[openstack-dev] [nova] A prototype implementation towards the "shared state scheduler"

Cheng, Yingxin yingxin.cheng at intel.com
Wed Feb 17 05:45:31 UTC 2016

To better illustrate the differences between shared-state, resource-provider and legacy scheduler, I've drew 3 simplified pictures [1] in emphasizing the location of resource view, the location of claim and resource consumption, and the resource update/refresh pattern in three kinds of schedulers. Hoping I'm correct in the "resource-provider scheduler" part.

A point of view from my analysis in comparing three schedulers (before real experiment):
1. Performance: The performance bottlehead of resource-provider and legacy scheduler is from the centralized db and scheduler cache refreshing. It can be alleviated by changing to a stand-alone high performance database. And the cache refreshing is designed to be replaced by to direct SQL queries according to resource-provider scheduler spec [2]. The performance bottlehead of shared-state scheduler may come from the overwhelming update messages, it can also be alleviated by changing to stand-alone distributed message queue and by using the "MessagePipe" to merge messages.
2. Final decision accuracy: I think the accuracy of the final decision are high in all three schedulers, because until now the consistent resource view and the final resource consumption with claims are all in the same place. It's resource trackers in shared-state scheduler and legacy scheduler, and it's the resource-provider db in resource-provider scheduler.
3. Scheduler decision accuracy: IMO the order of accuracy of a single schedule decision is resource-provider > shared-state >> legacy scheduler. The resource-provider scheduler can get the accurate resource view directly from db. Shared-state scheduler is getting the most accurate resource view by constantly collecting updates from resource trackers and by tracking the scheduler claims from schedulers to RTs. Legacy scheduler's decision is the worst because it doesn't track its claims and get resource views from compute nodes records which are not that accurate.
4. Design goal difference:
The fundamental design goal of the two new schedulers is different. Copy my views from [2], I think it is the choice between "the loose distributed consistency with retries" and "the strict centralized consistency with locks".

As can be seen in the illustrations [1], the main compatibility issue between shared-state and resource-provider scheduler is caused by the different location of claim/consumption and the assumed consistent resource view. IMO unless the claims are allowed to happen in both places(resource tracker and resource-provider db), it seems difficult to make shared-state and resource-provider scheduler work together.

[1] https://docs.google.com/document/d/1iNUkmnfEH3bJHDSenmwE4A1Sgm3vnqa6oZJWtTumAkw/edit?usp=sharing
[2] https://review.openstack.org/#/c/271823/


From: Sylvain Bauza [mailto:sbauza at redhat.com]
Sent: Monday, February 15, 2016 9:48 PM
To: OpenStack Development Mailing List (not for usage questions) <openstack-dev at lists.openstack.org>
Subject: Re: [openstack-dev] [nova] A prototype implementation towards the "shared state scheduler"

Le 15/02/2016 10:48, Cheng, Yingxin a écrit :
Thanks Sylvain,

1. The below ideas will be extended to a spec ASAP.

Nice, looking forward to it then :-)

2. Thanks for providing concerns I've not thought it yet, they will be in the spec soon.

3. Let me copy my thoughts from another thread about the integration with resource-provider:
The idea is about "Only compute node knows its own final compute-node resource view" or "The accurate resource view only exists at the place where it is actually consumed." I.e., The incremental updates can only come from the actual "consumption" action, no matter where it is(e.g. compute node, storage service, network service, etc.). Borrow the terms from resource-provider, compute nodes can maintain its accurate version of "compute-node-inventory" cache, and can send incremental updates because it actually consumes compute resources, furthermore, storage service can also maintain an accurate version of "storage-inventory" cache and send incremental updates if it also consumes storage resources. If there are central services in charge of consuming all the resources, the accurate cache and updates must come from them.

That is one of the things I'd like to see in your spec, and how you could interact with the new model.


From: Sylvain Bauza [mailto:sbauza at redhat.com]
Sent: Monday, February 15, 2016 5:28 PM
To: OpenStack Development Mailing List (not for usage questions) <openstack-dev at lists.openstack.org><mailto:openstack-dev at lists.openstack.org>
Subject: Re: [openstack-dev] [nova] A prototype implementation towards the "shared state scheduler"

Le 15/02/2016 06:21, Cheng, Yingxin a écrit :

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?

Such feature requires a spec file to be written http://docs.openstack.org/developer/nova/process.html#how-do-i-get-my-code-merged

Ideally, I'd like to see your below ideas written in that spec file so it would be the best way to discuss on the design.

2. Suggestions to improve its design and compatibility.

I don't want to go into details here (that's rather the goal of the spec for that), but my biggest concerns would be when reviewing the spec :
 - how this can meet the OpenStack mission statement (ie. ubiquitous solution that would be easy to install and massively scalable)
 - how this can be integrated with the existing (filters, weighers) to provide a clean and simple path for operators to upgrade
 - how this can be supporting rolling upgrades (old computes sending updates to new scheduler)
 - how can we test it
 - can we have the feature optional for operators

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.

That's the key point, thanks for noticing our priorities. So, you know that our resource modeling is drastically subject to change in Mitaka and Newton. That is the new game, so I'd love to see how you plan to interact with that.
Ideally, I'd appreciate if Jay Pipes, Chris Dent and you could share your ideas because all of you are having great ideas to improve a current frustrating solution.


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[1], and all the compute node records are retrieved from db every time. There are several problems in this update model, proven in experiments[3]:
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[2]. 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 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[3] compared with caching scheduler and filter scheduler:
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.
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.

[1] https://github.com/openstack/nova/blob/master/nova/scheduler/filter_scheduler.py#L104
[2] https://bugs.launchpad.net/nova/+bug/1341420/comments/24
[3] 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/



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