Monitoring Large-Scale Location-Based Information Systems

Monitoring the state of a distributed virtual world is challenging for several reasons: 1) the distributed information must be gathered in real-time without affecting the performance of the information system, 2) in large-scale systems it is impossible for a single node to collect and process all the data, 3) the vast information must be filtered and aggregated according to what the human observer wants to focus on, and 4) the point of interest of the observer can change frequently. In this paper we present and evaluate a non-intrusive monitoring middleware that addresses these challenges by dynamically partitioning the geographic map (e.g., of the virtual world or the game) in terms of map objects and (expected) state changes. We assign a different collector node to each of these partitions to collect and pre-process the data, and forward it to a central monitoring node. Furthermore, we provide mechanisms to efficiently filter and aggregate location changes, the pre-dominant changes in location-based information systems. We describe a specific monitoring setup that takes advantage of the replication model that is common in many virtual worlds and multiplayer games to collect the data. Finally, we present extensive performance results that show the trade-offs between scalability, precision, and real-time performance.