Performance of standardized web map servers for remote sensing Imagery

Data Flow: From Space to Earth. Applications and interoperability congress

PERFORMANCE OF STANDARDIZEDWEB MAP SERVERS

FOR REMOTE SENSING IMAGERYFOR REMOTE SENSING IMAGERYJoan Masó, Paula Díaz, Xavier Pons.

Data Flow: From Space to Earth. Applications and interoperability congressMarch 2011

CREAF & Universitat Autònoma de Barcelona

Index

1. INTRODUCTION

MATERIALS AND METHODOLOGY2. MATERIALS AND METHODOLOGY

3. EVALUATION OF WMS CONCURRENT REQUESTS TO A SINGLE SERVER

4. EVALUATION OF A CLUSTER OF SERVERS4. EVALUATION OF A CLUSTER OF SERVERS

5. TILING THE REQUEST AND THE RESPONSE

Data Flow: From Space to Earth. Applications and interoperability congress March 2011

6. CONCLUSIONS

1. INTRODUCTION

Amount of data (satellite)Web portals and clearinghouses

Standards available

Implementation of standardized protocols

Space technologies

Hazard modeling and analysis

Remote sensing imagery Space technologiesimprovements

Integration in bigger System


Communication satellitesg gg y

of Systems, like GEOSS

2. MATERIALS AND METHODOLOGYClientsServers ClientsStandardsData

Web Map Service (WMS) ( S)

Web Map Service Cache (WMS‐C)

Tile Map Service (TMS)

This communication evaluates the efficiency and possibilities of several maps servers

GEO‐PICTURES is an EU FP7 SPACE project with the aim of integrating lli i i h i i d d i l f


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

2. MATERIALS AND METHODOLOGY

22 satellite images of GeoEye‐1 (Orthorectified GeoTIFF; provided by Google)(http://www google com/relief/haitiearthquake/geoeye html)(http://www.google.com/relief/haitiearthquake/geoeye.html)

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

Traditional WMS server‐client interaction

WMSServer

requestGetMap

URLServer

responseresponse

All studied protocols request maps by creating an URL with specific syntax

URL requests were randomly generated


The time response is stored in an archive and analyzed

3 . EV A LU A T IO N O F W M S CO N CU RR EN T R EQ U EST S TO A

SINGLE SERVER

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

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

The requests were made from up to 6 concurrentclientsclients.

The time response for the requests are exposed in h


graphs.

3. EVALUATION OF WMS CONCURRENT REQUESTS TO A SINGLE SERVER

Evaluation of the time request for Pixel Size (multiple clients - MiraMon Server)

789

10

con

5 Clients4 Clients3 Clients2 Clients

Evaluation of the time request for Pixel Size (multiple clients - MapServer)

6789

10

econ

5 Clients4 Clients3 Clients2 Clients

0123456

0.001 0.010 0.100 1.000 10.000

PixelSize(secondsofarc)

Tim

e (s

ec

0123456

0.001 0.010 0.100 1.000 10.000

PixelSize(secondsofarc)

Tim

e (s

e

Pixel Size (seconds of arc) Pixel Size (seconds of arc)

Evaluation of the time request for Pixel Size (multiple clients -GeoServer)

910

5 Clients4 Clients3Clients

012345678

Tim

e (s

econ

3 Clients2 Clients


00.001 0.010 0.100 1.000 10.000

Pixel Size (seconds of arc)

4. EVALUATION OF A CLUSTER OF SERVERS

To overcome the performance degradation in concurrent requests a possible solution is to set up aconcurrent requests a possible solution is to set up a cluster of servers

h l f i l i lThe cluster of servers act as a virtual single server6 computers are able to respond at same time to different clients as if they were like a faster single serverclients as if they were like a faster single server

We carried out some tests comparing a WMS single


server and a WMS in a computer cluster server

4. EVALUATION OF A CLUSTER OF SERVERSEvaluation of the response time for Pixel Size (Clients to MiraMon Single Server)

1000

120.0

140.0

160.0

180.0

lisec

o

17 clients

14 Clients

11 Clients

8 Clients

4Cli t

0.0

20.0

40.0

60.0

80.0

100.0

Tim

e (m

ill 4 Clients

1 Client

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

160.0

180.017 clients

14 Clients

11Clients

0.0000 2.0000 4.0000 6.0000 8.0000 10.0000 12.0000 14.0000 16.0000


40.0

60.0

80.0

100.0

120.0

140.0

Tim

e (m

illis

eco

11 Clients

8 Clients

4 Clients

1 Client


0.0

20.0

0.0000 2.0000 4.0000 6.0000 8.0000 10.0000 12.0000 14.0000 16.0000


5. 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 viewportThey need several tiles to cover the whole viewportThey can recycle some tiles when the user moves the view laterallyAlso can take advantage of the cache mechanismsIf 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 ( p )configurations.

Speed metrics in the 3 different services were done for the three servers mentioned


mentioned

5. TILING THE REQUEST AND THE RESPONSETime response for unlimited concurrent 256x256 Time response for complete window request Time response for sequential 256x256 tiledTime response for up to 4 concurrent 256x256

tiled requests on a pure WMS server

2.5

3 MMServer

GeoServer

MapServer

p p qon a WMS server

2.5

3 MMServer

GeoServer

MapServer

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

2.5

3 MMServer

GeoServer

MapServer

tiled requests on a pure WMS server

3

3 MMServer

GeoServer

MapServer

1

1.5

2

Tim

e (s

econ

ds)

1

1.5

2

Tim

e (s

econ

ds)

1

1.5

2

Tim

e (s

econ

ds)

1

2

2

Tim

e (s

econ

ds)

0

0.5

0.001 0.010 0.100 1.000 10.000


0

0.5

0.001 0.010 0.100 1.000 10.000


0

0.5

0.001 0.010 0.100 1.000 10.000


0

1

0.001 0.010 0.100 1.000 10.000


Data Flow: From Space to Earth. Applications and interoperability congress March 2011Concurrent Tiled WMS

Full window WMS Sequential tiled WMSSemi-concurrent Tiled WMS

6. CONCLUSIONSThe speed tests described are a practical demonstration of the suitability of certain serversand service configurations in certain domains where reliability of services is imperative

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

To solve this situation a cluster server can be used

Results show that WMS servers perform worst if clients using tile strategies are used over servers that are not optimized for this situationservers that are not optimized for this situation

Future work will analyze tile cache strategies (TMS and WMTS) and implementations to overcome concurrent situations that can severely degrade map server performance.

MapServer and GeoServer with common data configuration do not require any data i b h i f i h h i h i i d ipreparation 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


single client requests, but GeoServer is surprisingly faster under concurrent simultaneous requests.

Thank you!

Joan Masó Paula Díaz Xavier Pons

Paula diaz@creaf uab es

Data Flow: From Space to Earth. Applications and interoperability congressMarch 2011

[email protected]

Data & Analytics

Performance of standardized web map servers for remote sensing Imagery