Tag Archives: Distributed Storage Fabric

Openstack + Nutanix: Glance Image Service

This post will cover the retrieval of base or cloud OS images via the Openstack Glance image service and how the Acropolis driver interacts with Glance and maintains the image data on the Nutanix Distributed Storage Fabric (DSF).

From the Openstack documentation:

  • The Glance image service includes discovering, registering and retrieving virtual machine images
  • has a RESTful API  – allows querying of image metadata and actual image retrieval
  • It has the ability to copy (or snapshot) a server image and then to store it promptly. Stored images then can be used as templates to get new servers up and running quickly, and can also be used to store and catalog unlimited backups.

The Acropolis driver interacts with the Glance service by redirecting an image from the Openstack controller to the Acropolis DSF. Aside from any image metadata (ie: image configuration details) being stored in Glance, the image itself is actually stored on the Nutanix cluster. We do not store any images in the OVM, either in the Glance store or anywhere else within the Openstack Controller.

Images are managed in Openstack via System > Images – see screenshot below for example list of available images in an Openstack environment

glance-images

Images in Openstack are mostly downloaded via HTTP URL. Though file upload does work.  The image creation workflow in the screenshot below shows a Fedora 23 Cloud image in QCOW2 format being retrieved. I have left the respective “Minimum Disk” (size) and “Minimum RAM” fields blank – so that no minimum is set for either.

image-create

You can confirm the images are loaded into the Nutanix Cluster backend by viewing the Image Configuration menu in Prism. The images in Prism are stored on a specific container in my case.

prism-images

Similarly, Prism will report the progress of the Image upload to the cluster through the event and progress monitoring facility on the main menu bar.

prism-images-tasks

If all you really need is a quick demo perhaps, then Openstack suggests the following OS image for test purposes. Use this simply to test and demonstrate the basic glance functionality via the command line. Works exactly the same if done via Horizon GUI, however.

[root@nx-ovm ~]# source keystonerc_admin
[root@nx-ovm ~(keystone_admin)]# glance image-create --name cirros-0.3.2-x86_64 \
--is-public true --container-format bare --disk-format qcow2 \
--copy-from http://download.cirros-cloud.net/0.3.2/cirros-0.3.2-x86_64-disk.img

+------------------+--------------------------------------+
| Property         | Value                                |
+------------------+--------------------------------------+
| checksum         | None                                 |
| container_format | bare                                 |
| created_at       | 2016-04-05T10:31:53.000000           |
| deleted          | False                                |
| deleted_at       | None                                 |
| disk_format      | qcow2                                |
| id               | f51ab65b-b7a5-4da1-92d9-8f0042af8762 |
| is_public        | True                                 |
| min_disk         | 0                                    |
| min_ram          | 0                                    |
| name             | cirros-0.3.2-x86_64                  | 
| owner            | 529638a186034e5daa11dd831cd1c863     |
| protected        | False                                |
| size             | 0                                    |
| status           | queued                               |
| updated_at       | 2016-04-05T10:31:53.000000           |
| virtual_size     | None                                 |
+------------------+--------------------------------------+

This is then reflected in the glance image list

[root@nx-ovm ~(keystone_admin)]# glance image-list
+--------------------------------------+----------------------------+-------------+------------------+------------+--------+
| ID                                   | Name                       | Disk Format | Container Format | Size       | Status |
+--------------------------------------+----------------------------+-------------+------------------+------------+--------+
| 44b4c9ab-b436-4b0c-ac8d-97acbabbbe60 | CentOS 7 x86_84            | qcow2       | bare             | 8589934592 | active |
| 033f24a3-b709-460a-ab01-f54e87e0e25b | cirros-0.3.2-x86_64        | qcow2       | bare             | 41126400   | active |
| f9b455b2-6fba-46d2-84d4-bb5cfceacdc7 | Fedora 23 Cloud            | qcow2       | bare             | 234363392  | active |
| 13992521-f555-4e6b-852b-20c385648947 | Ubuntu 14.04 - Cloud Image | qcow2       | bare             | 2361393152 | active |
+--------------------------------------+----------------------------+-------------+------------------+------------+--------+

One other thing to be aware of is that all network, image, instance and volume manipulation should only be done via the Openstack dashboard. All the Openstack elements created this way can not subsequently be changed or edited with the Acropolis Prism GUI. Both management interfaces are independent of one another. In fact the Openstack Services VM (OVM) was intentionally designed this way to be completely stateless. Though obviously this could change in future product iterations, if it was deemed to be a better solution going forward.

I have included the Openstack docs URL with additional image locations for anyone wanting to pull images of their own to work with. This is an excellent reference location for potential cloud instance images for both Linux distros and Windows:

http://docs.openstack.org/image-guide/

Next up, we will have a look at using the Acropolis Cinder plugin for Block Storage and the Nova Compute service integration.

Nutanix: Cloud-like DevOps powering NoSQL for BigData

The popularity of NoSQL has increasingly come about as developers want to use the same in-memory data structures in their applications and have them map directly into a database persistence layer. For example, storing data in XML or JSON format is often hierarchical and potentially does not lend itself to being easily stored in row based tables. It becomes more complicated if the data also contains lists and objects. Not having to convert these in-memory structures into relational database structures is a major advantage in terms of time to value. Such considerations have been made all the more acute by the rise of the web as a platform for services. There’s also an economic aspect, like the prohibitive infrastructure costs required to scale up traditional RDBMS to support high availability etc. Compare this to such Web-Scale or cloud aware apps like NoSQL, which expects to “just drop in” commodity hardware at the infrastructure layer and scale out horizontally on demand.

So if we were to consider the requirements from a modern hyper-converged infrastructure (HCI) that employed the same Web-Scale paradigms used by modern cloud-aware applications. Then to deploy apps, like a NoSQL database for example, the first thing I would want to do is virtualise. This means a right-sized, sandboxed environment (ie a virtual machine) to run individual NoSQL instances. If there was a need to scale up, then it’s a simple case of increasing RAM and CPU. As the application landscape grows over time and starts to scale out, there’s increased need for more nodes/VMs.  Hence, any HCI platform needs cloud like provisioning of nodes. So providing faster time to deploy and time to value. The ability to auto-discover and add new nodes by the click of a button is quite compelling. In short, horizontal scale out needs to be easily undertaken. Say, in the middle of the production day, while running the month end workload?

Intelligent, automated data tiering, locality and balancing via post-process techniques like Mapreduce is another key requirement. As any database working set grows over time, ie: more users will mean more queries, new tables, indexes, aggregations, etc. So the ability to maintain a responsive I/O profile via SSD, as more I/O is periodically obtained from disk, will be key. If all VMs are then able to get local access to their data via SSD from a global storage fabric so much the better. While we are here, consider how you would migrate to a new(er) hardware fleet with and without a distributed storage fabric. Far easier to just drop in units of converged compute/storage and then migrate VMs to it. Compare how that would work with a large white box server estate spread across numerous racks in a DC? There’s yet another aspect of economics to all this. In that auto tiering of the storage layer means the current “working set” data is held at the most performant (and by comparison more expensive) layer. While colder data sits on cheaper spinning disk.

Another advantage of a distributed storage fabric is one of data service features. Take point in time (PIT) backups of sharded DBs, which can sometimes be a complicated issue. In which case, a data service that supports VM centric snapshots of key VMs in a consistency group can avoid another potential pain point. Also, rapid cloning of preconfigured VMs will improve deployment times and speaks to the DevOps workflows that many IT shops have increasingly adopted. Consider how easy might it be to create dev/QA environments with production style data using such mechanisms? What about burst workloads? The ability to migrate VMs between public and private cloud would bring further benefits, both as a means to provide offsite backups or move VMs between geographies.

Bear in mind there isn’t 20+ years of ecosystem software (or even tribal knowledge perhaps?) in the NoSQL community – unlike in traditional RDBMS. For this reason continual monitoring is a major requirement. The ability to support a floor to ceiling overview of VMs, hypervisor and hardware platform in terms of performance, alerts and events is paramount. We mentioned briefly above how working set size and IO throughput could affect end user experience. So the ability to predict trends in such behaviour means timely decisions about when to scale or shard an application can be made.  No discussion of any DevOps processes is complete without including REST API and/or Powershell automation capabilities. Automation is key in terms of workflow agility, allowing routine tasks to be performed repeatedly with a well understood outcome. Dev/QA environments can benefit greatly from the features already described. In addition, via the API, developers can build self-service portal software allowing them to spin up new environments in a matter of minutes.

In previous roles I worked with customers running UNIX based failover clusters protecting traditional SQL RDBMS and ERP software. Think Solaris and SUN Cluster, underpinning Oracle and SAP installs.  While running this kind of ‘Big Iron’ was considered ‘state of the art’. Coming up fast on the inside was ‘Big Data’ and with it a complete rethink on how to achieve massive scale. Traditionally, systems had scaled vertically by adding more CPU and RAM to the host platform, and horizontally by adding system boards to a midframe chassis. This came at a price and often a staggering level of administrative complexity. While Web-Scale technologies may not have completely replaced this approach yet, large scale big iron systems will continue to become more niche as time goes on in my opinion.

So, coming back to the beginning of this post. HCI is not only about scaling just to support Big Data workloads, it’s also about creating lower time to value and radical ease of use synergies with the application that sits on top of the stack. Having a HCI platform designed from the ground up with the same underlying principles as modern Web-Scale applications, means we are able to remove the operational delays and complexity that tend to act as drag anchors in today’s rapid deployment environments. IT departments are then free to focus on innovations that help the business succeed.