Infrastructure

Servers

Server	Software	Operating system	Resources
prod-ocid-web-01.colt.enaikoon.de	Apache + Tomcat + MongoS	Ubuntu 12.04 LTS	2 vCPU, 4 GB
prod-ocid-web-02.colt.enaikoon.de	Apache + Tomcat + MongoS	Ubuntu 12.04 LTS	2 vCPU, 4 GB
prod-ocid-cfgsrv-01.colt.enaikoon.de	MongoDB ConfigServer	Ubuntu 12.04 LTS	1 vCPU, 2 GB
prod-ocid-web-02.colt.enaikoon.de	MongoDB ConfigServer	Ubuntu 12.04 LTS	1 vCPU, 2 GB
prod-ocid-web-03.colt.enaikoon.de	MongoDB ConfigServer	Ubuntu 12.04 LTS	1 vCPU, 2 GB
prod-ocid-db-01.colt.enaikoon.de	MongoDB Replication Set	Ubuntu 12.04 LTS	4 vCPU, 48 GB
prod-ocid-db-02.colt.enaikoon.de	MongoDB Replication Set	Ubuntu 12.04 LTS	4 vCPU, 48 GB
prod-ocid-db-03.colt.enaikoon.de	MongoDB Replication Set	Ubuntu 12.04 LTS	4 vCPU, 48 GB

Software stack

Operating System

All OpenCellID servers are running with Ubuntu Linux 12.04 LTS.

Frontend

The web frontend uses Apache web server as a proxy for serving web requests to Tomcat.
The OpenCellID web application is running on Tomcat and is reading and writing cell measurements data to/from the MongoDB database backend. jQuery Mobile is responsible for provide cross-platform user interface. The map is displayed using OpenStreetMap combined with Leaflet library.

Database Backend

The database backend, with a current 4.4 million cell towers and about 565 million measurements (1.1.2014), is a MongoDB database cluster with six servers.
Three servers are serving as MongoDB configuration servers and the other three servers are serving as database backend with one replication set spread across the three servers.

Challenges and solutions

The OpenCellID community is very strong and continously provides a high number of measurements.
This immediately poses a few challenges:

• High Volume
  data arrives from many differnet sources and is rapidly growing
• Scale
  growth of data should go along with predictable, incremental costs and no downtime should be needed when adding additional server resources
• Data Processing
  analyzing and processing of rapidly growing data must be constantly efficient.
  The current solutions are based on MongoDB and its features:
  
  • Native Analytics
    using the integrated aggregation framework and Map/Reduce to calculate aggregates and analyses in place without the need of prior exporting data to other systems
  • Advanced Geo Queries
    using geospatial MongoDB support to execute complex queries
  • Horizontal Scaling
    sharding making it easy to scale applications horizontally on commodity hardware to accommodate growing increased throughput
  • Reduced Total Cost of Ownership
    as open-source storage MongoDB is a very cost-effective solution