obtaining, by at least one computing device, a training dataset for a neural network executed by the at least one computing device, the training dataset comprising historical data associated with resource consumption of a set of workloads within a hyper-converged infrastructure, wherein the neural network comprises a long short term memory recurrent neural network operating on a time window input, the neural network further utilizing gates comprising a sigmoid neural net layer and a pairwise multiplication operation;

training the neural network based upon the training dataset, the training dataset comprising historical usage of the set of workloads, using a regression analysis applying a least-square error function;

monitoring, by the at least one computing device, usage demand for a particular time period, the usage demand associated with the set of workloads within the hyper-converged infrastructure executed on a plurality of host devices;

providing the usage demand to the neural network;

generating a usage prediction based upon the usage demand, the usage prediction comprising a predicted resource consumption for the set of workloads over a time period;

adjusting an allocation of physical resources within the hyper-converged infrastructure for the set of workloads based upon the usage prediction; and

validating the allocation of the physical resources by performing an ongoing validation of the usage prediction relative to an actual usage demand being placed upon the set of workloads by calculating a similarity measure utilizing a dynamic time warping signal processing algorithm, wherein the neural network can be retrained in response to the similarity measure indicating a difference between the actual usage demand and the usage prediction being greater than a threshold.