[Foss4 g2013]the architecture of mobile traffic map service final
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    [Foss4 g2013]the architecture of mobile traffic map service final [Foss4 g2013]the architecture of mobile traffic map service final Presentation Transcript

    • The Architecture Of MobileTraffic Map Service BJ JANG, Hayan Shin 1
    • TotalTraffic Information Service 2 Sponsored by NTIC(National Transport Information Center)Mobile Traffic Map Service
    • Background  About NTIC (our customer)  National Transport Information Center is a national organization belonging to the Ministry of Land, Infrastructure, and Transport  Role: Traffic Information Collection, Processing, Providing  http://www.its.go.kr/Eng/  Collected content  Wide-area(whole Korea) traffic information  Traffic cast CCTV  Vehicle Message Content Service (VMS)  Provided Information  Real-time road flow information  Standardized Node/Link data of roads (for ITS)  Short/long distance travel route information 3
    • Background  NTIC’s Requirements  Deliver Real-Time Traffic Information to Users  To Disperse Traffic on major national holidays - Lunatic New year’s first day, Chuseok  Environment at System Peak Times  About 30 million people move to visit hometowns and families  Most of them have Smartphone 4
    • Overview of Main Features Traffic status on roads and highways up on geographical map  Support interactive zoom in/out  3 Steps colorized traffic data  Updates every 5 minutes >40km/h 20~40 km/h <20km/h >40km/h 20~40 km/h <20km/h >80km/h 40~80 km/h <40km/h Road City highway highway 5
    • Overview of Main Features Traffic status on roads and highways up on geographical map(continue) traffic accidents information CCTV on roads (over 1000 points) KMA Weather Forecast/Warning 6
    • Architecture(2011) Mobile Data Provider MobileMananger WAS(Tomcat7) Windows Server Info Server GeoServer2.0.3 WAS(Tomcat7) Windows Server Map Server PostGIS 1.5.3 PostgreSQL 8.4 Windows Server Geo DB Server PostGIS Info Server Geo DB Server Map Server • 30,000 users • adopt Open Source GIS • request one-size image non- tiled • don’t consider cache Oracle 7
    • Problems on Existing System  Absence of Cache Server  Request for same region data  So, Frequent GeoServer Down at Peak Times  Reliability issue  Take lots of time to import traffic data into PostgreSQL  Doubt on GeoServer , PostGIS,PostgreSQL about low performance 8
    • System Improvement Goals in 2012  NTIC’s Requirements  Support 200,000 Users Per Day  Change DBMS to SQL Server  Consulting about Open Source GIS  Our Solutions  Reconstruct System Architecture and Redevelop SW  Change Mobile Client Request to Tiled Map base  Adopt Squid proxy server with SSD as Cache Server  To determine Effective Tiled Map Time and Region: Using  WMTS interface  content expire time  custom Time tag  Produce Tiled Map data every 5 minutes in advance 9
    • Architecture(2012) Mobile Data Provider MobileMananger Tomcat 7 (WAS) Windows Server Info Server Squid Proxy Server 2.7 Windows Server Cache Server GeoServer2.1.4 Tomcat 7 (WAS) Windows Server Map Server SQL Server2008R2 Windows Server Geo DB Server Info Server Map Server SQL Server SQL Server Geo DB Server SQL Server Cache Server • Support 200,000 Users • 256x256 tiled map • Apply OpenLayers Cache Structure into Mobile App(Android and iPhone App) • Apps Polling traffic map every 5 minutes • Compliance to Cache flow of HTTP 1.1 Cache Maker 10
    • Results  System Endured at Peak Times but, Not Satisfied Level  Sometimes Response Time went slowly  Transaction increased 10 times per User owing to tiled map  Polling Map Strategy causes unnecessary requests  Squid had in trouble when it reaches to over 100,000 Connections  Impossible to update tiled traffic map data within 5 minutes  Traffic Map Data consist of 10 levels(Zoom level) ~over 1 million tiled maps.  So, within 5minutes, only 8 level-map data can be updated. 11
    • Results  Scalability Issue  Cache Server UP GeoServer & SQLServer UP Cost UP  Cache Maker requests UP SQLServer Load UP(n times) Cache Server Map Server Cache Server Cache Server Map Server Map Server DB Server DB Server DB Server Bad scalable 12
    • Improvement Strategy  GeoServer connects PostGIS 1by1 instead of SQL Server-> Cost Down, Speed up Spatial Query  Adopt Memory Disk for Cache Server instead of SSD -> Cost Down  Push Tiled Traffic Map data into Cache Server ->Reduce Transaction time  Drop Polling method every 5 minutes to update traffic map-> Reduce Transactions 13
    • Architecture(2013) Mobile Data Provider MobileMananger Tomcat 7 (WAS) Windows Server Info Server Squid Proxy Server 2.7 ENGINX (Web Server) Windows Server Cache Server GeoServer 2.3 PostGIS 2.1 Tomcat 7 (WAS) PostgreSQL 9.2 Windows Server Map& GeoDB Server Info Server Map & GeoDB Server • Support 300,000 users per day • PostGIS to Query Spatial data • Request map only when client’s map view vhanging • Push tiled traffic data into Cache server Post GIS Post GIS Post GIS SQL Server Traffic Data Streaming Replication Cache Server CSV Tile generation manager 14
    • Tile Generation Manager Map Server Map Server Map Server Map Server Tile Generation Manager Divide Job Cache Server Cache Server Cache Server Push Generated Tile  Tile Generation Manager  Divide jobs for each GeoServer clearly To Produce map tile data in parallel  Push Tiled Traffic Map data into Cache Server PushCache Server Map Server Cache Server Cache Server For more connection, just add cache server  more scalable 15 Map Server
    • TileMap Update Idea!  Changing data are only roads  #of Map Tiles that roads across is Not Much!  So, Update Map Tiles Passing roads Only When Traffic Condition Changed, Instead of All the tiles! Mobile Apps can get Changed TileMap Only 16
    • Improvements 2012 2013 Initial (Total) tile Generation 90 minute (empty tile included ~over 1,437,000 tiles) 6~7 minute (road tile only ~183,000 tiles) Update interval of Tile Generation 5 minute (8 levels, not modified or empty time included) 1 minute (10 levels, modified tile only) Users per day 200,000 >300,000 Scalability Not good Very good In Service Now! 17
    • Lessons Learned  To persuade customer to adopt Opensource GIS  Need confidence of Performance about Opensource GIS  Make sure that Opensource GIS has equivalent performance to commercial products 18
    • Q&A  Please Ask BJ Jang via Email ! bjjang@gaia3d.com With experience of this project, he is constructing Mobile Weather Chart Service Using GeoServer and PostgreSQL at KMA(Korea Meteorological Administration) 19