Predicting time-to-finish of a workflow using deep neural network with biangular activation functions

A computer program product, comprising a non-transitory machine-readable storage medium having encoded therein executable code of one or more software programs, wherein the one or more software programs when executed by at least one processing device perform the following steps:

• obtaining a specification of at least one executing workflow of a plurality of concurrent workflows in a shared computing environment, wherein the specification comprises a plurality of states of past executions of the at least one executing workflow on at least one processing device;
• obtaining at least one trained deep neural network, wherein said at least one trained deep neural network is trained to predict one or more future states of the at least one executing workflow using the plurality of states of past executions and wherein said at least one trained deep neural network employs a biangular activation function comprising a substantially minimal number of parameters that define a position and a slope associated with two angles of the biangular activation function for a range of input values;
• and
• estimating, using the at least one trained deep neural network, a time-to-finish of the at least one executing workflow of the plurality of concurrent workflows.

An apparatus, comprising:

• a memory;
• and
• at least one processing device, coupled to the memory, operative to implement the following steps:
• obtaining a specification of at least one executing workflow of a plurality of concurrent workflows in a shared computing environment, wherein the specification comprises a plurality of states of past executions of the at least one executing workflow on at least one processing device;
• obtaining at least one trained deep neural network, wherein said at least one trained deep neural network is trained to predict one or more future states of the at least one executing workflow using the plurality of states of past executions and wherein said at least one trained deep neural network employs a biangular activation function comprising a substantially minimal number of parameters that define a position and a slope associated with two angles of the biangular activation function for a range of input values;
• and
• estimating, using the at least one trained deep neural network, a time-to-finish of the at least one executing workflow of the plurality of concurrent workflows.