TY - CHAP
T1 - Online response time optimization of apache web server
AU - Liu, Xue
AU - Sha, Lui
AU - Diao, Yixin
AU - Froehlich, Steven
AU - Hellerstein, Joseph L.
AU - Parekh, Sujay
PY - 2003
Y1 - 2003
N2 - Properly optimizing the setting of configuration parameters can greatly improve performance, especially in the presence of changing workloads. This paper explores approaches to online optimization of the Apache web server, focusing on the MaxClients parameter (which controls the maximum number of workers). Using both empirical and analytic techniques, we show that MaxClients has a concave upward effect on response time and hence hill climbing techniques can be used to find the optimal value of MaxClients. We investigate two optimizers that employ hill climbing - one based on Newton's Method and the second based on fuzzy control. A third technique is a heuristic that exploits relationships between bottleneck utilizations and response time minimization. In all cases, online optimization reduces response times by a factor of 10 or more compared to using a static, default value. The trade-offs between the online schemes are as follows. Newton's method is well known but does not produce consistent results for highly variable data such as response times. Fuzzy control is more robust, but converges slowly. The heuristic works well in our prototype system, but it may be difficult to generalize because it requires knowledge of bottleneck resources and an ability to measure their utilizations.
AB - Properly optimizing the setting of configuration parameters can greatly improve performance, especially in the presence of changing workloads. This paper explores approaches to online optimization of the Apache web server, focusing on the MaxClients parameter (which controls the maximum number of workers). Using both empirical and analytic techniques, we show that MaxClients has a concave upward effect on response time and hence hill climbing techniques can be used to find the optimal value of MaxClients. We investigate two optimizers that employ hill climbing - one based on Newton's Method and the second based on fuzzy control. A third technique is a heuristic that exploits relationships between bottleneck utilizations and response time minimization. In all cases, online optimization reduces response times by a factor of 10 or more compared to using a static, default value. The trade-offs between the online schemes are as follows. Newton's method is well known but does not produce consistent results for highly variable data such as response times. Fuzzy control is more robust, but converges slowly. The heuristic works well in our prototype system, but it may be difficult to generalize because it requires knowledge of bottleneck resources and an ability to measure their utilizations.
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U2 - 10.1007/3-540-44884-5_25
DO - 10.1007/3-540-44884-5_25
M3 - Chapter
AN - SCOPUS:35248877648
SN - 3540402810
SN - 9783540402817
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 461
EP - 478
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Jeffay, Kevin
A2 - Stoica, Ion
A2 - Wehrle, Klaus
PB - Springer
ER -