[openstack-dev] [nova] A prototype implementation towards the "shared state scheduler"
clint at fewbar.com
Wed Feb 17 17:52:52 UTC 2016
Excerpts from Cheng, Yingxin's message of 2016-02-14 21:21:28 -0800:
> I've uploaded a prototype https://review.openstack.org/#/c/280047/ to testify its design goals in accuracy, performance, reliability and compatibility improvements. It will also be an Austin Summit Session if elected: https://www.openstack.org/summit/austin-2016/vote-for-speakers/Presentation/7316
> I want to gather opinions about this idea:
> 1. Is this feature possible to be accepted in the Newton release?
> 2. Suggestions to improve its design and compatibility.
> 3. Possibilities to integrate with resource-provider bp series: I know resource-provider is the major direction of Nova scheduler, and there will be fundamental changes in the future, especially according to the bp https://review.openstack.org/#/c/271823/1/specs/mitaka/approved/resource-providers-scheduler.rst. However, this prototype proposes a much faster and compatible way to make schedule decisions based on scheduler caches. The in-memory decisions are made at the same speed with the caching scheduler, but the caches are kept consistent with compute nodes as quickly as possible without db refreshing.
> Here is the detailed design of the mentioned prototype:
> The host state cache maintained by host manager is the scheduler resource view during schedule decision making. It is updated whenever a request is received, and all the compute node records are retrieved from db every time. There are several problems in this update model, proven in experiments:
> 1. Performance: The scheduler performance is largely affected by db access in retrieving compute node records. The db block time of a single request is 355ms in average in the deployment of 3 compute nodes, compared with only 3ms in in-memory decision-making. Imagine there could be at most 1k nodes, even 10k nodes in the future.
> 2. Race conditions: This is not only a parallel-scheduler problem, but also a problem using only one scheduler. The detailed analysis of one-scheduler-problem is located in bug analysis. In short, there is a gap between the scheduler makes a decision in host state cache and the
> compute node updates its in-db resource record according to that decision in resource tracker. A recent scheduler resource consumption in cache can be lost and overwritten by compute node data because of it, result in cache inconsistency and unexpected retries. In a one-scheduler experiment using 3-node deployment, there are 7 retries out of 31 concurrent schedule requests recorded, results in 22.6% extra performance overhead.
> 3. Parallel scheduler support: The design of filter scheduler leads to an "even worse" performance result using parallel schedulers. In the same experiment with 4 schedulers on separate machines, the average db block time is increased to 697ms per request and there are 16 retries out of 31 schedule requests, namely 51.6% extra overhead.
This mostly agrees with recent tests I've been doing simulating 1000
compute nodes with the fake virt driver. My retry rate is much lower,
because there's less window for race conditions since there is no latency
for the time between nova-compute getting the message that the VM is
scheduled to it, and responding with a host update. Note that your
database latency numbers seem much higher, we see about 200ms, and I
wonder if you are running in a very resource constrained database
> This prototype solved the mentioned issues above by implementing a new update model to scheduler host state cache. Instead of refreshing caches from db, every compute node maintains its accurate version of host state cache updated by the resource tracker, and sends incremental updates directly to schedulers. So the scheduler cache are synchronized to the correct state as soon as possible with the lowest overhead. Also, scheduler will send resource claim with its decision to the target compute node. The compute node can decide whether the resource claim is successful immediately by its local host state cache and send responds back ASAP. With all the claims are tracked from schedulers to compute nodes, no false overwrites will happen, and thus the gaps between scheduler cache and real compute node states are minimized. The benefits are obvious with recorded experiments compared with caching scheduler and filter scheduler:
You don't mention this, but I'm assuming this is true: At startup of a
new shared state scheduler, it fills its host state cache from the
> 1. There is no db block time during scheduler decision making, the average decision time per request is about 3ms in both single and multiple scheduler scenarios, which is equal to the in-memory decision time of filter scheduler and caching scheduler.
> 2. Since the scheduler claims are tracked and the "false overwrite" is eliminated, there should be 0 retries in one-scheduler deployment, as proven in the experiment. Thanks to the quick claim responding implementation, there are only 2 retries out of 31 requests in the 4-scheduler experiment.
This is a real win. I've seen 3 schedulers get so overwhelmed with
retries that they go slower than 1.
> 3. All the filtering and weighing algorithms are compatible because the data structure of HostState is unchanged. In fact, this prototype even supports filter scheduler running at the same time(already tested). Like other operations with resource changes such as migration, resizing or shelving, they make claims in the resource tracker directly and update the compute node host state immediately without major changes.
> Extra features:
> More efforts are made to better adjust the implementation to real-world scenarios, such as network issues, service unexpectedly down and overwhelming messages etc:
> 1. The communication between schedulers and compute nodes are only casts, there are no RPC calls thus no blocks during scheduling.
> 2. All updates from nodes to schedulers are labelled with an incremental seed, so any message reordering, lost or duplication due to network issues can be detected by MessageWindow immediately. The inconsistent cache can be detected and refreshed correctly.
> 3. The overwhelming messages are compressed by MessagePipe in its async mode. There is no need to send all the messages one by one in the MQ, they can be merged before sent to schedulers.
> 4. When a new service is up or recovered, it sends notifications to all known remotes for quick cache synchronization, even before the service record is available in db. And if a remote service is unexpectedly down according to service group records, no more messages will send to it. The ComputeFilter is also removed because of this feature, the scheduler can detect remote compute nodes by itself.
> 5. In fact the claim tracking is not only from schedulers to compute nodes, but also from compute-node host state to the resource tracker. One reason is that there is still a gap between a claim is acknowledged by compute-node host state and the claim is successful in resource tracker. It is necessary to track those unhandled claims to keep host state accurate. The second reason is to separate schedulers from compute node and resource trackers. Scheduler only export limited interfaces `update_from_compute` and `handle_rt_claim_failure` to compute service and the RT, so the testing and reusing are easier with clear boundaries.
> There are still many features to be implemented, the most important are unit tests and incremental updates to PCI and NUMA resources, all of them are marked out inline.
>  https://github.com/openstack/nova/blob/master/nova/scheduler/filter_scheduler.py#L104
>  https://bugs.launchpad.net/nova/+bug/1341420/comments/24
>  http://paste.openstack.org/show/486929/
> The original commit history of this prototype is located in https://github.com/cyx1231st/nova/commits/shared-scheduler
> For instructions to install and test this prototype, please refer to the commit message of https://review.openstack.org/#/c/280047/
Thanks Yingxin. I'm very excited to try out your scheduler, as I think
it may solve some scale blocks we're experiencing as we ramp up.
More information about the OpenStack-dev