[mapserver-users] Mapserver installation in cloud environments (kubernetes)

[Seth G]

There was an interesting talk on running MapServer on Amazon Lambda - https://www.youtube.com/watch?v=g_WMDxsuCfk&t=2s
The CGI approach of MapServer scales well as each request can be run separately (at a slight overhead for database connections, PROJ startup etc). 

Seth

--
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On Fri, Apr 23, 2021, at 5:20 PM, Andreas Neumann wrote:
> Hi,
> 
> For a small project as part of the Swiss National Geodata Infrastructure (grant project) several people worked on a study document called "Cloud-optimized OGC WMS Server" where we analyzed problems that can arise when you install an OGC web server in the cloud (e.g. docker image deployed via Kubernetes, OpenShift or the likes). This work had a focus on QGIS Server with it's own set of problems - but some of the issues studied in this document also matter for other OGC WMS servers, such as UMN Mapserver or Geoserver, such as the load balancing problem, how to share resources, etc.
> 
> Here is the link to the document (not in final form yet, but close to being final): https://docs.google.com/document/d/1cOUWgzalRx7CHWTFgHz6-uyScsCcoaEmYC0VBHdZShQ/edit#heading=h.c7gq4lie7ys2
> 
> I wonder if any similar work has been done specifically around problems, challenges and solutions when you deploy UMN Mapserver in cloud environments? Do you know of any work?
> 
> One major problem that probably all installations of an OGC WMS server have is how to deploy a more intelligent load balancing system? Often, the default load balancer is some kind of round robin load balancer system, but often this leads to inferior results where "cheap and short" requests (such as a simple GetFeatureInfo or GetLegendGraphics request) can be queued behind a long-running GetMap request (potentially with many layers, many features and a high-dpi, such as 600dpi, where the request can take several seconds to process.
> 
> In our production system we are currently separating the requests to dedicated instances for short requests and potentially long requests, to avoid the above mentioned scenario, but we are not so satisfied with the solution, as it is  a bit inflexible and also a bit harder to maintain. Ideally, we would like to have a more intelligent load balancer with incoming queue that holds back requests as long as all WMS server instances are busy. This would avoid the situation where a "less intelligent" load balancer would simply forward the requests to instances based on Round-Robin principle.
> 
> Do you know of any work in the UMN Mapserver community regarding cloud deployment, cloud optimization, load balancing and resource sharing?
> 
> In our study document I'd like to also include the perspective of other WMS servers besides QGIS server, so any input would be welcome.
> 
> Thanks,
> Andreas
> 
> --
> Andreas Neumann
> QGIS.ORG <http://qgis.org/> board member (treasurer)
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