Hotspot Detection in a Service-Oriented Architecture.

Large-scale websites are predominantly built as a service-oriented architecture. Here, services are specialized for a certain task, run on multiple machines, and communicate with each other to serve a user's request. Reducing latency and improving the cost to serve is quite important, but optimizing this service call graph is particularly challenging due to the volume of data and the graph's non-uniform and dynamic nature. In this paper, we present a framework to detect hotspots in a service-oriented architecture. The framework is general, in that it can handle arbitrary objective functions. We show that finding the optimal set of hotspots for a metric, such as latency, is NP-complete and propose a greedy algorithm by relaxing some constraints. We use a pattern mining algorithm to rank hotspots based on the impact and consistency. Experiments on real world service call graphs from LinkedIn, the largest online professional social network, show that our algorithm consistently outperforms baseline methods.