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Identifying the location of performance bottlenecks is a non-trivial challenge when scaling n-tier applications in computing clouds. Specifically, we observed that an n-tier application may experience significant performance loss when bottlenecks alternate rapidly between component servers. Such rapidly alternating bottlenecks arise naturally and often from resource dependencies in an n-tier system and bursty workloads. These rapidly alternating bottlenecks are difficult to detect because the saturation in each participating server may have a very short lifespan (e.g., milliseconds) compared to current system monitoring tools and practices with sampling at intervals of seconds or minutes. Using passive network tracing at fine-granularity (e.g., aggregate at every 50ms), we are able to correlate throughput (i.e., request service rate) and load (i.e., number of concurrent requests) in each server of an n-tier system. Our experimental results show conclusive evidence of rapidly alternating bottlenecks caused by system software (JVM garbage collection) and middleware (VM collocation).
Throughput, Correlation, Time measurement, Hardware, Monitoring, Web servers

Qingyang Wang et al., "An Experimental Study of Rapidly Alternating Bottlenecks in n-Tier Applications," 2013 IEEE 6th International Conference on Cloud Computing (CLOUD), Santa Clara, CA, USA, 2013, pp. 171-178.
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