Abstract: Disclosed herein are a system and method for trend prediction of signals in a time series using a Markov model. The method includes receiving a plurality of data series and input parameters, where the input parameters include a time step parameter, preprocessing the plurality of data series according to the input parameters, to form binned and classified data series, and processing the binned and classified data series. The processing includes initializing a Markov model for trend prediction, and training the Markov model for trend prediction of the binned and classified data series to form a trained Markov model. The method further includes deploying the trained Markov model for trend prediction, including outputting trend predictions. The method develops an architecture for the Markov model from the data series and the input parameters, and disposes the Markov model, having the architecture, for trend prediction.

Abstract: A host enables any adapter of multiple adapters of the host to concurrently support any VIPA of the multiple VIPAs assigned to the host. Responsive to a failure of at least one particular adapter from among the multiple adapters, the host triggers the remaining, functioning adapters to broadcast a separate hardware address update for each VIPA over the network, such that for a failover in the host supporting the multiple VIPAs the host directs at least one other host accessible via the network to address any new packets for the multiple VIPAs to one of the separate hardware addresses of one of the remaining adapters.