William Hinsberg, Joy Cheng, et al.
SPIE Advanced Lithography 2010
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. © Copyright 2007 by International Business Machines Corporation.
William Hinsberg, Joy Cheng, et al.
SPIE Advanced Lithography 2010
Zohar Feldman, Avishai Mandelbaum
WSC 2010
A. Gupta, R. Gross, et al.
SPIE Advances in Semiconductors and Superconductors 1990
Leo Liberti, James Ostrowski
Journal of Global Optimization