different map servers.
• Increases performance and reliability
• Dont have to go to source map servers for every
  request.
• Will enable more interactive clients
• Google Map-like interaction

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Demonstration
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Next Steps

• University-private sector partnership
• MOUs with local government to implement system
  for emergency response
• University and private sector funding to
  implement ESRI or OGC map server functionality
• Develop Full Implementation System
• Finalize requirements
• Formalize programming interface using Web Service
  standards (WSDL and SOAP)
• Develop functionality
• Investigate scalability and performance issues