Cloud computing has transformed the landscape of compute operations. Of course, there are still many labs/businesses with local, large-scale compute clusters. For those businesses who keep the hardware, it would be beneficial to the users to have a tool for deciding whether or not a job should be ran locally versus on the cloud. Two important factors would be how much time the user can afford to wait and the funds available to the user.
If we were only optimizing for cost, of course, we'd send every job to the local resources. It gets interesting when you optimize for both cost and time, considering there's a finite amount of machines locally (other users' jobs creates a queue) and the cloud can provide better/faster hardware.
Ultimately, say you have some amount of money and a time frame. After providing this information and a list of jobs (each with varying CPU nodes requested, memory required, estimated job time, etc.), this tool should be able to make a near-optimal decision about which jobs to run locally and which to run on the cloud.
Considering the optimization nature of this problem, Google's OR-Tools library looks great to start with. They provide guides (check left side bar there) to several "problem types". I'm having trouble deciding which category this problem falls into.
For those unable to visit the OR-Tools link, the problem types are:
- Linear Optimization
- Integer Optimization
- Constraint Optimization
- Bin Packing
- Network Flows
What problem type category does this best fall into, and what are some ideas on how to formulate said problem?