1) Is the problem real?
To achieve fair sharing using Max-Min fair sharing on one resource is simple and efficient. However, when it comes to about heterogeneous resources demands, the max-min fair sharing cannot achieve good performance. The paper wants to achieve fairness among multiple resources when users have heterogeneous demands. The problem is real and needs to be solved.
2) What is the solution’s main idea (nugget)?
The DRF (Dominant Resource Faireness) introduces dominant sharing concept as the metric and using max-min fair sharing on this dominant sharing value. Dominant sharing is the amount of dominant resource one user needs.
The DRF tries to equalize the dominant share of users.
For online scheduler, DRF will schedule a task that has the smallest dominant share.
3) Why is solution different from previous work?
Previous works focusing on multi-resouces sharing only considering about the multiple instance of interchangeable resources, whereas DRF focus on allocation of resources of different types.
And many other framework using Max-Min fair sharing may only considering about one resources allocation. On the contrary, DRF considers about multiple resources fair sharing.
4) Does the paper (or do you) identify any fundamental/hard trade-offs?
The paper is based on the assumption that users want to achieve overall fairness. However, as shown in the evaluation part, by achieving this dominant fair sharing, a large amount of CPU/memory recourses are wasted. Some portions of CPU/Mem are idle during the tasks processing by each user. I think balancing both fairness sharing and computing resource utilization will be a better solution.
5) Do you think the paper will be influential in 10 years? Why or why not?
I do not think so. Since by achieving fairness, many resources are idle and wasted. DRF might be good for some specific scenarios that fairness sharing is necessary. I think balancing both fairness sharing and computing resource utilization will be a better solution.
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