Synnefo » snf-ganeti » ganeti-local

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 Synchronising htools to Ganeti 2.3

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Ganeti 2.3 introduces a number of new features that change the cluster

internals significantly enough that the htools suite needs to be

updated accordingly in order to function correctly.

Shared storage support

======================

Currently, the htools algorithms presume a model where all of an

instance's resources is served from within the cluster, more

specifically from the nodes comprising the cluster. While is this

usual for memory and CPU, deployments which use shared storage will

invalidate this assumption for storage.

To account for this, we need to move some assumptions from being

implicit (and hardcoded) to being explicitly exported from Ganeti.

New instance parameters

-----------------------

It is presumed that Ganeti will export for all instances a new

``storage_type`` parameter, that will denote either internal storage

(e.g. *plain* or *drbd*), or external storage.

Furthermore, a new ``storage_pool`` parameter will classify, for both

internal and external storage, the pool out of which the storage is

allocated. For internal storage, this will be either ``lvm`` (the pool

that provides space to both ``plain`` and ``drbd`` instances) or

``file`` (for file-storage-based instances). For external storage,

this will be the respective NAS/SAN/cloud storage that backs up the

instance. Note that for htools, external storage pools are opaque; we

only care that they have an identifier, so that we can distinguish

between two different pools.

If these two parameters are not present, the instances will be

presumed to be ``internal/lvm``.

New node parameters

-------------------

For each node, it is expected that Ganeti will export what storage

types it supports and pools it has access to. So a classic 2.2 cluster

will have all nodes supporting ``internal/lvm`` and/or

``internal/file``, whereas a new shared storage only 2.3 cluster could

have ``external/my-nas`` storage.

Whatever the mechanism that Ganeti will use internally to configure

the associations between nodes and storage pools, we consider that

we'll have available two node attributes inside htools: the list of internal

and external storage pools.

External storage and instances

------------------------------

Currently, for an instance we allow one cheap move type: failover to

the current secondary, if it is a healthy node, and four other

“expensive” (as in, including data copies) moves that involve changing

either the secondary or the primary node or both.

In presence of an external storage type, the following things will

change:

- the disk-based moves will be disallowed; this is already a feature

  in the algorithm, controlled by a boolean switch, so adapting

  external storage here will be trivial

- instead of the current one secondary node, the secondaries will

  become a list of potential secondaries, based on access to the

  instance's storage pool

Except for this, the basic move algorithm remains unchanged.

External storage and nodes

--------------------------

Two separate areas will have to change for nodes and external storage.

First, then allocating instances (either as part of a move or a new

allocation), if the instance is using external storage, then the

internal disk metrics should be ignored (for both the primary and

secondary cases).

Second, the per-node metrics used in the cluster scoring must take

into account that nodes might not have internal storage at all, and

handle this as a well-balanced case (score 0).

N+1 status

----------

Currently, computing the N+1 status of a node is simple:

- group the current secondary instances by their primary node, and

  compute the sum of each instance group memory

- choose the maximum sum, and check if it's smaller than the current

  available memory on this node

In effect, computing the N+1 status is a per-node matter. However,

with shared storage, we don't have secondary nodes, just potential

secondaries. Thus computing the N+1 status will be a cluster-level

matter, and much more expensive.

A simple version of the N+1 checks would be that for each instance

having said node as primary, we have enough memory in the cluster for

relocation. This means we would actually need to run allocation

checks, and update the cluster status from within allocation on one

node, while being careful that we don't recursively check N+1 status

during this relocation, which is too expensive.

However, the shared storage model has some properties that changes the

rules of the computation. Speaking broadly (and ignoring hard

restrictions like tag based exclusion and CPU limits), the exact

location of an instance in the cluster doesn't matter as long as

memory is available. This results in two changes:

- simply tracking the amount of free memory buckets is enough,

  cluster-wide

- moving an instance from one node to another would not change the N+1

  status of any node, and only allocation needs to deal with N+1

  checks

Unfortunately, this very cheap solution fails in case of any other

exclusion or prevention factors.

TODO: find a solution for N+1 checks.

Node groups support

===================

The addition of node groups has a small impact on the actual

algorithms, which will simply operate at node group level instead of

cluster level, but it requires the addition of new algorithms for

inter-node group operations.

The following two definitions will be used in the following

paragraphs:

local group

  The local group refers to a node's own node group, or when speaking

  about an instance, the node group of its primary node

regular cluster

  A cluster composed of a single node group, or pre-2.3 cluster

super cluster

  This term refers to a cluster which comprises multiple node groups,

  as opposed to a 2.2 and earlier cluster with a single node group

In all the below operations, it's assumed that Ganeti can gather the

entire super cluster state cheaply.

Balancing changes

-----------------

Balancing will move from cluster-level balancing to group

balancing. In order to achieve a reasonable improvement in a super

cluster, without needing to keep state of what groups have been

already balanced previously, the balancing algorithm will run as

follows:

#. the cluster data is gathered

#. if this is a regular cluster, as opposed to a super cluster,

   balancing will proceed normally as previously

#. otherwise, compute the cluster scores for all groups

#. choose the group with the worst score and see if we can improve it;

   if not choose the next-worst group, so on

#. once a group has been identified, run the balancing for it

Of course, explicit selection of a group will be allowed.

Super cluster operations

++++++++++++++++++++++++

Beside the regular group balancing, in a super cluster we have more

operations.

Redistribution

^^^^^^^^^^^^^^

In a regular cluster, once we run out of resources (offline nodes

which can't be fully evacuated, N+1 failures, etc.) there is nothing

we can do unless nodes are added or instances are removed.

In a super cluster however, there might be resources available in

another group, so there is the possibility of relocating instances

between groups to re-establish N+1 success within each group.

One difficulty in the presence of both super clusters and shared

storage is that the move paths of instances are quite complicated;

basically an instance can move inside its local group, and to any

other groups which have access to the same storage type and storage

pool pair. In effect, the super cluster is composed of multiple

‘partitions’, each containing one or more groups, but a node is

simultaneously present in multiple partitions, one for each storage

type and storage pool it supports. As such, the interactions between

the individual partitions are too complex for non-trivial clusters to

assume we can compute a perfect solution: we might need to move some

instances using shared storage pool ‘A’ in order to clear some more

memory to accept an instance using local storage, which will further

clear more VCPUs in a third partition, etc. As such, we'll limit

ourselves at simple relocation steps within a single partition.

Algorithm:

#. read super cluster data, and exit if cluster doesn't allow

   inter-group moves

#. filter out any groups that are “alone” in their partition

   (i.e. no other group sharing at least one storage method)

#. determine list of healthy versus unhealthy groups:

    #. a group which contains offline nodes still hosting instances is

       definitely not healthy

    #. a group which has nodes failing N+1 is ‘weakly’ unhealthy

#. if either list is empty, exit (no work to do, or no way to fix problems)

#. for each unhealthy group:

    #. compute the instances that are causing the problems: all

       instances living on offline nodes, all instances living as

       secondary on N+1 failing nodes, all instances living as primaries

       on N+1 failing nodes (in this order)

    #. remove instances, one by one, until the source group is healthy

       again

    #. try to run a standard allocation procedure for each instance on

       all potential groups in its partition

    #. if all instances were relocated successfully, it means we have a

       solution for repairing the original group

Compression

^^^^^^^^^^^

In a super cluster which has had many instance reclamations, it is

possible that while none of the groups is empty, overall there is

enough empty capacity that an entire group could be removed.

The algorithm for “compressing” the super cluster is as follows:

#. read super cluster data

#. compute total *(memory, disk, cpu)*, and free *(memory, disk, cpu)*

   for the super-cluster

#. computer per-group used and free *(memory, disk, cpu)*

#. select candidate groups for evacuation:

    #. they must be connected to other groups via a common storage type

       and pool

    #. they must have fewer used resources than the global free

       resources (minus their own free resources)

#. for each of these groups, try to relocate all its instances to

   connected peer groups

#. report the list of groups that could be evacuated, or if instructed

   so, perform the evacuation of the group with the largest free

   resources (i.e. in order to reclaim the most capacity)

Load balancing

^^^^^^^^^^^^^^

Assuming a super cluster using shared storage, where instance failover

is cheap, it should be possible to do a load-based balancing across

groups.

As opposed to the normal balancing, where we want to balance on all

node attributes, here we should look only at the load attributes; in

other words, compare the available (total) node capacity with the

(total) load generated by instances in a given group, and computing

such scores for all groups, trying to see if we have any outliers.

Once a reliable load-weighting method for groups exists, we can apply

a modified version of the cluster scoring method to score not

imbalances across nodes, but imbalances across groups which result in

a super cluster load-related score.

Allocation changes

------------------

It is important to keep the allocation method across groups internal

(in the Ganeti/Iallocator combination), instead of delegating it to an

external party (e.g. a RAPI client). For this, the IAllocator protocol

should be extended to provide proper group support.

For htools, the new algorithm will work as follows:

#. read/receive cluster data from Ganeti

#. filter out any groups that do not supports the requested storage

   method

#. for remaining groups, try allocation and compute scores after

   allocation

#. sort valid allocation solutions accordingly and return the entire

   list to Ganeti

The rationale for returning the entire group list, and not only the

best choice, is that we anyway have the list, and Ganeti might have

other criteria (e.g. the best group might be busy/locked down, etc.)

so even if from the point of view of resources it is the best choice,

it might not be the overall best one.

Node evacuation changes

-----------------------

While the basic concept in the ``multi-evac`` iallocator

mode remains unchanged (it's a simple local group issue), when failing

to evacuate and running in a super cluster, we could have resources

available elsewhere in the cluster for evacuation.

The algorithm for computing this will be the same as the one for super

cluster compression and redistribution, except that the list of

instances is fixed to the ones living on the nodes to-be-evacuated.

If the inter-group relocation is successful, the result to Ganeti will

not be a local group evacuation target, but instead (for each

instance) a pair *(remote group, nodes)*. Ganeti itself will have to

decide (based on user input) whether to continue with inter-group

evacuation or not.

In case that Ganeti doesn't provide complete cluster data, just the

local group, the inter-group relocation won't be attempted.

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