Model analysis for business event processing

L. Zeng H. Lei T. Koyanagi H. Ohsaki H. Chang Business event processing requires efficiently processing live events, computing business performance metrics, detecting business situations, and providing real-time visibility of key performance indicators. Given the high volume of events and significant complexity of computation, system performance event throughput is critical. In this paper, we advocate model-analysis techniques to improve event throughput. In the build time, a series of model analyses of the application logic are conducted to understand such factors as runtime data-access path, data flow, and control flow. Such analyses can be used to improve throughput three ways: at build time it can be used to facilitate the generation of customized code to optimize I/O and CPU usage; information about the control flow and data flow can be used to ensure that CPU resources are used effectively by distributing event-processing computation logic evenly over time; and at runtime, knowledge gained from the model can be used to plan multithreaded parallel event-processing execution to reduce wait states by maximizing parallelization and reducing the planning overhead. This paper presents a series of model-analysis techniques and the results of experiments that demonstrate their effectiveness.