Context-Based Analytics - Establishing Explicit Links between Runtime Traces and Source Code.

Diagnosing problems in large-scale, distributed applications running in cloud environments requires investigating different sources of information to reason about application state at any given time. Typical sources of information available to developers and operators include log statements and other runtime information collected by monitors, such as application and system metrics. Just as importantly, developers rely on information related to changes to the source code and configuration files (program code) when troubleshooting. This information is generally scattered, and it is up to the troubleshooter to inspect multiple implicitly-connected fragments thereof. Currently, different tools need to be used in conjunction, e.g., log aggregation tools, source- code management tools, and runtime-metric dashboards, each requiring different data sources and workflows. Not surprisingly, diagnosing problems is a difficult proposition. In this paper, we propose Context-Based Analytics, an approach that makes the links between runtime information and program-code fragments explicit by constructing a graph based on an application-context model. Implicit connections between information fragments are explicitly represented as edges in the graph. We designed a framework for expressing application-context models and implemented a prototype. Further, we instantiated our prototype framework with an application-context model for two real cloud applications, one from IBM and another from a major telecommunications provider. We applied context-based analytics to diagnose two issues taken from the issue tracker of the IBM application and found that our approach reduced the effort of diagnosing these issues. In particular, context-based analytics decreased the number of required analysis steps by 48% and the number of needed inspected traces by 40% on average as compared to a standard diagnosis approach.