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

A computer program product, comprising a tangible 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 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) 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.

An apparatus, comprising:

• a memory;
• and
• at least one processing device, coupled to the memory, operative to implement the following steps:
• 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) 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.