Comments (3)
L is the number of executors.
Hongzi: We used a masking mechanism to ensure the executor assignment is always valid. These are the code snippets for computing the mask https://github.com/hongzimao/decima-sim/blob/master/actor_agent.py#L424-L451
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I'm a little confused about the “source_job” variable of the above link code. Do you know what it stands for?
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source_job
is where the executor comes from. If source_job
== curr_job
, it means allocating the executor won't pay a migration cost (simulating how Spark shuts down/launches a JVM, more details see section 3 and 4 for "moving cost" in the paper).
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Related Issues (20)
- some questions of your code HOT 9
- The model training issue with reward function optimizing makespan HOT 14
- Updating Tensorflow 1.14 to 2 HOT 3
- About nodes information HOT 1
- A question about the result HOT 5
- some question about the main idea
- Some questions about Decima's GNN HOT 2
- A question about actor_network HOT 2
- Questions about the input vector HOT 2
- What are the versions of the project pkg requirement? HOT 6
- Question about multi resource training. HOT 1
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- About the model of generation HOT 1
- Question about .npy files
- Some questions about executor
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- Could u plz tell me the piplist of this code? HOT 2
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