Overview
The paper explored the feasibility for ML techniques to support resource management algorithms. The main challenges are firstly discussed, then serveral potential remedies are proposed for these challenges, finally a proof-of-concept level experiment is conducted which demonstrates the feasibility.
Adv.
- The paper is well orgnized and the writing is good, especially, the figures and tables in the paper is well designed, which makes me enjoy the reading.
- The discussion of the feasibility by IL (imitation learning) and RL (Reinforcement learning) is impressive and the disadvantages of these methods hit the point (in my opinion)
- The usage of domain knowledge is well surveyed, the challenges and corresponding proposed remedies are inspiring and the tricks for solving realistic constraints can be also used in other projects.
Drawbacks
- Though RL is hard to formulate a reward function, RL is still can be adopted in this direction and deserves a deeper discussion in the future.
- Fig 5 is not neccesary since the result table does not belong to this work.
Writing mistakes
- page1. reached reached -> reached
- page4. inference time, performance and energy -> inference time, power and energy (It I understand it correctly)
- page6. The second challenge are model -> The second challenge is model