1) Is the problem real?
The problem is that currently no single framework (e.g. Hadoop, Dryad ) can achieve decent performance for any applications. Once the problem is solved, the big data systems can have the potential of achieving optimal performance.
2) What is the solution’s main idea (nugget)?
1) resource offer: that is to delegate control over scheduling to the frameworks themselves.
2) fine-grained resource sharing model: consider about fairness, data locality, to achieve task-level resource sharing.
3) Why is solution different from previous work?
Previous works like HPC schedulers (e.g. Torque, LSF, Sun Grid Engine), virtual machine clouds, they achieve coarse-grained sharing for inelastic jobs. On the other hand, Mesos achieves fine-grained task-level resource sharing.
Condor cannot enable some cloud policy such as delay scheduling, whereas Mesos can achieve.
Next generation Hadoop uses a centralized scheduling based on locality reference, whereas Mesos is a decentralized resource sharing model.
4) Does the paper (or do you) identify any fundamental/hard trade-offs?
One of the key component of Mesos is its resource offer. The basic idea of resource offer is to offer available resources and let the frameworks decide which resources to use and which tasks to launch.
It keeps Mesos simple and can be used for future frameworks. However, this decentralized decisions may not be optimal.
5) Do you think the paper will be influential in 10 years? Why or why not?
Yes I think Mesos will be influential in 10 years. Since it can support future frameworks to be integrated into current data processing systems.
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