Optimally scheduling shared computing resources for a variety of tasks with different task-specific resource requests is a difficult problem. Scheduling computational resources is labor intensive, time consuming, error prone, and manual allocation can result in suboptimal use of computing resources. This disclosure describes techniques that train and utilize a machine learning model to automate the task scheduling and resource allocation for the use of shared computing resources. The model is trained on historical records of task requests and corresponding scheduling, allocations, runtime outcomes. From the training data, a score can be computed for each request, indicating the extent to which the corresponding entity makes responsible and fair use of the shared computing resources. For each incoming task request, the trained machine learning model outputs optimal resource allocation for successful completion of the project. The model can also be used for analyzing the overall use of the shared computing resources by an organization to identify global improvements, forecast future resource needs, and allocate newly acquired computing resources.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.