Adaptive Controller for Online Adaptation of Resource Allocation Policies for Iterative Workloads Using Reinforcement Learning

摘要

Techniques are provided for adaptive resource allocation for workloads. One method comprises obtaining a dynamic system model based on a relation between an amount of at least one resource for one or more iterative workloads and at least one predefined service metric; obtaining, from a resource allocation correction module, an instantaneous value of the at least one predefined service metric; and applying to a controller: (i) instantaneous parameters of the dynamic system model, and (ii) a difference between the instantaneous value of the at least one predefined service metric and a target value for the at least one predefined service metric, wherein the controller determines an adjustment to the amount of the at least one resource for the one or more iterative workloads. The obtained system model is optionally updated over time based on an amount of at least one resource added and the one or more predefined service metrics.