Abstract: A method is provided to produce training data set for training an inference engine to predict events in a data center comprising: producing probe vectors corresponding to components of a data center, each probe vector including a sequence of data elements, one of the probe vectors indicating an event at a component and at a time of the event; and producing at a master device a set of training snapshots, wherein each training snapshot includes a subsequence of data elements that corresponds to a time increment that matches or that occurred not later than the indicated time of occurrence of the event.

Abstract: A method is provided to produce training data set for training an inference engine to predict events in a data center comprising: producing probe vectors corresponding to components of a data center, each probe vector including a sequence of data elements, one of the probe vectors indicating an event at a component and at a time of the event; and producing at a master device a set of training snapshots, wherein each training snapshot includes a subsequence of data elements that corresponds to a time increment that matches or that occurred not later than the indicated time of occurrence of the event.

Abstract: An apparatus and method of a mobile node selecting a transmission channel of a mesh network is disclosed. The method includes the mobile node detecting a quality of routing beacons received over a previously selected transmission channel. If the quality of the routing beacons is below a predetermined threshold, then the mobile node transmits probe requests on each of a plurality of available transmission channels. Responses to the probe request are collected from at least one upstream access nodes over at least one of the available transmission channels. A new transmission channel is selected based upon the responses received over the available transmission channels.