Tag: availability

Dependability Modeling on OpenStack: Part 2

In the previous article we defined use cases for an OpenStack implementation according to the usage scenario in which the OpenStack environment is deployed. In this part of the Dependability Modeling article series we will show how these use cases relate to functions and services provided by the OpenStack environment and create a set of dependabilities between use cases, functions, services and system components. From this set we will draw the dependency graph and make the impact of component outages computable.

Construct dependency table

The dependency graph can be constructed if we define which functions, services and components allow provision of a use case. In the example below (Fig. 1) we defined the system architecture components, services and functions which allow to create, delete or update details of a Telco Account (account of mobile end user). Since these operations are provided within virtual machines, VM User Management and VM Security Management functions provide availability of this use case. Therefore we draw a column which contains these functions. Because these functions need a User Management, SSH & Password Management service in each VM in order to operate, we draw a second column which contains the required services. Another column is constructed which tells the system components required in order to deliver the required services.

Fig. 1: Dependency Graph Construction.

Fig. 1: Dependency Graph Construction.

The procedure mentioned above is repeated for all use cases. As a result you get a table like the one in (Tab. 1). This dependency table is the starting point for the production of the dependency graph.

Tab. 1: Dependencies between Use Cases, Services, Functions and Components.


Construct dependency graph

For each component that is listed in the table you have to model the corresponding services, functions and use cases. This is performed like in the example in (Fig. 2). We start from the right of the graph with the Ceilometer component and the VM plugin and look which services are provided by those components: it is e. g. the “Ceilometer Monitoring” service. Therefore we draw an icon that represents this service and draw arrows from the Ceilometer and VM plugin components to the service icon (1). In the next step we look which function is provided by the Ceilometer Monitoring service. This is the “Monitoring of VM” function. Therefore we paste an icon for the function and draw an arrow to this function (2). Then we look for the use cases provided by the Monitoring of VM function. Since this is e. g. “Measure SLAs”, we paste an icon for this use case and draw another arrow to “Measure SLAs” (3). The first path between an use case and components on which it depends is drawn. This procedure is repeated on all components in (Tab. 1).

Fig. 2: Dependency Graph Construction from Dependency Table.

Fig. 2: Dependency Graph Construction from Dependency Table.

The result is the dependency graph shown below (Fig. 3).

Fig. 3: Dependency Graph of OpenStack Environment.

Fig. 3: Dependency Graph of OpenStack Environment.

Add weight factors to use cases

Once the dependency graph is constructed, we can calculate the “impact” of component outages. When a component fails, you can simply follow the arrows in the dependency graph to see which user interactions (use cases) stop to be available for end users. If e. g. the Ceilometer component fails, you would not be able to measure SLAs, meter usage of Telco services or monitor the VM infrastructure.

But it would not be a very sophisticated practice to say that each use case is equally important to the end user. Some user interactions like e. g. creation of new VM nodes need not be available all the time (or at least it depends on the OLAs of the Telco). Other actions like e. g. Telco authentication must be available all the time. Therefore, we have to add weight factors to use cases. This can be done by adding another column to the dependency table and name it “Weight factor”. The weight factor should be a score measuring the “importance” of an user interaction in terms of business need. In a productive OpenStack environment, financial values (which correspond to the business value of the user interaction) could be assigned as weight factors to each use case. For reasons of simplicity we take the ordinal values 1, 2 and 3 as weight factors (whereby 1 signifies the least important user transaction and 3 the most important user transaction). For each use case row in the dependency table we add the corresponding weight factor (Fig. 4).

Fig. 4: Assignment of weight factors.

Fig. 4: Assignment of weight factors.

As a next step, we create a pivot table containing the components and use cases as consecutive row fields and the weight factors as data field. In order to avoid duplicate counts (of use cases) we use the maximum function instead of the sum function. As a result we get the pivot table in (Tab. 2).

Tab. 2: Pivot Table of Component/Use Case dependencies.


Calculate outage impacts

Calculation of system component outages is now quite straightforward. Just look at the pivot table and calculate the pivot sum of the weight factors of each component. As a result we have a table of failure impact sizes (Tab.3).

Tab. 3: OpenStack Components and Failure Impact Sizes.

Tab. 3: OpenStack Components and Failure Impact Sizes.

This table reveals which components are very important for the overall reliability of the OpenStack environment and which are not. It is an operationalization of the measurement of “failure impact” for a given IT environment (failure impacts can be measured as number). The advantage of this approach is that we can build a test framework for OpenStack availability based on the failure impact sizes.

Most obviously components whith strong support functionality like e. g. MySQL or the Keystone component have high failure impact sizes and should be strongly protected against outages. VM internal components seem to be not so important because VMs can be easily cloned and recovered in a cloud environment.

In a further article we will show how availability can be tested with the given failure impact size values on a given OpenStack architecture.


2nd Swiss OpenStack User Group Meeting

The second swiss OpenStack user group (CHOSUG) was held. It was an excellent event so well attended that there was only standing room! A big thanks goes out to the organisers and sponsors (RackSpace, SWITCH and ICCLab).


There was six presentations, 3 which were more detailed and 3 that were more lightning talks in nature. Lucas and Toni from the ICCLab gave a super presentation on Ceilometer and Christof made short work of the deep topic of OpenStack and CloudFoundry. The presentations (in running order) were:

All talks were recorded by the kind folks at SWITCH and are available for your viewing pleasure!

Check out more pics on the CHOSUG flickr account.

How Do You Organise Your OpenStack Deployment?

So you have a new shiny OpenStack installation! Within that installation you may have differing classes of hardware and so you wish to be able to organise those classes.

To organise your OpenStack deployment there are two concepts currently available: Availability Zones (AZs) and Cells. These allow you categorise your resources within an OpenStack deployment and organise those resources as you see fit. These is a very useful feature in order to offer different types of the same service. For example you might want to offer a compute service that runs on SSDs or plain spinning disks.

In this article we’ll describe OpenStack Availability Zones (AZs) and OpenStack Cells. We’ll also show how each differ.

AZs are already available in Essex and Folsom releases. AZs allow you organise groups of hosts by tag (think folksonomy), however each compute node only can have one tag – the name of the availability zone their part of.


In this blog post is was said that “The good thing with availability zones is that you can manage and isolate different entities in your infrastructure.” However, there is little in terms of isolation given with AZs as all instances, without significant customisation, are still sharing the same infrastructure and OpenStack services. AZs allow you organise both compute and volume services and to enable the AZ feature you simply only have to declare node_availability_zone = $YOUR_AZ_NAME in the /etc/nova/nova.conf file (or configuration file fragment e.g. nova-compute.conf). This should be done on a per-node basis (so using parameterised puppet classes is useful here ;-). To list your nodes along with their assigned AZ, simply execute nova-manage service list. To allocate an instance upon a specific AZ you need to add –availability-zone $TARGET_AZ to the nova boot command. Currently, AZs are not exposed in the Horizon dashboard.

An alternative to AZs are OpenStack Cells. Cells separate much of the OpenStack services that are not separated by default when using AZs. These are only available in the current development edition of OpenStack and are targeted at the Grizzly release. Cells allow you organise groups of hosts as a directed graph (think ontology) or in other words, a tree. This is shown below (source).


For each cell there is a separate message bus and database. The head of the tree has the OpenStack API service running. All child nodes within the cell tree are known to the OpenStack scheduler service and depending on what cell is selected, the scheduler will route a new instance request to that cell. In this way you can think of the request processing as IP packet routing. This certainly makes for more flexible organisation of larger OpenStack deployments, however perhaps add some amount of delay to provisioning: only benchmarking will tell. For further details on its configuration and setup see the Cell OpenStack wiki article. This is also a good overview presentation of Cells by one of the key developers. Cells are pretty cool and possibly lend themselves well to providing management capabilities where a provider has inter-connected data centres.

The great thing with both of these approaches is that it allows you to offer differentiated services to your customers. Could they be used together, most likely! We’ll certainly be experimenting with these features on our ICCLab OpenStack cluster.