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53 new things to look for in OpenStack Newton (plus a few more)

OpenStack Newton, the technology’s 14th release, shows just how far we’ve come: where we used to focus on basic things, such as supporting specific hypervisors or enabling basic SDN capabilities, now that’s a given, and we’re talking about how OpenStack has reached its goal of supporting cloud-native applications in all of their forms — virtual machines, containers, and bare metal.

There are hundreds of changes and new features in OpenStack Newton, and you can see some of the most important in our What’s New in OpenStack Newton webinar.  Meanwhile, as we do with each release, let’s take a look at 53 things that are new in OpenStack Newton.

openstack_main_services-svg

Compute (Nova)

  1. Get me a network enables users to let OpenStack do the heavy lifting rather than having to understand the underlying networking setup.
  2. A default policy means that users no longer have to provide a full policy file; instead they can provide just those rules that are different from the default.
  3. Mutable config lets you change configuration options for a running Nova service without having to restart it.  (This option is available for a limited number of options, such as debugging, but the framework is in place for this to expand.)
  4. Placement API gives you more visibility into and control over resources such as Resource providers, Inventories, Allocations and Usage records.
  5. Cells v2, which enables you to segregate your data center into sections for easier manageability and scalability,has been revamped and is now feature-complete.

Network (Neutron)

  1. 802.1Q tagged VM connections (VLAN aware VMs) enables VNFs to target specific VMs.
  2. The ability to create VMs without IP Address means you  can create a VM with no IP address and specify complex networking later as a separate process.
  3. Specific pools of external IP addresses let you optimize resource placement by controlling IP decisions.
  4. OSProfiler support lets you find bottlenecks and troubleshoot interoperability issues.
  5. No downtime API service upgrades

Storage (Cinder, Glance, Swift)

Cinder

  1. Microversions let developers can add new features you can access without breaking the main version.
  2. Rolling upgrades let you update to Newton without having to take down the entire cloud.
  3. enabled_backends config option defines which backend types are available for volume creation.
  4. Retype volumes from encrypted to not encrypted, and back again after creation.
  5. Delete volumes with snapshots using the cascade feature rather than having to delete the snapshots first.
  6. The Cinder backup service can now be scaled to multiple instances for better reliability and scalability.

Glance

  1. Glare, the Glance Artifact Repository, provides the ability to store more than just images.
  2. A trust concept for long-lived snapshots makes it possible to avoid errors on long-running operations.
  3. The new restrictive default policy means that all operations are locked down unless you provide access, rather than the other way around.

Swift

  1. Object versioning lets you keep multiple copies of an individual object, and choose whether to keep all versions, or just the most recent.
  2. Object encryption provides some measure of confidentiality should your disk be separated from the cluster.
  3. Concurrent bulk-deletes speed up operations.

Other core projects (Keystone, Horizon)

Keystone

  1. Simplified configuration setup
  2. PCI support of password configuration options
  3. Credentials encrypted at rest

Horizon

  1. You can now exercise more control over user operations with parameters such as IMAGES_ALLOW_LOCATION, TOKEN_DELETE_DISABLED, LAUNCH_INSTANCE_DEFAULTS
  2. Horizon now works if only Keystone is deployed, making it possible to use Horizon to manage a Swift-only deployment.
  3. Horizon now checks for Network IP availability rather than enabling users to set bad configurations.
  4. Be more specific when setting up networking by restricting the CIDR range for a user private network, or specify a fixed IP or subnet when creating a port.
  5. Manage Consistency Groups.

Containers (Magnum, Kolla, Kuryr)

Magnum

  1. Magnum is now more about container orchestration engines (COEs) than containers, and can now deploy Swarm, Kubernetes, and Mesos.
  2. The API service is now protected by SSL.
  3. You can now use Kubernetes on bare metal.
  4. Asynchronous cluster creation improves performance for complex operations.

Kolla

  1. You can now use Kolla to deploy containerized OpenStack to bare metal.

Kuryr

  1. Use Neutron networking capabilities in containers.
  2. Nest VMs through integration with Magnum and Neutron.

Additional projects (Heat, Ceilometer, Fuel, Murano, Ironic, Community App Catalog, Mistral)

Heat

  1. Use DNS resolution and integration with an external DNS.
  2. Access external resources using the external_id attribute.

Ceilometer

  1. New REST API that makes it possible to use services such as Gnocchi rather than just interacting with the database.
  2. Magnum support.

FUEL

  1. Deploy Fuel without having to use an ISO.
  2. Improved life cycle management user experience, including Infrastructure as Code.
  3. Container-based deployment possibilities.

Murano

  1. Use the new Application Development Framework to build more complex applications.
  2. Enable users to deploy your application across multiple regions for better reliability and scalability.
  3. Specify that when resources are no longer needed, they should be deallocated.

Ironic

  1. You can now have multiple nova-compute services using Ironic without causing duplicate entries.
  2. Multi-tenant networking makes it possible for more than one tenant to use ironic without sharing network traffic.
  3. Specify granular access restrictions to the REST API rather than just turning it off or on.

Community App Catalog

  1. The Community App Catalog now uses Glare as its backend, making it possible to more easily store multiple application types.
  2. Use the new v2 API to add and manage assets directly, rather than having to go through gerrit.
  3. Add and manage applications via the Community App Catalog website.

CoreOS Stackanetes puts OpenStack in containers for easy management

CoreOS Stackanetes puts OpenStack in containers for easy management

Stackanetes uses Kubernetes to deploy OpenStack as a set of containerized apps, simplifying management of OpenStack components

The ongoing effort to make OpenStack easier to deploy and maintain has received an assist from an unexpected source: CoreOS and its new Stackanetes project, announced today at the OpenStack Summit in Austin.

Containers are generally seen as a lighter-weight solution to many of the problems addressed by OpenStack. But CoreOS sees Stackanetes as a vehicle to deliver OpenStack’s benefits — an open source IaaS with access to raw VMs — via Kubernetes and its management methodology.

A rich dev and test toolchain, collaborative end-to-end workflow, and improved Windows support put Chef

OpenStack in Kubernetes

Kubernetes, originally created by Google, manages containerized applications across a cluster. Its emphasis is on keeping apps healthy and responsive with a minimal amount of management. Stackanetes uses Kubernetes to deploy OpenStack as a set of containerized applications, one container for each service.

The single biggest benefit, according to CoreOS, is “radically simplified management of OpenStack components,” a common goal of most recent  OpenStack initiatives.

But Stackanetes is also a “single platform for consistently managing both IaaS and container workloads.” OpenStack has its own container management service, Magnum, used mainly as an interface to run Docker and, yes, Kubernetes instances within OpenStack. Stackanetes stands this on its head, and OpenStack becomes another containerized application running alongside all the rest in a cluster.

Easy management = admin appeal

Other projects that deploy OpenStack as a containerized service have popped up but have taken a different approach. Kolla, an OpenStack “big tent” project, uses Docker containers and Ansible playbooks to deploy an OpenStack configuration. The basic deployment is “highly opinionated,” meaning it comes heavily preconfigured but can be customized after deployment.

Stackanetes is mostly concerned with making sure individual services within OpenStack remain running — what CoreOS describes as the self-healing capacity. It’s less concerned with under-the-hood configurations of individual OpenStack components — which OpenStack has been trying to make less painful.

One long-standing OpenStack issue that Stackanetes does try to address is upgrades to individual OpenStack components. In a demo video, CoreOS CEO Alex Polvi showed how Stackanetes could shift workloads from nodes running an older version of the Horizon service to nodes running a newer version. The whole process involved only a couple of clicks.

With Stackanetes, CoreOS is betting more people would rather use Kubernetes as a deployment and management mechanism for containers than OpenStack. At the least, Kubernetes gives admins a straightforward way to stand up and manage the pieces of an OpenStack cluster — and that, by itself, has admin appeal

Why Red Hat’s OpenShift, not OpenStack, is making waves with developers

Red Hat has historically whiffed with developers. But, its PaaS offering, OpenShift, may mark a new era for the open source giant.

Developers may be the new kingmakers in the enterprise, to borrow Redmonk’s phrase, but they’ve long ignored the enterprise open source leader, Red Hat. Two years ago, I called out Red Hat’s apparent inability to engage the very audience that would ensure its long-term relevance. Now, there are signs that Red Hat got the message.

And, no, I’m not talking about OpenStack. Though Red Hat keeps selling OpenStack (seven of its top-30 deals last quarter included OpenStack, according to Red Hat CEO Jim Whitehurst), it’s really OpenShift, the company’s Platform-as-a-Service (PaaS) offering, that promises a bright, developer-friendly future for Red Hat.

redhatLooking beyond OpenStack

Red Hat continues to push OpenStack, and rightly so—it’s a way for Red Hat to certify a cloud platform just as it once banked on certifying the Linux platform. There’s money in assuring risk-averse CIOs that it’s safe to go into the OpenStack cloud environment.

Even so, as Whitehurst told investors in June, OpenStack is not yet “material” to the company’s overall revenue, and generally generates deals under $100,000. It will continue to grow, but OpenStack adoption is primarily about telcos today, and that’s unlikely to change as enterprises grow increasingly comfortable with public IaaS and PaaS options. OpenStack feels like a way to help enterprises avoid the public cloud and try to dress up their data centers in the fancy “private cloud” lingo.

OpenShift, by contrast, is far more interesting.

OpenShift, after all, opens Red Hat up to containers and all they mean for enterprise developer productivity. It’s also a way to pull through other Red Hat software like JBoss and Red Hat Enterprise Linux, because Red Hat’s PaaS is built on these products. OpenShift has found particular traction among sophisticated financial services companies that want to get in early on containers, but the list of customers includes a wide range of companies like Swiss Rail, BBVA, and many others.

More and faster

To be clear, Red Hat still has work to do. According to Gartner’s most recent Magic Quadrant for PaaS, Salesforce and Microsoft are still a ways ahead, particularly in their ability to execute their vision:

mqdrnt.jpg

Still, there are reasons to think Red Hat will separate itself from the PaaS pack. For one thing, the company is putting its code where it hopes its revenue will be. Red Hat learned long ago that, to monetize Linux effectively, it needed to contribute heavily. In similar fashion, only Google surpasses Red Hat in Kubernetes code contributions, and Docker Inc. is the only company to contribute more code to the Docker container project.

Why does this matter? If you’re an enterprise that wants a container platform then you’re going to trust those vendors that best understand the underlying code and have the ability to influence its direction. That’s Red Hat.

Indeed, one of the things that counted against Red Hat in Gartner’s Magic Quadrant ranking was its focus on Kubernetes and Docker (“Docker and Kubernetes have tremendous potential, but these technologies are still young and evolving,” the report said). These may be young and relatively immature technologies, but all signs point to them dominating a container-crazy enterprise world for many years to come. Kubernetes, as I’ve written, is winning the container management war, putting Red Hat in pole position to benefit from that adoption, especially as it blends familiar tools like JBoss with exciting-but-unfamiliar technologies like Docker.

Red Hat has also been lowering the bar for getting started and productive with OpenShift, as Serdar Yegulalp described. By focusing on developer darlings like Docker and Kubernetes, and making them easily consumable by developers and more easily run by operations, Red Hat is positioning itself to finally be relevant to developers…and in a big way.

OpenStack’s latest release focuses on scalability and resilience

OpenStack, the massive open source project that helps enterprises run the equivalent of AWS in their own data centers, is launching the 14th major version of its software today. Newton, as this new version is called, shows how OpenStack has matured over the last few years. The focus this time is on making some of the core OpenStack services more scalable and resilient. In addition, though, the update also includes a couple of major new features. The project now better supports containers and bare metal servers, for example.

In total, more than 2,500 developers and users contributed to Newton. That gives you a pretty good sense of the scale of this project, which includes support for core data center services like compute, storage and networking, but also a wide range of smaller projects.

As OpenStack Foundation COO Mark Collier told me, the focus with Newton wasn’t so much on new features but on adding tools for supporting new kinds of workloads.

Both Collier and OpenStack Foundation executive director Jonathan Bryce stressed that OpenStack is mostly about providing the infrastructure that people need to run their workloads. The project itself is somewhat agnostic as to what workloads they want to run and which tools they want to use, though. “People aren’t looking at the cloud as synonymous with [virtual machines] anymore,” Collier said. Instead, they are mixing in bare metal and containers as well. OpenStack wants to give these users a single control plane to manage all of this.

Enterprises do tend to move slowly, though, and even the early adopters that use OpenStack are only now starting to adopt containers. “We see people who are early adopters who are running container in production,” Bryce told me. “But I think OpenStack or not OpenStack, it’s still early for containers in production usage.” He did note, however, that he is regularly talks to enterprise users who are looking at how they can use the different components in OpenStack to get to containers faster. 
networktopology

Core features of OpenStack, including the Nova compute service, as well as the Horizon dashboard and Swift object/blob store, have now become more scalable. The Magnum project for managing containers on OpenStack, which already supported Docker Swarm, Kubernetes and Mesos, now also allows operators to run Kubernetes clusters on bare metal servers, while the Ironic framework for provisioning those bare metal servers is now more tightly integrated with Magnuma and also now supports multi-tenant networking.

The release also includes plenty of other updates and tweaks, of course. You can find a full (and fully overwhelming) rundown of what’s new in all of the different projects here.

With this release out of the door, the OpenStack community is now looking ahead to the next release six months form now. This next release will go through its planning stages at the upcoming OpenStack Summit in Barcelona later this month and will then become generally available next February.

AppFormix now helps enterprises monitor and optimize their virtualized networks

AppFormix helps enterprises, including the likes of Rackspace and its customers, monitor and optimize their OpenStack- and container-based clouds. The company today announced that it has also now added support for virtualized network functions (VNF) to its service.

Traditionally, networking was the domain of highly specialized hardware, but increasingly, it’s commodity hardware and software performing these functions (often for a fraction of the cost). Almost by default, however, networking functions are latency sensitive, especially in the telco industry, which is one of the core users of VNF and also makes up a large number of OpenStack’s users. Using commodity hardware, however, introduces new problems, including increased lag and jitter.

AppFormix co-founder and CEO Sumeet Singh tells me that his company’s service can now reduce jitter by up to 70 percent. “People are just starting to roll out VNFs and as telcos move from hardware to software, that’s where they run into this problem,” he noted. “Our software is designed as this real-time system where we are able to analyze how everything is performing and do optimization based on this analysis.”

For VNF, this often means modifying how workloads are placed and how resources are allocated. Interestingly, AppFormix’s research showed that CPU allocations have very little influence on jitter. Instead, it’s all about how you use the available cache and memory. It’s controlling cache allocations correctly that allows Appformix to reduce jitter.

Singh stressed that it’s not just telcos that can benefit from this but also e-commerce sites and others who want to be able to offer their users a highly predictable experience.

The new feature is now available as part of AppFormix’s overall cloud optimization platform, which currently focuses on OpenStack and Kubernetes deployments.

Building a Highly Available OpenStack Cloud

I Building a Highly Available OpenStack Cloud Computing some details in my previous post, about RPC-R’s reference architecture. Here, I want to drill down even more into how we worked with Red Hat to build high availability into the control plane of the RPC-R solution.

As we know, component failures will happen in a cloud environment, particularly as that cloud grows in scale. However, users still expect their OpenStack services and APIs will be always on and always available, even when servers fail. This is particularly important in a public cloud or in a private cloud where resources are being shared by multiple teams.

The starting point for architecting a highly available OpenStack cloud is the control plane. While a control plane outage would not disrupt running workloads, it would prevent users from being able to scale out or scale in in response to business demands. Accordingly, a great deal effort went into maintaining service uptime as we architected RPC-R, particularly the Red Hat OpenStack platform which is the OpenStack distribution at the core of RPC-R.

Red Hat uses a key set of open source technologies to create clustered active-active controller nodes for all Red Hat OpenStack Platform clouds. Rackspace augments that reference architecture with hardware, software and operational expertise to create RPC-R with our 99.99% API uptime guarantee. The key technologies for creating our clustered controller nodes are:

  • Pacemaker – A cluster resource manager used to manage and monitor the availability of OpenStack components across all nodes in the controller nodes cluster
  • HAProxy – Provides load balancing and proxy services to the cluster (Note that while HAProxy is the default for Red Hat OpenStack Platform, RPC-R uses F5 hardware load balancers instead)
  • Galera – Replicates the Red Hat OpenStack Platform database across the cluster

control-plane

Putting it all together, you can see in the diagram above that redundant instances of each OpenStack component run on each controller node in a collapsed cluster configuration managed by Pacemaker. As the cluster manager, Pacemaker monitors the state of the cluster and has responsibility for failover and failback of services in the event of hardware and software failures. This includes coordinating restart of services to ensure that startup is sequenced in the right order and takes into account service dependencies.

A three node cluster is the minimal size in our RPC-R reference architecture to ensure a quorum in the event of a node failure. A quorum defines the minimal number of nodes that must function for the cluster itself to remain functional. Since a quorum is defined as half the nodes + 1, three nodes is the smallest feasible cluster you can have. Having at least three nodes also allows Pacemaker to compare the content of each node in the cluster and in the event that inconsistencies are found, a majority rule algorithm can be applied to determine what should be the correct state of each node.

While Pacemaker is used to manage most of the OpenStack services, RPC-R uses the Galera multi-master database cluster manager to replicate and synchronize the MariaDB based OpenStack database running on each controller node. MariaDB is a community fork of MySQL that is used in a number of OpenStack distributions, including Red Hat OpenStack platform.

database read write

Using Galera, we are able to create an active-active OpenStack database cluster and do so without the need for shared storage. Reads and writes can be directed to any of the controller nodes and Galera will synchronize all database instances. In the event of a node failure, Galeria will handle failover and failback of database nodes.

By default, Red Hat OpenStack Platform uses HAProxy to load balance API requests to OpenStack services running in the control plane. In this configuration, each controller node runs an instance of HAProxy and each set of services has its own virtual IP. HAProxy is also clustered together using Pacemaker to provide fault tolerance for the API endpoints.

pacemaker

As mentioned previously, Rackspace has chosen to use redundant hardware load balancers in place of HAProxy. Per the previous diagram, the Red Hat OpenStack Platform architecture is identical to RPC-R with the exception that we use F5 appliances in place of clustered HAProxy instances. We believe this option provides better performance and manageability for RPC-R customers.

external-network

An enterprise grade Private Cloud is achievable but requires a combination of the right software, a well thought-out architecture and operational expertise. We believe that Rackspace and Red Hat collaborating together is the best choice for customers looking for partners to help them build out such a solution.

OpenStack users: Backup your Cinder volumes to Google Cloud Storage

OpenStack Mitaka has just launched and we’re super excited about it. In collaboration with Red Hat and Biarca, we’ve developed an OpenStack Cinder backup driver for Google Cloud Storage, available in the Mitaka release.

Google joined the OpenStack Foundation in July 2015, when we announced Kubernetes integration with OpenStack. Our work on Mitaka is the next step on our roadmap to making Google Cloud Platform a seamless public cloud complement for OpenStack environments. Backup and recovery services represent one of the most costly and complex aspects of large scale infrastructure management. OpenStack provides an efficient mechanism for allocation and management of persistent block storage through Cinder. In an OpenStack deployment, Cinder volumes house virtual machine data at rest as well as, potentially, the operating system boot device. In production deployments, it’s critical that this persistent data is protected as part of a comprehensive business continuity and disaster recovery strategy. To satisfy this requirement, Cinder provides a backup service that includes a backup driver specification allowing storage vendors to add support for additional backup targets. This is where we come in. The addition of highly durable and available cloud-scale object storage allows organizations to shift from bulk commodity storage for backup to a more operationally efficient and cost-effective architecture, all while avoiding additional capital expenditures and the complexity of managing storage device scale out. The traditional barrier to adoption for object storage is the engineering effort required to adapt existing software and systems, designed for either file or block storage access, to object store native REST interfaces. The Cinder backup driver model provides the potential to abstract this engineering complexity for OpenStack users. As long as an appropriate backup driver is installed, the backup target works with Cinder as intended. Our Openstack Cinder backup driver is included as part of the standard Cinder backup driver set in Mitaka and requires minimal setup to get up and running. Full Cinder backup functionality was successfully tested with the Cloud Storage driver against 1GB, 5GB and 10GB Cinder volume sizes. In addition, the driver provides the following user configurable parameters to allow administrators to tune the installation:

Parameter Purpose
backup_gcs_credential_file Denotes the full path of the json file of the Google service account (downloaded from the Google Developer Console in step 3)
backup_gcs_bucket GCS bucket name to use for backup. Please refer to the official bucket naming guidelines.
backup_gcs_driver Used for selecting the Google backup driver
backup_gcs_project_id Denotes the project ID where the backup bucket will be created
backup_gcs_object_size The size in bytes of GCS backup objects. default: 52428800 bytes
backup_gcs_block_size The change tracking size for incremental backup, in bytes. backup_gcs_object_size has to be a multiple of backup_gcs_block_size default: 327678 bytes
backup_gcs_user_agent http user-agent string for the gcs API
backup_gcs_reader_chunk_size Chunk size for GCS object downloads in bytes. default: 2097152 bytes
backup_gcs_writer_chunk_size Chunk size for GCS object uploads in bytes. Pass in a value of -1 to cause the file to be uploaded as a single chunk. default: 2097152 bytes
backup_gcs_num_retries/td> Number of times to retry transfers. default: 3
backup_gcs_bucket_location Location of GCS bucket. default: ‘US’
backup_gcs_storage_class Storage class of GCS bucket. default: ‘NEARLINE’
backup_gcs_retry_error_codes List of GCS error codes for which to initiate a retry. default: [‘429’]
backup_gcs_enable_progress_timer Enable or Disable the timer to send the periodic progress notifications to Ceilometer when backing up the volume to the GCS backend storage. The default value is True to enable the timer. default: True

The Cinder backup driver works with any class of Cloud Storage, including our Google Cloud Storage Nearline archival option. Nearline provides the full durability of Standard storage, at a slightly lower level of availability and with a slightly higher latency and offers read performance of 4MB/TB stored, scaling with storage density. As an example, 3TB of backup data can be restored at 12MB/s. The low cost yet high performance of Nearline makes backing up Cinder volumes economical while offering the ability to quickly restore if necessary. If you’re running OpenStack, there’s no need to invest in additional storage systems or build out a second datacenter for backup and recovery. You can now use Cloud Storage in a hybrid scenario, optimized via the Cinder backup driver now available in Mitaka.

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