Resource model changes

Introduction

In order to manage virtual machines across the cluster, Ganeti needs to understand the resources present on the nodes, the hardware and software limitations of the nodes, and how much can be allocated safely on each node. Some of these decisions are delegated to IAllocator plugins, for easier site-level customisation.

Similarly, the HTools suite has an internal model that simulates the hardware resource changes in response to Ganeti operations, in order to provide both an iallocator plugin and for balancing the cluster.

While currently the HTools model is much more advanced than Ganeti’s, neither one is flexible enough and both are heavily geared toward a specific Xen model; they fail to work well with (e.g.) KVM or LXC, or with Xen when tmem is enabled. Furthermore, the set of metrics contained in the models is limited to historic requirements and fails to account for (e.g.) heterogeneity in the I/O performance of the nodes.

Current situation

Ganeti

At this moment, Ganeti itself doesn’t do any static modelling of the cluster resources. It only does some runtime checks:

• when creating instances, for the (current) free disk space
• when starting instances, for the (current) free memory
• during cluster verify, for enough N+1 memory on the secondaries, based on the (current) free memory

Basically this model is a pure SoW one, and it works well when there are other instances/LVs on the nodes, as it allows Ganeti to deal with ‘orphan’ resource usage, but on the other hand it has many issues, described below.

HTools

Since HTools does an pure in-memory modelling of the cluster changes as it executes the balancing or allocation steps, it had to introduce a static (SoR) cluster model.

The model is constructed based on the received node properties from Ganeti (hence it basically is constructed on what Ganeti can export).

Disk

For disk it consists of just the total (tdsk) and the free disk space (fdsk); we don’t directly track the used disk space. On top of this, we compute and warn if the sum of disk sizes used by instance does not match with tdsk - fdsk, but otherwise we do not track this separately.

Memory

For memory, the model is more complex and tracks some variables that Ganeti itself doesn’t compute. We start from the total (tmem), free (fmem) and node memory (nmem) as supplied by Ganeti, and additionally we track:

instance memory (imem)

the total memory used by primary instances on the node, computed as the sum of instance memory

reserved memory (rmem)

the memory reserved by peer nodes for N+1 redundancy; this memory is tracked per peer-node, and the maximum value out of the peer memory lists is the node’s rmem; when not using DRBD, this will be equal to zero

unaccounted memory (xmem)

memory that cannot be unaccounted for via the Ganeti model; this is computed at startup as:

tmem - imem - nmem - fmem

and is presumed to remain constant irrespective of any instance moves

available memory (amem)

this is simply fmem - rmem, so unless we use DRBD, this will be equal to fmem

tmem, nmem and xmem are presumed constant during the instance moves, whereas the fmem, imem, rmem and amem values are updated according to the executed moves.

CPU

The CPU model is different than the disk/memory models, since it’s the only one where:

1. we do oversubscribe physical CPUs
2. and there is no natural limit for the number of VCPUs we can allocate

We therefore track the total number of VCPUs used on the node and the number of physical CPUs, and we cap the vcpu-to-cpu ratio in order to make this somewhat more similar to the other resources which are limited.

Dynamic load

There is also a model that deals with dynamic load values in htools. As far as we know, it is not currently used actually with load values, but it is active by default with unitary values for all instances; it currently tracks these metrics:

• disk load
• memory load
• cpu load
• network load

Even though we do not assign real values to these load values, the fact that we at least sum them means that the algorithm tries to equalise these loads, and especially the network load, which is otherwise not tracked at all. The practical result (due to a combination of these four metrics) is that the number of secondaries will be balanced.

Limitations

There are unfortunately many limitations to the current model.

Memory

The memory model doesn’t work well in case of KVM. For Xen, the memory for the node (i.e. dom0) can be static or dynamic; we don’t support the latter case, but for the former case, the static value is configured in Xen/kernel command line, and can be queried from Xen itself. Therefore, Ganeti can query the hypervisor for the memory used for the node; the same model was adopted for the chroot/KVM/LXC hypervisors, but in these cases there’s no natural value for the memory used by the base OS/kernel, and we currently try to compute a value for the node memory based on current consumption. This, being variable, breaks the assumptions in both Ganeti and HTools.

This problem also shows for the free memory: if the free memory on the node is not constant (Xen with tmem auto-ballooning enabled), or if the node and instance memory are pooled together (Linux-based hypervisors like KVM and LXC), the current value of the free memory is meaningless and cannot be used for instance checks.

A separate issue related to the free memory tracking is that since we don’t track memory use but rather memory availability, an instance that is temporary down changes Ganeti’s understanding of the memory status of the node. This can lead to problems such as:

The behaviour here is wrong; the migration of instance2 to the node in question will succeed or fail depending on whether instance1 is running or not. And for instance1, it can lead to cases where it if crashes, it cannot restart anymore.

Finally, not a problem but rather a missing important feature is support for memory over-subscription: both Xen and KVM support memory ballooning, even automatic memory ballooning, for a while now. The entire memory model is based on a fixed memory size for instances, and if memory ballooning is enabled, it will “break” the HTools algorithm. Even the fact that KVM instances do not use all memory from the start creates problems (although not as high, since it will grow and stabilise in the end).

Disks

Because we only track disk space currently, this means if we have a cluster of N otherwise identical nodes but half of them have 10 drives of size X and the other half 2 drives of size 5X, HTools will consider them exactly the same. However, in the case of mechanical drives at least, the I/O performance will differ significantly based on spindle count, and a “fair” load distribution should take this into account (a similar comment can be made about processor/memory/network speed).

Another problem related to the spindle count is the LVM allocation algorithm. Currently, the algorithm always creates (or tries to create) striped volumes, with the stripe count being hard-coded to the ./configure parameter --with-lvm-stripecount. This creates problems like:

• when installing from a distribution package, all clusters will be either limited or overloaded due to this fixed value
• it is not possible to mix heterogeneous nodes (even in different node groups) and have optimal settings for all nodes
• the striping value applies both to LVM/DRBD data volumes (which are on the order of gigabytes to hundreds of gigabytes) and to DRBD metadata volumes (whose size is always fixed at 128MB); when stripping such small volumes over many PVs, their size will increase needlessly (and this can confuse HTools’ disk computation algorithm)

Moreover, the allocation currently allocates based on a ‘most free space’ algorithm. This balances the free space usage on disks, but on the other hand it tends to mix rather badly the data and metadata volumes of different instances. For example, it cannot do the following:

• keep DRBD data and metadata volumes on the same drives, in order to reduce exposure to drive failure in a many-drives system
• keep DRBD data and metadata volumes on different drives, to reduce performance impact of metadata writes

Additionally, while Ganeti supports setting the volume separately for data and metadata volumes at instance creation, there are no defaults for this setting.

Similar to the above stripe count problem (which is about not good enough customisation of Ganeti’s behaviour), we have limited pass-through customisation of the various options of our storage backends; while LVM has a system-wide configuration file that can be used to tweak some of its behaviours, for DRBD we don’t use the drbdadmin tool, and instead we call drbdsetup directly, with a fixed/restricted set of options; so for example one cannot tweak the buffer sizes.

Another current problem is that the support for shared storage in HTools is still limited, but this problem is outside of this design document.

Locking

A further problem generated by the “current free” model is that during a long operation which affects resource usage (e.g. disk replaces, instance creations) we have to keep the respective objects locked (sometimes even in exclusive mode), since we don’t want any concurrent modifications to the free values.

A classic example of the locking problem is the following:

In the above example, the second IAllocator run will wait for locks for nodes A and B, even though in the end the second instance will be placed on another set of nodes (C and D). This wait shouldn’t be needed, since right after the first IAllocator run has finished, hail knows the status of the cluster after the allocation, and it could answer the question for the second run too; however, Ganeti doesn’t have such visibility into the cluster state and thus it is forced to wait with the second job.

Similar examples can be made about replace disks (another long-running opcode).

Policies

For most of the resources, we have metrics defined by policy: e.g. the over-subscription ratio for CPUs, the amount of space to reserve, etc. Furthermore, although there are no such definitions in Ganeti such as minimum/maximum instance size, a real deployment will need to have them, especially in a fully-automated workflow where end-users can request instances via an automated interface (that talks to the cluster via RAPI, LUXI or command line). However, such an automated interface will need to also take into account cluster capacity, and if the hspace tool is used for the capacity computation, it needs to be told the maximum instance size, however it has a built-in minimum instance size which is not customisable.

It is clear that this situation leads to duplicate definition of resource policies which makes it hard to easily change per-cluster (or globally) the respective policies, and furthermore it creates inconsistencies if such policies are not enforced at the source (i.e. in Ganeti).

Balancing algorithm

The balancing algorithm, as documented in the HTools README file, tries to minimise the cluster score; this score is based on a set of metrics that describe both exceptional conditions and how spread the instances are across the nodes. In order to achieve this goal, it moves the instances around, with a series of moves of various types:

• disk replaces (for DRBD-based instances)
• instance failover/migrations (for all types)

However, the algorithm only looks at the cluster score, and not at the “cost” of the moves. In other words, the following can and will happen on a cluster:

Even though a migration is much, much cheaper than a disk replace (in terms of network and disk traffic on the cluster), if the disk replace results in a score infinitesimally smaller, then it will be chosen. Similarly, between two disk replaces, one moving e.g. 500GiB and one moving 20GiB, the first one will be chosen if it results in a score smaller than the second one. Furthermore, even if the resulting scores are equal, the first computed solution will be kept, whichever it is.

Fixing this algorithmic problem is doable, but currently Ganeti doesn’t export enough information about nodes to make an informed decision; in the above example, if the 500GiB move is between nodes having fast I/O (both disks and network), it makes sense to execute it over a disk replace of 100GiB between nodes with slow I/O, so simply relating to the properties of the move itself is not enough; we need more node information for cost computation.

Allocation algorithm

Note

This design document will not address this limitation, but it is worth mentioning as it directly related to the resource model.

The current allocation/capacity algorithm works as follows (per node-group):

repeat:
    allocate instance without failing N+1

This simple algorithm, and its use of N+1 criterion, has a built-in limit of 1 machine failure in case of DRBD. This means the algorithm guarantees that, if using DRBD storage, there are enough resources to (re)start all affected instances in case of one machine failure. This relates mostly to memory; there is no account for CPU over-subscription (i.e. in case of failure, make sure we can failover while still not going over CPU limits), or for any other resource.

In case of shared storage, there’s not even the memory guarantee, as the N+1 protection doesn’t work for shared storage.

If a given cluster administrator wants to survive up to two machine failures, or wants to ensure CPU limits too for DRBD, there is no possibility to configure this in HTools (neither in hail nor in hspace). Current workaround employ for example deducting a certain number of instances from the size computed by hspace, but this is a very crude method, and requires that instance creations are limited before Ganeti (otherwise hail would allocate until the cluster is full).

Proposed architecture

There are two main changes proposed:

• changing the resource model from a pure SoW to a hybrid SoR/SoW one, where the SoR component is heavily emphasised
• extending the resource model to cover additional properties, completing the “holes” in the current coverage

The second change is rather straightforward, but will add more complexity in the modelling of the cluster. The first change, however, represents a significant shift from the current model, which Ganeti had from its beginnings.

Lock-improved resource model

Hybrid SoR/SoW model

The resources of a node can be characterised in two broad classes:

• mostly static resources
• dynamically changing resources

In the first category, we have things such as total core count, total memory size, total disk size, number of network interfaces etc. In the second category we have things such as free disk space, free memory, CPU load, etc. Note that nowadays we don’t have (anymore) fully-static resources: features like CPU and memory hot-plug, online disk replace, etc. mean that theoretically all resources can change (there are some practical limitations, of course).

Even though the rate of change of the two resource types is wildly different, right now Ganeti handles both the same. Given that the interval of change of the semi-static ones is much bigger than most Ganeti operations, even more than lengthy sequences of Ganeti jobs, it makes sense to treat them separately.

The proposal is then to move the following resources into the configuration and treat the configuration as the authoritative source for them (a SoR model):

• CPU resources:

  • total core count
  • node core usage (new)

• memory resources:

  • total memory size
  • node memory size
  • hypervisor overhead (new)

• disk resources:

  • total disk size
  • disk overhead (new)

Since these resources can though change at run-time, we will need functionality to update the recorded values.

Pre-computing dynamic resource values

Remember that the resource model used by HTools models the clusters as obeying the following equations:

disk_free = disk_total - ∑ disk_instances

mem_free = mem_total - ∑ mem_instances - mem_node - mem_overhead

As this model worked fine for HTools, we can consider it valid and adopt it in Ganeti. Furthermore, note that all values in the right-hand side come now from the configuration:

• the per-instance usage values were already stored in the configuration
• the other values will are moved to the configuration per the previous section

This means that we can now compute the free values without having to actually live-query the nodes, which brings a significant advantage.

There are a couple of caveats to this model though. First, as the run-time state of the instance is no longer taken into consideration, it means that we have to introduce a new offline state for an instance (similar to the node one). In this state, the instance’s runtime resources (memory and VCPUs) are no longer reserved for it, and can be reused by other instances. Static resources like disk and MAC addresses are still reserved though. Transitioning into and out of this reserved state will be more involved than simply stopping/starting the instance (e.g. de-offlining can fail due to missing resources). This complexity is compensated by the increased consistency of what guarantees we have in the stopped state (we always guarantee resource reservation), and the potential for management tools to restrict which users can transition into/out of this state separate from which users can stop/start the instance.

Separating per-node resource locks

Many of the current node locks in Ganeti exist in order to guarantee correct resource state computation, whereas others are designed to guarantee reasonable run-time performance of nodes (e.g. by not overloading the I/O subsystem). This is an unfortunate coupling, since it means for example that the following two operations conflict in practice even though they are orthogonal:

• replacing a instance’s disk on a node
• computing node disk/memory free for an IAllocator run

This conflict increases significantly the lock contention on a big/busy cluster and at odds with the goal of increasing the cluster size.

The proposal is therefore to add a new level of locking that is only used to prevent concurrent modification to the resource states (either node properties or instance properties) and not for long-term operations:

• instance creation needs to acquire and keep this lock until adding the instance to the configuration
• instance modification needs to acquire and keep this lock until updating the instance
• node property changes will need to acquire this lock for the modification

The new lock level will sit before the instance level (right after BGL) and could either be single-valued (like the “Big Ganeti Lock”), in which case we won’t be able to modify two nodes at the same time, or per-node, in which case the list of locks at this level needs to be synchronised with the node lock level. To be determined.

Lock contention reduction

Based on the above, the locking contention will be reduced as follows: IAllocator calls will no longer need the LEVEL_NODE: ALL_SET lock, only the resource lock (in exclusive mode). Hence allocating/computing evacuation targets will no longer conflict for longer than the time to compute the allocation solution.

The remaining long-running locks will be the DRBD replace-disks ones (exclusive mode). These can also be removed, or changed into shared locks, but that is a separate design change.

FIXME

Need to rework instance replace disks. I don’t think we need exclusive locks for replacing disks: it is safe to stop/start the instance while it’s doing a replace disks. Only modify would need exclusive, and only for transitioning into/out of offline state.

Instance memory model

In order to support ballooning, the instance memory model needs to be changed from a “memory size” one to a “min/max memory size”. This interacts with the new static resource model, however, and thus we need to declare a-priori the expected oversubscription ratio on the cluster.

The new minimum memory size parameter will be similar to the current memory size; the cluster will guarantee that in all circumstances, all instances will have available their minimum memory size. The maximum memory size will permit burst usage of more memory by instances, with the restriction that the sum of maximum memory usage will not be more than the free memory times the oversubscription factor:

∑ memory_min ≤ memory_available

∑ memory_max ≤ memory_free * oversubscription_ratio

The hypervisor will have the possibility of adjusting the instance’s memory size dynamically between these two boundaries.

Note that the minimum memory is related to the available memory on the node, whereas the maximum memory is related to the free memory. On DRBD-enabled clusters, this will have the advantage of using the reserved memory for N+1 failover for burst usage, instead of having it completely idle.

FIXME

Need to document how Ganeti forces minimum size at runtime, overriding the hypervisor, in cases of failover/lack of resources.

New parameters

Unfortunately the design will add a significant number of new parameters, and change the meaning of some of the current ones.

Instance size limits

As described in Policies, we currently lack a clear definition of the support instance sizes (minimum, maximum and standard). As such, we will add the following structure to the cluster parameters:

• min_ispec, max_ispec: minimum and maximum acceptable instance specs
• std_ispec: standard instance size, which will be used for capacity computations and for default parameters on the instance creation request

Ganeti will by default reject non-standard instance sizes (lower than min_ispec or greater than max_ispec), but as usual a --ignore-ipolicy option on the command line or in the RAPI request will override these constraints. The std_spec structure will be used to fill in missing instance specifications on create.

Each of the ispec structures will be a dictionary, since the contents can change over time. Initially, we will define the following variables in these structures:

Name	Description	Type
mem_size	Allowed memory size	int
cpu_count	Allowed vCPU count	int
disk_count	Allowed disk count	int
disk_size	Allowed disk size	int
nic_count	Alowed NIC count	int

Inheritance

In a single-group cluster, the above structure is sufficient. However, on a multi-group cluster, it could be that the hardware specifications differ across node groups, and thus the following problem appears: how can Ganeti present unified specifications over RAPI?

Since the set of instance specs is only partially ordered (as opposed to the sets of values of individual variable in the spec, which are totally ordered), it follows that we can’t present unified specs. As such, the proposed approach is to allow the min_ispec and max_ispec to be customised per node-group (and export them as a list of specifications), and a single std_spec at cluster level (exported as a single value).

Allocation parameters

Beside the limits of min/max instance sizes, there are other parameters related to capacity and allocation limits. These are mostly related to the problems related to over allocation.

Name	Level(s)	Description	Current value	Type
vcpu_ratio	cluster, node group	Maximum ratio of virtual to physical CPUs	64 (only in htools)	float
spindle_ratio	cluster, node group	Maximum ratio of instances to spindles; when the I/O model doesn’t map directly to spindles, another measure of I/O should be used instead	none	float
max_node_failures	cluster, node group	Cap allocation/capacity so that the cluster can survive this many node failures	1 (hardcoded in htools)	int

Since these are used mostly internally (in htools), they will be exported as-is from Ganeti, without explicit handling of node-groups grouping.

Regarding spindle_ratio, in this context spindles do not necessarily have to mean actual mechanical hard-drivers; it’s rather a measure of I/O performance for internal storage.

Disk parameters

The proposed model for the new disk parameters is a simple free-form one based on dictionaries, indexed per disk template and parameter name. Only the disk template parameters are visible to the user, and those are internally translated to logical disk level parameters.

This is a simplification, because each parameter is applied to a whole nested structure and there is no way of fine-tuning each level’s parameters, but it is good enough for the current parameter set. This model could need to be expanded, e.g., if support for three-nodes stacked DRBD setups is added to Ganeti.

At JSON level, since the object key has to be a string, the keys can be encoded via a separator (e.g. slash), or by having two dict levels.

When needed, the unit of measurement is expressed inside square brackets.

Disk template	Name	Description	Current status	Type
plain	stripes	How many stripes to use for newly created (plain) logical voumes	Configured at ./configure time, not overridable at runtime	int
drbd	data-stripes	How many stripes to use for data volumes	Same as for plain/stripes	int
drbd	metavg	Default volume group for the metadata LVs	Same as the main volume group, overridable via ‘metavg’ key	string
drbd	meta-stripes	How many stripes to use for meta volumes	Same as for lvm ‘stripes’, suboptimal as the meta LVs are small	int
drbd	disk-barriers	What kind of barriers to disable for disks; either “n” or a string containing a subset of “bfd”	Either all enabled or all disabled, per ./configure time option	string
drbd	meta-barriers	Whether to disable or not the barriers for the meta volume	Handled together with disk-barriers	bool
drbd	resync-rate	The (static) resync rate for drbd, when using the static syncer, in KiB/s	Hardcoded in constants.py, not changeable via Ganeti	int
drbd	dynamic-resync	Whether to use the dynamic resync speed controller or not. If enabled, c-plan-ahead must be non-zero and all the c-* parameters will be used by DRBD. Otherwise, the value of resync-rate will be used as a static resync speed.	Not supported.	bool
drbd	c-plan-ahead	Agility factor of the dynamic resync speed controller. (the higher, the slower the algorithm will adapt the resync speed). A value of 0 (that is the default) disables the controller [ds]	Not supported.	int
drbd	c-fill-target	Maximum amount of in-flight resync data for the dynamic resync speed controller [sectors]	Not supported.	int
drbd	c-delay-target	Maximum estimated peer response latency for the dynamic resync speed controller [ds]	Not supported.	int
drbd	c-max-rate	Upper bound on resync speed for the dynamic resync speed controller [KiB/s]	Not supported.	int
drbd	c-min-rate	Minimum resync speed for the dynamic resync speed controller [KiB/s]	Not supported.	int
drbd	disk-custom	Free-form string that will be appended to the drbdsetup disk command line, for custom options not supported by Ganeti itself	Not supported	string
drbd	net-custom	Free-form string for custom net setup options	Not supported	string

Currently Ganeti supports only DRBD 8.0.x, 8.2.x, 8.3.x. It will refuse to work with DRBD 8.4 since the drbdsetup syntax has changed significantly.

The barriers-related parameters have been introduced in different DRBD versions; please make sure that your version supports all the barrier parameters that you pass to Ganeti. Any version later than 8.3.0 implements all of them.

The minimum DRBD version for using the dynamic resync speed controller is 8.3.9, since previous versions implement different parameters.

A more detailed discussion of the dynamic resync speed controller parameters is outside the scope of the present document. Please refer to the drbdsetup man page (8.3 and 8.4). An interesting discussion about them can also be found in a drbd-user mailing list post.

All the above parameters are at cluster and node group level; as in other parts of the code, the intention is that all nodes in a node group should be equal. It will later be decided to which node group give precedence in case of instances split over node groups.

FIXME

Add details about when each parameter change takes effect (device creation vs. activation)

Node parameters

For the new memory model, we’ll add the following parameters, in a dictionary indexed by the hypervisor name (node attribute hv_state). The rationale is that, even though multi-hypervisor clusters are rare, they make sense sometimes, and thus we need to support multipe node states (one per hypervisor).

Since usually only one of the multiple hypervisors is the ‘main’ one (and the others used sparringly), capacity computation will still only use the first hypervisor, and not all of them. Thus we avoid possible inconsistencies.

Name	Description	Current state	Type
mem_total	Total node memory, as discovered by this hypervisor	Queried at runtime	int
mem_node	Memory used by, or reserved for, the node itself; not that some hypervisors can report this in an authoritative way, other not	Queried at runtime	int
mem_hv	Memory used either by the hypervisor itself or lost due to instance allocation rounding; usually this cannot be precisely computed, but only roughly estimated	Not used, htools computes it internally	int
cpu_total	Total node cpu (core) count; usually this can be discovered automatically	Queried at runtime	int
cpu_node	Number of cores reserved for the node itself; this can either be discovered or set manually. Only used for estimating how many VCPUs are left for instances	Not used at all	int

Of the above parameters, only _total ones are straight-forward. The others have sometimes strange semantics:

• Xen can report mem_node, if configured statically (as we recommend); but Linux-based hypervisors (KVM, chroot, LXC) do not, and this needs to be configured statically for these values
• mem_hv, representing unaccounted for memory, is not directly computable; on Xen, it can be seen that on a N GB machine, with 1 GB for dom0 and N-2 GB for instances, there’s just a few MB left, instead fo a full 1 GB of RAM; however, the exact value varies with the total memory size (at least)
• cpu_node only makes sense on Xen (currently), in the case when we restrict dom0; for Linux-based hypervisors, the node itself cannot be easily restricted, so it should be set as an estimate of how “heavy” the node loads will be

Since these two values cannot be auto-computed from the node, we need to be able to declare a default at cluster level (debatable how useful they are at node group level); the proposal is to do this via a cluster-level hv_state dict (per hypervisor).

Beside the per-hypervisor attributes, we also have disk attributes, which are queried directly on the node (without hypervisor involvment). The are stored in a separate attribute (disk_state), which is indexed per storage type and name; currently this will be just LD_LV and the volume name as key.

Name	Description	Current state	Type
disk_total	Total disk size	Queried at runtime	int
disk_reserved	Reserved disk size; this is a lower limit on the free space, if such a limit is desired	None used in Ganeti; htools has a parameter for this	int
disk_overhead	Disk that is expected to be used by other volumes (set via reserved_lvs); usually should be zero	None used in Ganeti; htools detects this at runtime	int

Instance parameters

New instance parameters, needed especially for supporting the new memory model:

Name	Description	Current status	Type
offline	Whether the instance is in “permanent” offline mode; this is stronger than the “admin_down” state, and is similar to the node offline attribute	Not supported	bool
be/max_memory	The maximum memory the instance is allowed	Not existent, but virtually identical to memory	int

HTools changes

All the new parameters (node, instance, cluster, not so much disk) will need to be taken into account by HTools, both in balancing and in capacity computation.

Since the Ganeti’s cluster model is much enhanced, Ganeti can also export its own reserved/overhead variables, and as such HTools can make less “guesses” as to the difference in values.

FIXME

Need to detail more the htools changes; the model is clear to me, but need to write it down.