Abstract: Some embodiments automatically and proactively adjust network device configuration settings during network operation, based on correlations between device performance and device configuration. Correlations are computed using statistics routines or computed by a machine learning module. Some embodiments share adjusted configuration values via a cache, and some persist adjusted values through an application restart. In some embodiments, the cache is hierarchical and different kinds of reconfiguration data are shared at different levels. In some embodiments, the configuration value is shared only between application instances that have sufficiently similar contexts. Some embodiments detect a correlation loss and fall back to a known good configuration setting or a default configuration setting. Some embodiments optimize network internode communications by making dynamic adjustments which are not available from static configuration settings or from static configuration rules.

Abstract: Some embodiments automatically and proactively adjust network device configuration settings during network operation, based on correlations between device performance and device configuration. Correlations are computed using statistics routines or computed by a machine learning module. Some embodiments share adjusted configuration values via a cache, and some persist adjusted values through an application restart. In some embodiments, the cache is hierarchical and different kinds of reconfiguration data are shared at different levels. In some embodiments, the configuration value is shared only between application instances that have sufficiently similar contexts. Some embodiments detect a correlation loss and fall back to a known good configuration setting or a default configuration setting. Some embodiments optimize network internode communications by making dynamic adjustments which are not available from static configuration settings or from static configuration rules.