Impact of user concurrency in commonly used OGC map server implementations

INFOCOMP 2011

Impact of User Concurrency in Commonly

Used OGC Map Server Implementations

INFOCOMP 2011October 2011

Used OGC Map Server ImplementationsJoan Masó, Paula Díaz, Xavier Pons, José L. Monteagudo-Pereira,

Joan Serra-Sagristà, Francesc Aulí-Llinàs

Center of Research in Ecology and Forestry Applications

Universitat Autònoma de Barcelona

Introduction


Introduction

Three Steps for Disaster Management


GEO-PICTURES is an EC FP7 SPACE project with the aim of

integrating satellite imagery with in-situ sensors and geo-tagged images as a tool for decision making in emergency crisis

Rapid Mapping and Technology


The images

� 22 satellite images of GeoEye-1 (Orthorectified GeoTIFF; provided by Google)

� (http://www.google.com/relief/haitiearthquake/geoeye.html)

� Covering Port-au-Prince


� Covering Port-au-Prince and surroundings

� 16-01-2010, 3 days after the Earthquake

� Each image has 196 373 kb � 4.21 Gb

� 40 994x57 392 pixels

pdiaz4

Diapositiva 5

pdiaz4 Al Web de descàrrega posa:

By downloading these files, you agree to use the imagery solely for non-commercial use related to emergency relief, and to provide a proper and distinct photo credit to “GeoEye Satellite Image.”

Això significa que hem de posar el logo de GeoEye a la presentació?pdiaz; 13/10/2010

The service

� We are going to test implementations of two standards:

� Web Map Service (WMS) standard


� Web Map Tile Service (WMTS) standard

� Both standards are Open Geospatial Consortium standards

� Assess performance

Web Map Service


This is a map

Web Map Tile Service


This is a tile

Methodology


Methodology

The analysisServers ClientsStandardsData

Web Map Service

(WMS)

Web Map Service


Web Map Service

Cache (WMS-C)

Tile Map Service

(TMS)

Tile Map Tile Service (WMTS)

Traditional WMS server-client interaction

WMSServer

request GetMap

URL


response

� All studied protocols request maps by creating an URL with specific syntax� http://www.ogc.uab.es/cgi-bin/SIGMA/MiraMon5_0.cgi?VERSION=1.1.0&

REQUEST=GetMap&SRS=EPSG:27573&BBOX=532776,22819,538776,26419&WIDTH=600&HEIGHT=360&LAYERS=mh-andorra&STYLES=&FORMAT=image/gif&TRANSPARENT=TRUE

� URL requests were randomly generated and sent from different clients

� The time response is stored in logs and latter analyzed

Results


Results

Evaluation of WMS Concurrent Requests to a Single Server

WMSServer

reqGetMap


WMS Server Server

res

Evaluation of WMS Concurrent Requests to a Single Server

�More than one hundred different requests were done (without optimizing speed configurations).

�The influence of the pixel size and the image size in


�The influence of the pixel size and the image size in the time response were evaluated

�The requests were made from up to 5 concurrentclients.

�The time response for the requests are exposed in graphs.

Evaluation of WMS Concurrent Requests to a Single ServerResponse time of 5 different server vendors at different scales (pixel sizes) each one under

5 simultaneous requests

10.000

MapServer

GeoServerMiraMon Server

ArcGIS Server Express Server


0.010

0.100

1.000

0.0001 0.0010 0.0100 0.1000 1.0000 10.0000 100.0000

Pixel Size (seconds of arc)

Tim

e (s

econ

ds)

Evaluation of a Cluster of Servers

�To overcome the performance degradation in

concurrent requests a possible solution is to set up a

cluster of servers


cluster of servers

�The cluster of servers act as a virtual single server

� 6 computers are able to respond at same time to different

clients as if they were like a faster single server

Evaluation of a Cluster of ServersEvaluation of the response time for Pixel Size (Clients to MiraMon Single Server)

60.0

80.0

100.0

120.0

140.0

160.0

180.0

Tim

e (m

illis

econ

ds)

17 clients

14 Clients

11 Clients

8 Clients

4 Clients

1 Client


Evaluation of the response time for Pixel Size (Clients to MiraMon Server Cluster)

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

0.0000 2.0000 4.0000 6.0000 8.0000 10.0000 12.0000 14.0000 16.0000


Tim

e (m

illis

econ

ds)

17 clients

14 Clients

11 Clients

8 Clients

4 Clients

1 Client

0.0

20.0

40.0

0.0000 2.0000 4.0000 6.0000 8.0000 10.0000 12.0000 14.0000 16.0000


Tiling the Request and the Response: Sequential

WMSServer

reqGetMap

reqGetMap

reqGetMap

reqGetMap

reqGetMap


Server

Tiling the Request and the Response

� Some WMS clients are able to tile the space in a regular matrix of small pieces.

� They need several tiles to cover the whole viewport

� They can recycle some tiles when the user moves the view laterally


They can recycle some tiles when the user moves the view laterally

� Also can take advantage of the cache mechanisms

� If the caching mechanism cannot help the response time can increase even if each tile is smaller that the whole view

� Tiled clients (tiles of 256x256 pixels) were simulated in three configurations.

Tiling the Request and the Response: Sequential�Results of the WMTS speed metrics

Time response for sequential 256x256 tiled requests on a pure WMS server

10MapServerGeoServerTilecache


0.01

0.1

1

0.000

90.0

010

0.002

40.0

029

0.005

10.0

076

0.010

20.0

145

0.018

80.0

246

0.033

10.0

462

0.185

50.2

130

0.265

40.4

717

0.567

01.0

383

1.642

5

seconds of arc

Sec

on

ds

(tim

e)

TilecacheMMServerArcGIS ServerExpress ServerGeoWebCache

Sequential tiled WMS

Tiling the Request and the Response: Concurrent

WMSServer

reqGetMap 1reqGetMap 2reqGetMap 3reqGetMap 4reqGetMap 5


Server

Tiling the Request and the Response:Concurrent�Results of the WMTS speed metrics



Time response for unlimited concurrent 256x256 tiled requests on a pure WMS server

10MapServer


0.01

0.1

1

0.000

90.0

010

0.002

40.0

029

0.005

10.0

076

0.010

20.0

145

0.018

80.0

246

0.033

10.0

462

0.185

50.2

130

0.265

40.4

717

0.567

01.0

383

1.642

5

seconds of arc

Sec

on

ds

(tim

e)


Sequential tiled WMSConcurrent Tiled WMS

0.01

0.1

1

10

0.00

090.

0011

0.00

270.

0049

0.00

760.

0110

0.01

590.

0245

0.03

310.

0627

0.19

150.

2348

0.47

170.

5745

1.17

30

seconds of arc

Sec

on

ds

(tim

e)

GeoServerTilecacheMMServerArcGIS ServerExpress ServerGeoWebCache

Tiling the Request and the Response: Semi-concurrent

�Results of the WMTS speed metrics



Time response for unlimited concurrent 256x256 tiled requests on a pure WMS server

10MapServer

Time response for up to 4 concurrent 256x256 tiled requests on a pure WMS server


0.01

0.1

1

0.000

90.0

010

0.002

40.0

029

0.005

10.0

076

0.010

20.0

145

0.018

80.0

246

0.033

10.0

462

0.185

50.2

130

0.265

40.4

717

0.567

01.0

383

1.642

5

seconds of arc

Sec

on

ds

(tim

e)


Sequential tiled WMSConcurrent Tiled WMS

0.01

0.1

1

10

0.00

090.

0011

0.00

270.

0049

0.00

760.

0110

0.01

590.

0245

0.03

310.

0627

0.19

150.

2348

0.47

170.

5745

1.17

30

seconds of arc

Sec

on

ds

(tim

e)

GeoServerTilecacheMMServerArcGIS ServerExpress ServerGeoWebCache

Semi-concurrent Tiled WMS

0.01

0.1

1

10

0.00

090.

0011

0.00

270.

0049

0.00

760.

0110

0.01

590.

0245

0.03

310.

0627

0.19

150.

2348

0.47

170.

5745

1.17

30

seconds of arc

Sec

on

ds

(tim

e)

MapServerGeoServerTilecacheMMServerArcGIS ServerExpress ServerGeoWebCache

Conclusions (1/2)

� The work presented covers:

� A metrics on WMS and WMTS services

� GeoServer, MapServer, MiraMon Map Server, ArcGIS Server, Express Server

� TileCache GeoWebCache

� A set of recommendations of Disaster Management

Easy to setup: MapServer


� Easy to setup: MapServer

� Easiest configure and update: GeoServer

� Fastest: Express Server

� The speed tests described are a practical demonstration of the suitability of certain servers and service configurations in certain domains where reliability of services is imperative

� We have seen differences in performance of 2 order of magnitude.

� All the analyzed servers have slower performances when the number of simultaneous clients is increased

� A cluster of server dramatically improves performance

p2

Diapositiva 25

p2 HE FET DUES OPCIONS PER A LA DIAPO 1/2 DE CONCLUSIONS, UNA MÉS DENSA I L'ALTR AMOOLT MÉS LLEUGERA. TAMBÉ POTS COMBINAR LA LLEUGERA 1/2 AMB LA 2/2 QUE TROB QUE ÉS DENSA PERÒ QUE ÉS LA QUE VAREM FER A VENÈCIA. p.diaz; 25/10/2011

Conclusions (2/2)� In order to improve performance, some clients request tiles to servers that are not

prepared to serve them

� This results on no better performance in some servers

� Server optimization for tile requests is needed.

� Web clients auto-impose themselves a limit in the number of parallel request


� Web clients auto-impose themselves a limit in the number of parallel request

� We saw that this more conservative strategy results on better performance

� MapServer and GeoServer with common open source services that do not require any data preparation process but

� their performance is worst than other services that require indexing methods like MiraMon Map Server

� MapServer (based on C++ code) performs better than GeoServer (based on Java code) under single client requests, but GeoServer is surprisingly faster under concurrent simultaneous requests.

Thank you!


Thank you!

[email protected]

Impact of user concurrency in commonly used OGC map server implementations

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