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.

Abstract: Instances of a service are deployed at different quality of service (QOS) levels associated with different instance priorities. A manifest for a service specifies a first QoS level associated with a first QoS level priority value. A first deployment object is created for deploying instances of the service at the first QoS level, and is associated with a first combined priority value determined based on the priority of the service and the first Qos level priority value. The first deployment object is further associated with a deployment quota associated with deployment of the service at the first QoS level. An instance of the service is deployed using the first deployment object when the instances of the service currently deployed in the cluster satisfy a predetermined relationship with the deployment quota associated with the first QoS level.

Abstract: A taxonomy-agnostic data protection framework includes a plugged-in data classification taxonomy definition having a taxonomy identifier and a set of data classification indicators. The taxonomy-agnostic data protection framework also includes a plugged-in set of data classification processing routines, and a plugged-in mapping mechanism which maps between data classification processing routines and data classification indicators. The framework facilitates efficient, accurate, and thorough implementation of data classification propagation per the plugged-in taxonomy, both within a given program and between programs that connect over a network. The framework also facilitates flexible implementation of per-taxonomy data protection actions such as deletion, redaction, encryption, anonymization, pseudonymization, hashing, or enrichment, in response to individual or combined data classification indicators.