26227b79e9
There is a new Zones implementation coming that will use AMQP-to-AMQP channels vs. the public API. This is being done for three reasons: 1. remove complications in the OpenStack API (and possibly allow EC2 Zones) 2. remove dependencies on keystone and novaclient 3. faster scheduling (fewer chances for race conditions) Learn more here: http://wiki.openstack.org/EssexSchedulerImprovements Change-Id: I6fe538923dd5ae19276afac2ac3311a285fd5c99
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5.6 KiB
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89 lines
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Copyright 2011 OpenStack LLC
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All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may
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not use this file except in compliance with the License. You may obtain
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a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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License for the specific language governing permissions and limitations
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under the License.
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Source for illustrations in doc/source/image_src/zone_distsched_illustrations.odp
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(OpenOffice Impress format) Illustrations are "exported" to png and then scaled
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to 400x300 or 640x480 as needed and placed in the doc/source/images directory.
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Distributed Scheduler
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=====================
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The Scheduler is akin to a Dating Service. Requests for the creation of new instances come in and the most applicable Compute nodes are selected from a large pool of potential candidates. In a small deployment we may be happy with the currently available Chance Scheduler which randomly selects a Host from the available pool. Or if you need something a little more fancy you may want to use the Distributed Scheduler, which selects Compute hosts from a logical partitioning of available hosts (within a single Zone).
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.. image:: /images/dating_service.png
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The Distributed Scheduler (DS) supports filtering and weighing to make informed decisions on where a new instance should be created.
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So, how does this all work?
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Costs & Weights
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---------------
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When deciding where to place an Instance, we compare a Weighted Cost for each Host. The Weighting, currently, is just the sum of each Cost. Costs are nothing more than integers from `0 - max_int`. Costs are computed by looking at the various Capabilities of the Host relative to the specs of the Instance being asked for. Trying to put a plain vanilla instance on a high performance host should have a very high cost. But putting a vanilla instance on a vanilla Host should have a low cost.
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Some Costs are more esoteric. Consider a rule that says we should prefer Hosts that don't already have an instance on it that is owned by the user requesting it (to mitigate against machine failures). Here we have to look at all the other Instances on the host to compute our cost.
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An example of some other costs might include selecting:
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* a GPU-based host over a standard CPU
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* a host with fast ethernet over a 10mbps line
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* a host that can run Windows instances
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* a host in the EU vs North America
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* etc
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This Weight is computed for each Instance requested. If the customer asked for 1000 instances, the consumed resources on each Host are "virtually" depleted so the Cost can change accordingly.
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.. image:: /images/costs_weights.png
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Filtering and Weighing
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----------------------
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The filtering (excluding compute nodes incapable of fulfilling the request) and weighing (computing the relative "fitness" of a compute node to fulfill the request) rules used are very subjective operations ... Service Providers will probably have a very different set of filtering and weighing rules than private cloud administrators. The filtering and weighing aspects of the `DistributedScheduler` are flexible and extensible.
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.. image:: /images/filtering.png
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Host Filter
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-----------
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As we mentioned earlier, filtering hosts is a very deployment-specific process. Service Providers may have a different set of criteria for filtering Compute nodes than a University. To faciliate this the `nova.scheduler.filters` module supports a variety of filtering strategies as well as an easy means for plugging in your own algorithms.
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The filter used is determined by the `--default_host_filters` flag, which points to a Python Class. By default this flag is set to `[AllHostsFilter]` which simply returns all available hosts. But there are others:
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* `ComputeFilter` provides host filtering based on the memory and disk size specified in the `InstanceType` record passed into `run_instance`.
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* `JSONFilter` filters hosts based on simple JSON expression grammar. Using a LISP-like JSON structure the caller can request instances based on criteria well beyond what `ComputeFilter` specifies. See `nova.tests.scheduler.test_host_filters` for examples.
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To create your own `HostFilter` the user simply has to derive from `nova.scheduler.filters.AbstractHostFilter` and implement one method: `host_passes`. This method accepts a `HostState` instance describing a host as well as a `filter_properties` dictionary. Host capabilities can be found in `HostState`.capabilities and other properites can be found in `filter_properties` like `instance_type`, etc. Your method should return True if it passes the filter.
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Flags
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-----
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Here are some of the main flags you should set in your `nova.conf` file:
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::
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--scheduler_driver=nova.scheduler.distributed_scheduler.DistributedScheduler
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--default_host_filters=nova.scheduler.filters.AllHostsFilter
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`scheduler_driver` is the real workhorse of the operation. For Distributed Scheduler, you need to specify a class derived from `nova.scheduler.distributed_scheduler.DistributedScheduler`.
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`default_host_filter` is the host filter to be used for filtering candidate Compute nodes.
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Some optional flags which are handy for debugging are:
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::
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--connection_type=fake
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--verbose
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Using the `Fake` virtualization driver is handy when you're setting this stuff up so you're not dealing with a million possible issues at once. When things seem to working correctly, switch back to whatever hypervisor your deployment uses.
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