Parallel Filter Scheduler¶
This spec was abandoned by efried on account of having been sitting in the backlog directory since 2015.
This backlog spec discusses the issues around parallelism and the current Filter Scheduler in Nova. This is particularly interesting for when migrating existing cells v1 users to cells v2.
We need the nova filter scheduler to work well in typical public cloud even after they have migrated from cells v1 to cells v2.
Some key observations about the current nova-scheduler:
If you running two nova-scheduler processes they race each other, they don’t find out about each others choices until the DB gets updated by the nova-compute resource tracker. This has lead to many deployments opting for an Active/Passive HA setup for the nova-scheduler process.
The resource tracker has the final say on if an instance can fit. If the request ends up on a full compute node, the build errors out, and lets the retry system find an different compute node to try. However, we stop retrying after three attempts, and this extends build times for users, so its best to avoid these retries.
Deployers often chose to fill first, to ensure they keep room for larger flavors they offer. They then use the IO-ops filter to ensure you don’t have too many builds happening on the same node at any time. Adding these together, this makes the above races much worse.
Randomization of decisions has been added to reduce the impact of the races, but this means its making ‘worse’ decisions.
Querying the DB is the most expensive part of the scheduling process.
The C based DB driver and eventlet means the scheduler performs best when the eventlet thread pool is very small, ideally less than 5. Without that, you find it makes several DB calls before processing the (now stale) information it has fetched from the DB.
The Caching Scheduler was added to periodically update the host state in a periodic task, so no user request has to wait for the DB query. It uses consume_from_instance to update the cached state for future requests. Note its not until the next poll period that the data will be refreshed to include information about any delete requests. The state is also local to each scheduler process.
Until the end of kilo, many filters and weights also made DB queries to fetch information such as host aggregates. The work help isolate the scheduler from the rest of nova has removed all those extra DB calls, so it is now only periodically fetching the state from the DB.
Cells v1 shards the system so there is a single nova-scheduler per cell. Each cell typically has several hundred hosts. When a build request comes in, cells v1 first picks a cell, then inside that cell, the regular nova-scheduler picks one of the compute nodes in that cell. The API cell is given a list of slots from the nova-cells process in each of the child cells. The child cell’s nova-cells process periodically looks at the state of all the hosts, alongside the current set of flavors, and reports the number of slots each cell has available. The slots are based on memory and disk, and not actually per flavor. This system has several limitations, but the key ones for this discussion are:
Once in a cell, the build retry attempts only happen between compute nodes within that cell. If you fail to build in a cell, there is no way to try another cell. If a cell gets full, there is no way to move a VM to another cell if it needs to be resized up.
The reported slots have hidden dependencies. If you have space for a 2 GB VM, the system also reports two slots for a 1 GB VM. There is no way to express that those three slots are related. If the scheduler chooses to use the 2GB slots, when the next request uses one of the 1GB slots, when it reaches that cell it will discover the capacity was already used the by previous request.
The current cells scheduler doesn’t update its in memory state between scheduling decisions, and has no randomization. Consider two cells, one with 12x1GB slots another with 10x1GB slots. If you get 15 requests for a 1GB slot, they all get sent to the cell reporting 12 slots. 2 of those build request will fail because that cell has no room. There are plans to randomly distribute builds between those slots, but that just limits the impact of this problem, rather than eliminating it.
This is a described in the Google omega paper as “two level scheduling”
So in summary, the current nova-scheduler works best when:
there is a single nova-scheduler process running
it periodically refreshes its state from the DB
it updates its in memory state with any previous decisions it has made
it makes one decision at once, zero parallelism
This is the backdrop in which we need to look at new ways to scale out the nova-scheduler, so we are able to scale to a level where existing cells v1 user are able to move to cells v2 where a single nova-scheduler deployment has to deal with the current load 15-20 nova-schedulers are dealing with today.
Consider a deployment where you have more than 10k hypervisors, with build request bursts of at least 1k within a 15 min period.
This is particularly relevant to cells v1 users that are going to be migrate to cells v2.
This spec is to agree the problem, and list some possible solutions.
Multiple Scheduler Future¶
Nova has two schedulers, the random scheduler and the filter scheduler. In the future, its increasingly likely there will be multiple schedulers that work well for particular use cases and usage patterns.
With that in mind, I am going to focus on replacing the typical public cloud use cases as described above.
Moving away from filters and weights¶
An alternative to the existing scheduler is to have nova-compute nodes pull build requests from a shared queue, rather than being pushed work from a central scheduler.
This works well for spread first, but requires some careful co-ordination to make a fill first scheduler work. You probably need a central system to give a new compute node permission to pull from the queue, or something like that.
If you pull a message from the queue, and discover you are unable to service that request you could put that message back on the queue. Ideally you would shard the number of queues to limit the cases where such retries are needed. Although, its hard to do that with per tenant affinity and anit-affinity rules.
While I really want someone to explore building a driver like this, this spec is not considering this approach. It will instead focus on a more direct replacement for the current filter scheduler.
Partitioning the Filter Scheduler¶
A great way to reduce the size of a problem, is to split the larger problem into smaller pieces. Lets look at this in more detail.
One major issue is interference between different schedulers. Ideally we don’t want multiple schedulers assigning work to the same nova-compute nodes, as they will be competing with each other for the same resources. Ideally each scheduler would be looking a different subset of hosts.
Fighting this requirement are cluster wide behaviors, such as affinity and anti-affinity rules, where ideally we need to know the full state of the system, rather than just looking at a subset of the system.
Its possible to have a dynamic partitioning, but for simplicity, I am going to focus on static partitions of the system. The problem with a static partition is that they tend to have capacity planning implications. If a subset of all requests get routed to a particular set of hosts, then you need to ensure you increase the number of hosts to match the demand for that subset of hosts.
In cells v1, the top level scheduling was used to try and spread the load between lots of groups that get added as you expand, but this two level scheduling caused lots of other races of its own.
With these ideas at the back of my mind there is an interesting use case we can consider:
Certain groups of hosts can have specific hardware mapped to specific flavors. i.e. SSD vs non-SSD local storage vs all storage form cinder (no local disk)
Keeping Windows and Linux VMs on different sets of hypervisors is common place, to allow for the best utilization of bulk license savings. This is a very similar distinct split between hosts based on the users build request.
Lets consider having a separate nova-scheduler cluster for each of these groups of hosts. We can route requests to each scheduler cluster based on the request spec. The flavor is required in all build requests, and can route you to one of each subset. Requests for global concepts such as affinity don’t really make sense across these groups of hosts, and its possible that the request router could check these kinds of constraints.
In a cells v2 world, you would have multiple cells in each group. For simplicity we can assume each complete cell would be registered to one (and only one) of the scheduler clusters. In practice, we probably want each host to know what scheduler it should report things two.
The nice property of this partition is that you need to do capacity planning for each of these groups of hosts independently, regardless of how the scheduling is implemented.
There are many other possible partitions, but this seems one of the simplest and well help many of the large cloud users moving from cells v1 to cells v2. Lets consider another partition, such as using hash of a tenant to choose between some distinct subset of hosts. Here you need to have a very large number of tenants and/or even usage across your tenants, otherwise you end up having to expand capacity differently across each of the groups as the demand from those different tenants goes up and down. When each of those schedulers look at overlapping subsets of the nodes, you improve the spread of resources, but you tent to end up with some interference between the different scheduler clusters.
While some of these alternative partitioning schemes may well be useful once we have some of the other enhancements discussed here, I am limiting the scope of this spec to the simplest partitioning scheme, a distinct partitioning of hosts based on the requested flavor, for the initial version. The major downside of this approach is it limits the impact of partitioning to the very largest cloud deployments, those where there are several distinct groups of hosts that have their capacity managed separately.
Using the Resource Tracker to implement “distributed” locking¶
There have been various discussions about having the resource tracker persist the resource claims it hands out, so those claims persist across a nova-compute service restart. On top of that, we can add some RPC calls so the nova-conductor, or any other node, would be able to acquire one of these claims during VM move operations, such as resize and live-migrate, where you don’t want new VM builds taking up space you are about to use once you have move the VM. It was also discussed that these claims should expire after an amount of time if the claim is not used. This should protect against failure modes where you get a leak of capacity due to un-used resource tracker claims. This moves what could be a distributed locking mechanism to a per nova-compute locking system, that should mean there is much less lock contention, and generally its a much easier problem to solve.
When the resource tracker reports its current available resources up to the scheduler it would reduce the amount of free resources to take account of the current claims on its resources.
Now consider if the scheduler was able to acquire one of these claims before returning the chosen host to the nova-conductor. This would be moving the claim request from the very start of the build process in nova-compute into the scheduler. This would allow the scheduler to build up a collection of claims for the requested resources before returning the choice to the caller what resources the scheduler has chosen. Should there be a problem detected, the scheduler can perform retries until it gets all the claims required for the given resource request made to the scheduler.
Putting this all together, you now see that the schedulers will start to see each others decisions because the claims acquired by another scheduler show up more quickly in the shared state.
Taking this a step further we could ensure that a scheduler waits for the claim it just took to show up in the shared state before returning the compute node choice to the scheduler’s caller.
Another possible twist is to consider a claim system very similar to the “compare and swap” DB call system. When the scheduler makes a claim, it could tell the compute node only to give out that claim if the compute node still has the same free resources and the scheduler currently things it has. If the scheduler has a different view of the resources, it is should update its internal state to see if this is still the best node to send the request. It could be done by having a hash of the currently reported node state, and comparing that. Its assumed such a hash would not change when an instance goes from the claimed state to a state where it is using that claim.
It seems likely that a combination of these strategies should help ensure the scheduler is able to deal with most races between other parallel schedulers before returning the chosen compute node to the scheduler caller. This should reduce the cost of any scheduler races that may still occur.
Moving from querying the DB state to consuming a stream of updates¶
As mentioned above, the most expensive part of the scheduling process is not running through the list of filters and weights, it is getting updating the current host state from the database.
We currently use the Caching Scheduler to reduce the cost of these DB calls, but using stale data that gets updated in memory to reduce the impact of it being stale. And interesting alternative is to just consume the updates to the current state, rather than having to fetch the full copy of the host state every time: https://blueprints.launchpad.net/nova/+spec/no-db-scheduler
This is very similar to a shared state scheduler discussed in the omega paper. In this case the shared state is implemented using an in memory structure in each of the schedulers, with a stream of updates that are required being fed to all of the consumers.
Should you need to re-start a nova-scheduler process, or start an additional nova-scheduler process, they would need to go back to the “start” and consume all the updates, so its state is in-sync with all the other schedulers, before starting to service any requests. Making sure all computes report their full state occasionally means there is a point where you can trim the old updates and still get a full view of the the full system.
The pain point of friction with the no-db-scheduler was the complexity of maintaining the code that look a lot like the implementation of a DB log. Being able to efficiently trim old updates, so any new schedulers have only have a small amount of data to catch up. It turns our Kafka has already implemented at lot of these semantics and is has already been proven to work at an extremely large scale: http://kafka.apache.org/documentation.html#introduction
It seems we should be able to create a kafka based system to get efficient incremental updates to the current state of the system, rather than having to make the expensive DB call to get the state for all the hosts we are interested in.
There have been worries about the assumption we can store in memory a list of all the hosts in the system, and their current state.
It seems that, in practice, this will be the least of our worries when it comes to finding what limits the level of scale this solution can reach.
Data model impact¶
REST API impact¶
Other end user impact¶
Other deployer impact¶
Any solution will need a way to live upgrade from the existing scheduler.
Primary assignee: None
Other contributors: None
The existing tempest tests will be able to ensure the scheduler works as a drop in replacement for the old scheduler.
The grenade tests (or a similar test) should be enhanced to test the migration between the existing scheduler and this new scheduler.
It would be good to investigate some functional tests to stress test the scheduler system, so we can simulate the race conditions that are being seen in certain production scenarios, and prove is the new system improves things.
Google omega paper: http://research.google.com/pubs/pub41684.html