S-D25.5 Simulation Framework resource allocation Y4M05

[KZzizzle]

Dynamic computational resource allocation, scalability

Definition of Done:

• The aws deployment system notices, when resources become too limited and expands the network with the necessary additional machines that fit the demand (GPU, RAM...)
• It also gives back resources that are no longer needed
• When a user or a service (e.g., an iterator service) knows in advance that he will be requiring a certain amount of dedicated resources, those resources can be reserved. (For now, this functionality does not need to be interfaced to normal platform users.)

User story:

• A parallelized series of EM simulation instances using GPU-accelerated FDTD is set up using the sweeper functionality.
• The cluster notices that there are not enough gpu machines available to handle the parallel load and adds more machines.
• Those machines are released again, when the system notices that they are no longer needed

[sanderegg]

Update on sprint Capra delle nevi

Please check #349 for status

[mrnicegyu11]

See related autoscaling cases:

• Autoscaling - Dynamic Services #657
• Autoscaling - Computational Backend #656