Category: Software defined storage

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148: GreyBeards talk software defined infrastructure with Anthony Cinelli and Brian Dean, Dell PowerFlex

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This is one of a series of podcasts the GreyBeards are doing with Dell PowerFlex software defined infrastructure. Today, we talked with Anthony Cinelli, Sr. Director Dell Technologies and Brian Dean, Technical Marketing for PowerFlex. We have talked with Brian before but this is the first time we’ve met Anthony. They were both very knowledgeable about PowerFlex and the challenges large enterprises have today with their storage environments.

The key to PowerFlex’s software defined solution is its extreme flexibility, which comes mainly from its architecture which offers scale-out deployment options ranging from HCI solutions to a fully disaggregated compute-storage environment, in seemingly any combination (see technical resources for more info). With this sophistication, PowerFlex can help consolidate enterprise storage across just about any environment from virtualized workloads, to standalone databases, big data analytics, as well as containerized environments and of course, the cloud. Listen to the podcast to learn more.

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To support this extreme flexibility, PowerFlex uses both client and storage software that can be configured together on a server (HCI) or apart, across compute and storage nodes to offer block storage. PowerFlex client software runs on any modern bare-metal or virtualized environment.

Anthony mentioned that one common problem to enterprises today is storage sprawl. Most large customers have an IT environment with sizable hypervisor based workloads, a dedicated database workload, a big data/analytics workload, a modern container based workload stack, an AI/ML/DL workload and more often than not, a vertical specific workload.

Each workload usually has their own storage system. And the problem with 4-7 different storage systems is cost, e.g., cost of underutilized storage. Typical to these environments, each storage system could be used at say, 60% utilization on average, but this will vary a lot between silos, leading to stranded capacity.

The main reason customers haven’t consolidated yet is because each silo has different performance characteristics. As a result, they end up purchasing excess capacity which increases cost and complexity, as a standard part of doing business.

To consolidate storage across these disparate environments requires a no-holds barred approach to IO performance, second to none, which PowerFlex can deliver. The secret to to its high levels of IO performance is RAID 10, deployed across a scale-out cluster. And PowerFlex clusters can range from 4 to 1000 or more nodes.

RAIID 10 mirrors data and spreads mirrored data across all drives and servers in a cluster or some subset. As a result, as you add storage nodes, IO performance scales up, almost linearly.

Yes, there can be other bottlenecks in clusters like this, most often networking, but with PowerFlex storage, IO need not be one of them. Anthony mentioned that PowerFlex will perform as fast as your infrastructure will support. So if your environment has 25 Gig Ethernet, it will perform IO at that speed, if you use 100 Gig Ethernet, it will perform at that speed.

In addition, PowerFlex offers automated LifeCycle Management (LCM), which can make having a 1000 node PowerFlex cluster almost as easy as a 10 node cluster. However to make use this automated LCM, one must run its storage server software on Dell PowerEdge servers.

Brian said adding or decommissioning PowerFlex nodes is a painless process. Because data is always mirrored, customers can remove any node, at any time and PowerFlex will automatically rebuild data across other nodes and drives. When you add nodes, those drives become immediately available to support more IO activity. Another item to note, because of RAID 10, PowerFlex mirror rebuilds happen very fast, as just about every other drive and node in the cluster (or subset) participates in the rebuild process.

PowerFlex supports Storage Pools. This partitions PowerFlex storage nodes and devices into multiple pools of storage used to host volume IO and data Storage pools can be used to segregate higher performing storage nodes from lower performing ones so that some volumes can exclusively reside on higher (or lower) performing hardware.

Although customers can configure PowerFlex to use all nodes and drives in a system or storage pool for volume data mirroring, PowerFlex offers other data placement alternatives to support high availability.

PowerFlex supports Protection Domains which are subsets or collections of storage servers and drives in a cluster where volume data will reside. This will allow one protection domain to go down while others continue to operate. Realize that because volume data is mirrored across all devices in a protection domain, it will take lots of nodes or devices to go down before a protection domain is out of action.

PowerFlex also uses Fault Sets, which are a collection of storage servers and their devices within a Protection Domain, that will contain one half of a volume’s data mirror. PowerFlex will insure that a primary and its mirror copy of volume’s data will not both reside on the same fault set. A fault set could be a rack of servers, multiple racks, all PowerFlex storage servers in an AZ, etc. With fault sets, customer data will always reside across a minimum of two fault sets, and if any one goes down, data is still available.

PowerFlex also operates in the cloud. In this case, customers bring their own PowerFlex software and deploy it over cloud compute and storage.

Brian mentioned that anything PowerFlex can do such as reconfiguring servers, can be done through RESTful/API calls. This can be particularly useful in cloud deployments as above, if customers want to scale up or down IO performance automatically.

Besides block services, PowerFlex also offers NFS/CIFS-SMB native file services using a File Node Controller. This frontends PowerFlex storage nodes to support customer NFS/SMB file access to PowerFlex data.

Anthony Cinelli, Sr. Director Global PowerFlex Software Defined & MultiCloud Solutions

Anthony Cinelli is a key leader for Dell Technologies helping drive the success of our software defined and multicloud solutions portfolio across the customer landscape. Anthony has been with Dell for 13 years and in that time has helped launch our HCI and Software Defined businesses from startup to the multi-billion dollar lines of business they now represent for Dell.

Anthony has a wealth of experience helping some of the largest organizations in the world achieve their IT transformation and multicloud initiatives through the use of software defined technologies.

Brian Dean, Dell PowerFlex Technical Marketing

Brian is a 16+ year veteran of the technology industry, and before that spent a decade in higher education. Brian has worked at EMC and Dell for 7 years, first as Solutions Architect and then as TME, focusing primarily on PowerFlex and software-defined storage ecosystems.

Prior to joining EMC, Brian was on the consumer/buyer side of large storage systems, directing operations for two Internet-based digital video surveillance startups.

When he’s not wrestling with computer systems, he might be found hiking and climbing in the mountains of North Carolina.

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126: GreyBeards talk k8s storage with Alex Chircop, CEO, Ondat

Keith and I had an interesting discussion with Alex Chircop (@chira001), CEO of Ondat, a kubernetes storage provider. They have a high performing system, laser focused on providing storage for k8s stateful container applications. Their storage is entirely containerized and has a number of advanced features for data availability, performance and security that developers need the run stateful container apps. Listen to the podcast to learn more.

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Ondat-21-12-transcriptDownload

We started by asking Alex how Ondats different from all the other k8s storage solutions out there today (which we’ve been talking with lately). He mentioned three crucial capabilities:

  • Ondat was developed from the ground up to run as k8s containers. Doing this would allow any k8s distribution to run their storage to support stateful container apps. .
  • Ondat was designed to allow developers to run any possible container app. Ondat supports both block as well as file storage volumes.
  • Ondat provides consistent, superior performance, at scale, with no compromises. Sophisticated data placement insures that data is located where it is consumed and their highly optimized data path provides low-latency access that data storage.

Ondat creates a data mesh (storage pool) out of all storage cluster nodes. Container volumes are carved out of this data mesh and at creation time, data and the apps that use them are co-located on the same cluster nodes.

At volume creation, Dev can specify the number of replicas (mirrors) to be maintained by the system. Alex mentioned that Ondat uses synchronous replication between replica clusters nodes to make sure that all active replica’s are up to date with the last IO that occurred to primary storage.

Ondat compresses all data that goes over the network as well as encrypts data in flight. Dev can easily specify that the data-at-rest also be compressed and/or encrypted. Compressing data in flight helps supply consistent performance where networks are shared.

Alex also mentioned that they support both the 1 reader/writer, k8s block storage volumes as well as multi-reader/multi-writer, k8s file storage volumes for containers.

In Ondat each storage volume includes a mini-brain used to determine primary and replica data placement. Ondat also uses desegregated consensus to decide what happens to primary and replica data after a k8s split cluster occurs. After a split cluster, isolated replica’s are invalidated and replicas are recreate, where possible, in the surviving nodes of the cluster portion that holds the primary copy of the data.

Also replica’s can optionally be located across AZs if available in your k8s cluster. Ondat doesn’t currentlysupport replication across k8s clusters.

Ondat storage works on any hyperscaler k8s solution as well as any onprem k8s system. I asked if Ondat supports VMware TKG and Alex said yes but when pushed mentioned that they have not tested it yet.

Keith asked what happens when things go south, i.e., an application starts to suffer worse performance. Alex said that Ondat supplies system telemetry to k8s logging systems which can be used to understand what’s going on. But he also mentioned they are working on a cloud based, Management-aaS offering, to provide multi-cluster operational views of Ondat storage in operation to help understand, isolate and fix problems like this.

Keith mentioned he had attended a talk by Google engineers that developed kubernetes and they said stateful containers don’t belong under kubernetes. So why are stateful containers becoming so ubiquitous now.

Alex said that may have been the case originally but k8s has come a long way from then and nowadays as many enterprises shift left enterprise applications from their old system environment to run as containers they all require state for processing. Having that stateful information or storage volumes accessible directly under k8s makes application re-implementation much easier.

What’s a typical Ondat configuration? Alex said there doesn’t appear to be one. Current Ondat deployments range from a few 100 to 1000s of k8s cluster nodes and 10 to 100s of TB of usable data storage.

Ondat has a simple pricing model, licensing costs are determined by the number of nodes in your k8s cluster. There’s different node pricing depending on deployment options but other than that it’s pretty straightforward.

Alex Chircop, CEO Ondat

Alex Chircop is the founder and CEO of Ondat (formerly StorageOS), which makes it possible to easily deploy and manage stateful Kubernetes applications with persistent data volumes. He also serves as co-chair of the CNCF (Cloud Native Computing Foundation) Storage Technical Advisory Group.

Alex comes from a technical background working in IT that includes more than 10 years with Nomura and Goldman Sachs.

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125: GreyBeard talk K8s storage with Tad Lebeck, US CTO for ionir

We had some technical difficulties with Matt getting on the podcast so, Ray had to fly solo. This month we continue our investigations into K8s storage with a discussion with Tad Lebeck (@TadLebeck) US CTO, ionir, a software defined storage system that only runs under K8s. ionir Kubernetes Data Services platform is an outgrowth of Reduxio a “tin-wrapped” software defined storage system which pivoted to K8s as the environment to target and left the tin behind.

ionir offers a deduplicating, continuous data protection storage system for PVs (persistent volumes) under K8s that uses 3 way mirroring, across data nodes for data protection. Their solution offers a number of unique services that we haven’t seen in other K8s storage systems. Listen to the podcast to learn more.

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Ionir-21-11-transcriptDownload

Tad opened with a long spiel on what ionir is and we spent the next 40 minutes unpacking that to understand what exactly they were doing.

Let’s start with why stateful containers are all the rage these days. Tad had a slightly different rationale than we’ve heard before. From his perspective, it all comes from current enterprise applications that used database servers/machines. As these apps are re-factored to run as K8s containerized micro services, developers need and want their data be containerized right along with the application.

ionir constructs a block storage system across K8s data nodes or K8s worker nodes with direct attached storage. In the cloud, this storage can be ephemeral (storage that only exists as long as the compute instance operates) or normal block storage (e.g., EBS in AWS). It’s unclear how ephemeral works on-prem. But in any case, they cluster together a set of data nodes into one massive block storage and map PVs onto that. K8s data nodes can be added to the ionir cluster while it’s operating.

As mentioned earlier, they use 3-way mirroring for data protection and ionir insures the 3 copies are stored on different data nodes. As such, when one data node goes down, copies of PV data are available from the other 2 nodes and the data can then be rewritten elsewhere to insure 3-way mirroring continues. We suppose this means a minimum configuration requires at least 3 data nodes.

ionir also provides deduplicating block storage, which should theoretically reduce physical storage footprint for any PV. Data blocks are deduplicated across the cluster. ionir also has a metadata service (also 3 way replicated, to different data space) that records the manifest for all blocks associated with a PV, their hashes and (logical/physical) locations.

There was no mention of data compression or encryption so those are probably not present. We find deduplication very effective for backup storage but less effective for primary storage. Any deduplication ratio for ionir primary storage is likely specific to data being stored, i.e. columnar database, row database, text, office files, etc. Each of these would likely have different dedupe ratios for primary storage.

Furthermore, ionir supplies continuous data protection (CDP) for PV data. PV data written to ionir is immutable, i.e., never modified AND they keep previous versions of PV blocks in storage until they age out. This allows ionir to provide any prior version (well most recent ones) of a PV. ionic uses a timestamp to distinguish different PV versions. So, if ransomware attacked your site, users could ask for a PV version just prior to the time of the attack and you’d have that version of the PV to restart operations. Customer’s can limit how far back ionir saves prior versions of blocks for PVs.

Having CDP for PVs, makes DevOps qualification and testing significantly faster. Normally DevOps would need to copy production data to test environments in order to validate new app code. But ionir can easily instantiate a separate copy of any PV (at any time in their saved set) in a matter of seconds. This can take DevOps deployment testing down from days to minutes or less.

In addition, ionir can teleport PV data to other, remote K8s clusters running ionir. Essentially, this copies PV metadata and it’s “hot” blocks over to any remote ionir cluster. During teleportation, the remote cluster can access PV data as soon as all PV metadata has been copied. The remote site accesses this PV data from the originating cluster (albeit much slower than accesses within the cluster) while “hot” blocks are being copied. Any writes, at the remote site, to PV data would be considered new data, deduplicated at the remote site, and only available at the remote site. Somewhat surprisingly, all of the PV’s data is never copied to the remote system, leaving the PV in a permanent teleported access mode.

Not sure we like the implications of teleporting PVs, from a data integrity perspective. It does make for near-instant access to PV data from other clusters and offers a solution to data gravity (it takes forever to move TB of data across the web), it’s incomplete, as the data is never fully copied to the remote site. Once hot blocks have been copied, remote cluster PV access should run faster. But If there’s 20% of the requested blocks, not in the heat map, those IOs will take 100s mseclonger, depending on wire distance between the sites, to perform. And the write’s at the remote site cause the two copies (one at source site and one at remote site) of the PV to diverge.

Their storage system is priced on a per data node basis which makes it easy to price out their various deployment options. And it works on any K8s standard environment, although Tad admits they haven’t tested VMware Tanzu yet, but they have tested it on GCP, Microsoft Azure, AWS, and Red Hat OpenShift.

They offer a fully functional free trial of ionir storage, only capped at the number of data nodes in use. So, if you only need a small amount of storage (ok 3 data nodes with 24 14TB SSDs each make for large amount of storage) for your K8s environment, you can probably run forever on the free version.

Tad Lebeck, US CTO, ionir

Tad Lebeck is a global technology executive with over two decades of experience in startups and large vendors. Prior to ionir, he founded and led Nuvoloso, an innovator in Kubernetes data services. Earlier, Lebeck served as CTO at Huawei Symantec Technologies, Vice President at Symantec/Veritas, co-founder/CTO at Invio, and CTO at Legato Systems, where he helped create the modern enterprise data-protection market.

Tad was a founding member of the SNIA Technical Council. He earned an MS/CS from the University of Wisconsin, and a combined MBA from the Columbia, London, and HKU Schools of Business.

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124: GreyBeards talk k8s storage orchestration using CNCF Rook Project with Sébastien Han & Travis Nielsen, Red Hat

Stateful containers are becoming a hot topic these days so we thought it a good time to talk to the CNCF (Cloud Native Computing Foundation) Rook team about what they are doing to make storage easier to use for k8s container apps. CNCF put us into contact with Sébastien Han (@leseb_), Ceph Storage Architect and Travis Nielsen (@STravisNielsen), both Principal Software Engineers at Red Hat and active on the Rook project. Rook is a CNCF “graduated” open source project just like Kubernetes, Prometheus, ContainerD, etc., this means it’s mature enough to run production workloads.

Rook is used to configure, deploy and manage a Red Hat Ceph(r) Storage cluster under k8s. Rook creates all the k8s deployment scripts to set up a Ceph Storage cluster as containers, start it and monitor its activities. Rook monitoring of Ceph operations can restart any Ceph service container or scale any Ceph services up/down as needed by container apps using its storage. Rook is not in the Ceph data path, but rather provides a k8s based Ceph control or management plane for running Ceph storage under k8s.

Readers may recall we talked to SoftIron, an appliance provider, for Ceph Storage in the enterprise for our 120th episode. Rook has another take on using Ceph storage, only this time running it under k8s,. Listen to the podcast to learn more.

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The main problem Rook is solving is how to easily incorporate storage services and stateful container apps within k8s control. Containerized apps can scale up or down based on activity and storage these apps use needs the same capabilities. The other option is to have storage that stands apart or outside k8s cluster and control. But then tho container apps and their storage have 2 (maybe more) different control environments. Better to have everything under k8s control or nothing at all.

Red Hat Ceph storage has been available as a standalone storage solutions for a long time now and has quite the extensive customer list, many with multiple PB of storage. Rook-Ceph and all of its components run as containers underneath k8s.

Ceph supports replication (mirroring) of data 1 to N ways typically 3 way or erasure coding for data protection and also supports file, block and object protocols or access methods. Ceph normally consumes raw block DAS for it’s backend but Ceph can also support a file gateway to NFS storage behind it. Similarly, Ceph can offers an object storage gateway option. But with either of these approaches, the (NFS or object) storage exists outside k8s scaling and resiliency capabilities and Rook management.

Ceph uses storage pools that can be defined using storage performance levels, storage data protection levels, system affinity, or any combination of the above. Ceph storage pools are mapped to k8s storage classes using the Ceph CSI. Container apps that want to use storage would issue a persistent volume claim (PVC) request specifying a Ceph storage class which would allocate the Ceph storage from the pool to the container.  

Besides configuring, deploying and monitoring/managing your Ceph storage cluster, Rook can also automatically upgrade your Ceph cluster for you. 

We discussed the difference between running Rook-Ceph within k8s and running Ceph outside k8s. Both approaches depend on Ceph CSI but with Rook, Ceph and all its software is all running under k8s control as containers and Rook manages the Ceph cluster for you. When it’s run outside 1) you manage the Ceph cluster and 2) Ceph storage scaling and resilience are not automatic. 

Sébastien Han, Principal Software Engineer, Ceph Architect, Red Hat

Sebastien Han currently serves as a Senior Principal Software Engineer, Storage Architect for Red Hat. He has been involved with Ceph Storage since 2011 and has built strong expertise around it.

Curious and passionate, he loves working on bleeding edge technologies and identifying opportunities where Ceph can enhance the user experience. He did that with various technology such as OpenStack, Docker.

Now on a daily basis, he rotates between Ceph, Kubernetes, and Rook in an effort to strengthen the integration between all three. He is one of the maintainers of Rook-Ceph.

Travis Nielson, Principal Software Engineer, Red Hat

Travis Nielsen is a Senior Principal Software Engineer at Red Hat with the Ceph distributed storage system team. Travis leads the Rook project and is one of the original maintainers, integrating Ceph storage with Kubernetes.

Prior to Rook, Travis was the storage platform tech lead at Symform, a P2P storage startup, and an engineering lead for the Windows Server group at Microsoft.

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107: GreyBeards talk MinIO’s support of VMware’s new Data Persistence Platform with AB Periasamy, CEO MinIO

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The GreyBeards have talked with Anand Babu (AB) Periasamy (@ABPeriasamy), CEO MinIO, before (see 097: GreyBeards talk open source S3… episode). And we also saw him earlier this year, at their headquarters for Storage Field Day 19 (SFD19) where AB gave a great discussion of what they were doing and how it worked (see MinIO’s SFD18 presentation videos).

The podcast runs ~26 minutes. AB is very technically astute and always a delight to talk with. He’s extremely knowledgeable about the cloud, containerized applications and high performing S3 compatible object storage. And now with MinIO and vSAN Data Persistence under VCF Tanzu, very knowledgeable about the virtualized IT environment as well. Listen to the podcast to learn more. [We’re trying out a new format placing the podcast up front. Let us know what you think; The Eds.]

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VMware VCF vSAN Data Persistence Platform with MinIO

Earlier this month VMware announced a new capability available with the next updates of vSAN, vSphere & VCF called the vSAN Data Persistence Platform. The Data Persistence Platform is a VMware framework designed to integrate stateful, independent vendor software defined storage services in vSphere. By doing so, VCF can provide API access to persistent storage services for containerized applications running under Tanzu Kubernetes (k8s) Grid service clusters.

At the announcement, VMware identified three object storage and one (Cassandra) database technical partners that had been integrated with the solution.  MinIO was an object storage, open source partner.

VMware’s VCF vSAN Data Persistence framework allows vCenter administrators to use vSphere cluster infrastructure to configure and deploy these new stateful storage services, like MinIO, into namespaces and enables app developers direct k8s API access to these storage namespaces to provide persistent, stateful object storage for applications. 

With VCF Tanzu and the vSAN Data Persistence Platform using MinIO, dev can have full support for their CiCd pipeline using native k8s tools to deploy and scale containerized apps on prem, in the public cloud and in hybrid cloud, all using VCF vSphere.

MinIO on the Data Persistence Platform

AB said MinIO with Data Persistence takes advantage of a new capability called vSAN Direct which gives vSAN almost JBOF types of IO control and performance. With MinIO vSAN Direct, storage and k8s cluster applications can co-reside on the same ESX node hardware so that IO activity doesn’t have to hop off host to be performed. In addition, can now populate ESX server nodes with lots (100s to 1000s?) of storage devices and be assured the storage will be used by applications running on that host.

As a result, MinIO’s object storage IO performance on VCF Tanzu is very good due to its use of vSAN Direct and MinIO’s inherent superior IO performance for S3 compatible object storage.

With MinIO on the VCF vSAN Data Persistence Platform, VMware takes over all the work of deploying MinIO software services on the VCF cluster. This way customers can take advantage of MiniO’s fully compatible S3 object storage system operating in their VCF cluster. For app developers they get the best of all worlds, infrastructure configured, deployed and managed by admins but completely controllable, scaleable and accessible through k8s API services.

If developers want to take advantage of MinIO specialized services such as data security or replication, they can do so directly using MinIOs APIs, just like they would when operating bare metal or in the cloud.

AB said the VMware development team was very responsive during development of Data Persistence. AB was surprised to see such a big company, like VMware, operate with almost startup like responsiveness. Keith mentioned he’s seen this in action as vSAN has matured very rapidly to a point of almost feature parity, with just about any storage system out there today .

With MinIO object storage, container applications that need PB of data, now have a home on VCF Tanzu. And it’s as easily usable as any public cloud storage. And with VCF Tanzu configuring and deploying the storage over its own infrastructure, and then having it all managed and administered by vCenter admins, its simple to create and use PB of object storage.

MinIO is already the most popular S3 compatible object storage provider for applications running in the cloud and on prem. And VMware is easily the most popular virtualization platform on the planet. Now with the two together on VCF Tanzu, there seems to be nothing in the way of conquering containerized applications running in IT as well.

With that, MinIO is available everywhere containers want to run, natively available in the cloud, on prem and hybrid cloud or running with VCF Tanzu everywhere as well.

AB Periasamy, CEO MinIO

AB Periasamy is the CEO and co-founder of MinIO. One of the leading thinkers and technologists in the open source software movement,

AB was a co-founder and CTO of GlusterFS which was acquired by RedHat in 2011. Following the acquisition, he served in the office of the CTO at RedHat prior to founding MinIO in late 2015.

AB is an active angel investor and serves on the board of H2O.ai and the Free Software Foundation of India.

He earned his BE in Computer Science and Engineering from Annamalai University.

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