Abstract: Embodiments train data analytics models by fitting that is distributed computationally and from a data storage point of view to produce an equivalent model to that achieved by sequential fitting. For example, a method may include performing a first pass on an untrained model at a first node, repeatedly transmitting the model to a next node and training the data analytics model at the next node until the data analytics model has been trained by at least a portion of the plurality of processing nodes. There may be a plurality of models that need to be fitted on the dataset and that may be independent or may result from varying and choosing different combinations of model structure, model meta-parameters that are not learned through training, and training algorithm parameters. Embodiments may provide the capability for training multiple models simultaneously by performing the single-model fitting process on different successions of nodes.

Abstract: A computer-implemented method including: (i) receiving a computational task to be processed by the datacenter environment; (ii) receiving a set of computational task parameters, The computational task parameters specifying characteristics of the computational task to be processed; (iii) receiving a set of computing entity parameters, The computing entity parameters specifying characteristics of the computing entities; (iv) transmitting the computational task parameters and the computing entity parameters to a scheduling entity; and (v) selecting one or more computing entities of The plurality of computing entities for processing the computational task by the scheduling entity, based on various factors.

Abstract: A computer-implemented method including: (i) receiving a computational task to be processed by the datacenter environment; (ii) receiving a set of computational task parameters, The computational task parameters specifying characteristics of the computational task to be processed; (iii) receiving a set of computing entity parameters, The computing entity parameters specifying characteristics of the computing entities; (iv) transmitting the computational task parameters and the computing entity parameters to a scheduling entity; and (v) selecting one or more computing entities of The plurality of computing entities for processing the computational task by the scheduling entity, based on various factors.

Abstract: Embodiments relate to creating a coherent load or congestion map that displays the simultaneous activity of all queues of physical and virtual switches and adapters in a network without forcing clock synchronization. An aspect includes sampling, by a central processing device, a status of all queues in a plurality of elements in a network. The sampled data flows are received from the plurality of elements in the network and an image is created of the network. The image includes the status of all sampled queues in the plurality of elements at a point in time. Accordingly, a load map is created without synchronizing clocks of the plurality of elements. The load map is assembled using segments of the image of the network.

Abstract: Embodiments relate to creating a coherent load or congestion map that displays the simultaneous activity of all queues of physical and virtual switches and adapters in a network without forcing clock synchronization. An aspect includes sampling, by a central processing device, a status of all queues in a plurality of elements in a network. The sampled data flows are received from the plurality of elements in the network and an image is created of the network. The image includes the status of all sampled queues in the plurality of elements at a point in time. Accordingly, a load map is created without synchronizing clocks of the plurality of elements. The load map is assembled using segments of the image of the network.