Synnefo » snf-ganeti » ganeti-local

Cluster tools (h-aneti?)

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

These are some simple cluster tools for fixing common problems. Right now N+1

and rebalancing are included.

.. contents::

Cluster N+1 solver

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

This program runs a very simple brute force algorithm over the instance

placement space in order to determine the shortest number of replace-disks

needed to fix the cluster. Note this means we won't get a balanced cluster,

just one that passes N+1 checks.

Also note that the set of all instance placements on a 20/80 cluster is

(20*19)^80, that is ~10^200, so...

Algorithm

+++++++++

The algorithm is a simple two-phase process.

In phase 1 we determine the removal set, that is the set of instances that when

removed completely from the cluster, make it healthy again. The instances that

can go into the set are all the primary and secondary instances of the failing

nodes. The result from this phase is actually a list - we compute all sets of

the same minimum length.

So basically we aim to determine here: what is the minimum number of instances

that need to be removed (this is called the removal depth) and which are the

actual combinations that fit (called the list of removal sets).

In phase 2, for each removal set computed in the previous phase, we take the

removed instances and try to determine where we can put them so that the

cluster is still passing N+1 checks. From this list of possible solutions

(called the list of solutions), we compute the one that has the smallest delta

from the original state (the delta is the number of replace disks that needs to

be run) and chose this as the final solution.

Implementation

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

Of course, a naive implementation based on the above description will run for

long periods of time, so the implementation has to be smart in order to prune

the solution space as eagerly as possible.

In the following, we use as example a set of test data (a cluster with 20

nodes, 80 instances that has 5 nodes failing N+1 checks for a total of 12

warnings).

On this set, the minimum depth is 4 (anything below fails), and for this depth

the current version of the algorithm generates 5 removal sets; a previous

version of the first phase generated a slightly different set of instances, with

two removal sets. For the original version of the algorithm:

- the first, non-optimized implementation computed a solution of delta=4 in 30

  minutes on server-class CPUs and was still running when aborted 10 minutes

  later

- the intermediate optimized version computed the whole solution space and

  found a delta=3 solution in around 10 seconds on a laptop-class CPU (total

  number of solutions ~600k)

- latest version on server CPUs (which actually computes more removal sets)

  computes depth=4 in less than a second and depth=5 in around 2 seconds, and

  depth=6 in less than 20 seconds; depth=8 takes under five minutes (this is

  10^10 bigger solution space)

Note that when (artificially) increasing the depth to 5 the number of removal

sets grows fast (~3000) and a (again artificial) depth 6 generates 61k removal

sets. Therefore, it is possible to restrict the number of solution sets

examined via a command-line option.

The factors that influence the run time are:

- the removal depth; for each increase with one of the depth, we grow the

  solution space by the number of nodes squared (since a new instance can live

  any two nodes as primary/secondary, therefore (almost) N times N); i.e.,

  depth=1 will create a N^2 solution space, depth two will make this N^4,

  depth three will be N^6, etc.

- the removal depth again; for each increase in the depth, there will be more

  valid removal sets, and the space of solutions increases linearly with the

  number of removal sets

Therefore, the smaller the depth the faster the algorithm will be; it doesn't

seem like this algorithm will work for clusters of 100 nodes and many many

small instances (e.g. 256MB instances on 16GB nodes).

Currently applied optimizations:

- when choosing where to place an instance in phase two, there are N*(N-1)

  possible primary/secondary options; however, if instead of iterating over all

  p * s pairs, we first determine the set of primary nodes that can hold this

  instance (without failing N+1), we can cut (N-1) secondary placements for

  each primary node removed; and since this applies at every iteration of phase

  2 it linearly decreases the solution space, and on full clusters, this can

  mean a four-five times reductions of solution space

- since the number of solutions is very high even for smaller depths (on the

  test data, depth=4 results in 1.8M solutions) we can't compare them at the

  end, so at each iteration in phase 2 we only promote the best solution out of

  our own set of solutions

- since the placement of instances can only increase the delta of the solution

  (placing a new instance will add zero or more replace-disks steps), it means

  the delta will only increase while recursing during phase 2; therefore, if we

  know at one point that we have a current delta that is equal or higher to the

  delta of the best solution so far, we can abort the recursion; this cuts a

  tremendous number of branches; further promotion of the best solution from

  one removal set to another can cut entire removal sets after a few recursions

Command line usage

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

Synopsis::

    hn1 { [-n NODES_FILE] [-i INSTANCES_FILE] | [-m CLUSTER] } \

        [-d START_DEPTH] \

        [-r MAX_REMOVALS] [-l MIN_DELTA] [-L MAX_DELTA] \

        [-p] [-C]

The -n and -i options change the names of the input files.

Alternatively, the -m option specifies collection of data via RAPI.

The -d option

changes the start depth, as a higher depth can give (with a longer computation

time) a solution with better delta. The -r option restricts at each depth the

number of solutions considered - with r=1000 for example even depth=10 finishes

in less than a second.

The -p option will show the cluster state after the solution is implemented,

while the -C option will show the needed gnt-instance commands to implement

it.

The -l (--min-delta) and -L (--max-delta) options restrict the solution in the

following ways:

- min-delta will cause the search to abort early once we find a solution with

  delta less than or equal to this parameter; this can cause extremely fast

  results in case a desired solution is found quickly; the default value for

  this parameter is zero, so once we find a "perfect" solution we finish early

- max-delta causes rejection of valid solution but which have delta higher

  than the value of this parameter; this can reduce the depth of the search

  tree, with sometimes significant speedups; by default, this optimization is

  not used

Individually or combined, these two parameters can (if there are any) very

fast result; on our test data, depth=34 (max depth!) is solved in 2 seconds

with min-delta=0/max-delta=1 (since there is such a solution), and the

extremely low max-delta causes extreme pruning.

Cluster rebalancer

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

Compared to the N+1 solver, the rebalancer uses a very simple algorithm:

repeatedly try to move each instance one step, so that the cluster score

becomes better. We stop when no further move can improve the score.

The algorithm is divided into rounds (all identical):

#. Repeat for each instance:

    #. Compute score after the potential failover of the instance

    #. For each node that is different from the current primary/secondary

        #. Compute score after replacing the primary with this new node

        #. Compute score after replacing the secondary with this new node

    #. Out of this N*2+1 possible new scores (and their associated move) for

       this instance, we choose the one that is the best in terms of cluster

       score, and then proceed to the next instance

Since we don't compute all combinations of moves for instances (e.g. the first

instance's all moves Cartesian product with second instance's all moves, etc.)

but we proceed serially instance A, then B, then C, the total computations we

make in one steps is simply N(number of nodes)*2+1 times I(number of instances),

instead of (N*2+1)^I. So therefore the runtime for a round is trivial.

Further rounds are done, since the relocation of instances might offer better

places for instances which we didn't move, or simply didn't move to the best

place. It is possible to limit the rounds, but usually the algorithm finishes

after a few rounds by itself.

Note that the cluster *must* be N+1 compliant before this algorithm is run, and

will stay at each move N+1 compliant. Therefore, the final cluster will be N+1

compliant.

Single-round solutions

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

Single-round solutions have the very nice property that they are

incrementally-valid. In other words, if you have a 10-step solution, at each

step the cluster is both N+1 compliant and better than the previous step.

This means that you can stop at any point and you will have a better cluster.

For this reason, single-round solutions are recommended in the common case of

let's make this better. Multi-round solutions will be better though when adding

a couple of new, empty nodes to the cluster due to the many relocations needed.

Multi-round solutions

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

A multi-round solution (not for a single round), due to de-duplication of moves

(i.e. just put the instance directly in its final place, and not move it five

times around) loses both these properties. It might be that it's not possible to

directly put the instance on the final nodes. So it can be possible that yes,

the cluster is happy in the final solution and nice, but you cannot do the steps

in the shown order. Solving this (via additional instance move(s)) is left to

the user.

Command line usage

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

Synopsis::

    hbal { [-n NODES_FILE] [-i INSTANCES_FILE] | [-m CLUSTER] } \

         [-r MAX_ROUNDS] \

         [-p] [-C] [-o]

The -n and -i options change the names of the input files.

Alternatively, the -m option specifies collection of data via RAPI.

The -r option restricts the maximum number of rounds (and is more of

safety measure).

The -p option will show the cluster state after the solution is implemented,

while the -C option will show the needed gnt-instance commands to implement

it. The -o option specifies that instead the default, quite verbose

output, a single line of output should be shown, in the format::

  initial_score number_of_moves final_score improvement

Integration with Ganeti

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

The programs can either get their input from text files, or directly

from a cluster via RAPI. For text files, the following two commands

should be run::

    gnt-node list -oname,mtotal,mnode,mfree,dtotal,dfree \

      --separator '|' --no-headers > nodes

    gnt-instance list -oname,admin_ram,sda_size,pnode,snodes \

      --separator '|' --no-head > instances

These two files should be saved under the names of 'nodes' and 'instances'.

For RAPI, the "-m" argument to both hn1 and hbal should specify the

cluster or master node name.

When run, the programs will show some informational messages and output the

chosen solution, in the form of a list of instance name and chosen

primary/secondary nodes. The user then needs to run the necessary commands to

get the instances to live on those nodes.

Note that sda_size is less than the total disk size of an instance by 4352

MiB, so if disk space is at a premium the calculation could be wrong; in this

case, please adjust the values manually.