Mechanism for machine learning in distributed computing

A non-transitory computer readable medium storing a computer program product in the form of executable instructions for managing distributed computation in a hierarchical system having a compute deployment including a plurality of compute nodes, wherein each node is configured to execute a respective estimation model to obtain a confidence level of an estimation model output for carrying out a compute task, wherein the executable instructions are configured to

• determine a cost function for the system, which cost function includes at least one first parameter associated with power consumption for carrying out said compute task in a first node of said nodes, and at least one second parameter associated with bandwidth utilization for escalating said compute task from the first node to a second node in the hierarchical system;
• employ a machine learning mechanism in the control function to optimize said cost function on one or more overall system cost parameters;
• and
• configure said compute deployment based on the optimization of the cost function by the machine learning mechanism, including adjusting a confidence level threshold to be used by the estimation model in one or more of said nodes.

A hierarchical system comprising

• a compute deployment including a plurality of compute nodes, wherein each node is configured to execute a respective estimation model to obtain a confidence level of an estimation model output for carrying out a compute task, and
• a control function communicatively connected to said compute nodes, wherein said control function comprises a computer program product for managing distributed computation in the hierarchical system, configured to
• determine a cost function for the system, which cost function includes at least one first parameter associated with power consumption for carrying out said compute task in a first node of said nodes, and at least one second parameter associated with power consumption for escalating said compute task from the first node to a second node in the hierarchical system;
• employ a machine learning mechanism in the control function to optimize said cost function on one or more overall system cost parameters;
• and
• configure said compute deployment based on the optimization of the cost function by the machine learning mechanism, including adjusting a confidence level threshold to be used by the estimation model in one or more of said nodes.