104: GreyBeards talk new cloud defined (shared) storage with Siamak Nazari, CEO Nebulon

Ray has known Siamak Nazari (@NebulonInc), CEO Nebulon for three companies now but has rarely had a one (two) on one discussion with him. With Nebulon just emerging from stealth (a gutsy move during the pandemic), the GreyBeards felt it was a good time to get Siamak on the show to tell us what he’s been up to. Turns out he and Nebulon decided it was time to completely rethink/rearchitect shared storage for the new data center.

At his prior company, Siamak spent a lot of time with many customers discussing the problems they had dealing with the complexity of managing, provisioning and maintaining multiple shared storage arrays. Somewhere in all those discussions Siamak saw this as a problem that needed a radical solution. If we could just redo shared storage from the ground up, there might be a solution to all these problems.

Redefining shared storage

Nebulon’s new approach to shared storage starts with an SPU card which replaces SAS RAID cards in a server. But instead of creating SAS RAID groups, the SPU creates a shareable, enterprise class, pool of storage across a throng of servers.

They call a collection of servers with SPUs, Cloud Defined Storage (CDS) and it creates a Nebulon nPod. An nPod essentially consists of multiple servers with SPU cards, with or without attached SSD storage, that are provisioned, managed and monitored via the cloud. Nebulon nPod servers are elements or nodes of a shared storage pool across all interconnected SPU servers in a data center.

In an SPU server with local (SAS, SATA, NVMe) SSD storage, the SPU creates an erasure coded pool of storage which can be used to serve (SAS) LUNs to this or any other SPU attached server in the nPod. In a SPU server without local SSD storage, the SPU provides access to any other SPU server shared storage in the nPod. Nebulon nPods only works with flash storage, it doesn’t support spinning media.

The SPU can supply boot storage for its server. There’s no need to have the CPU running OS code to use nPod shared storage. Yes, the SPU needs power and an active PCIe bus to work, but the functionality of an SPU doesn’t require an operational OS to work. The SPU provides a SAS LUN interface to server CPUs.

Each SPU has dual port access to an inter-cluster (25GbE) interconnect that connects all SPUs to the nPod. The nPod inter-cluster protocol is proprietary but takes advantage of standard TCP/IP services across the network with standard 25GbE switching.

The SPU firmware insures that it stays connected as long as power is available to the server. Customers can have more than one SPU in a server but these would be used for more IO performance. Each SPU also has 32GB of NVRAM for caching purposes and it’s also used for power fail fault tolerance.

In the unlikely case that the server and SPU are completely down (e.g. power outage), clients can still access that SPUs data storage, if it was mirrored (see below). When the SPU server comes back up, it will be resynched with any data that had been changed.

Other Nebulon storage features

Nebulon supports data-at-rest encryption, compression and deduplication for customer data. That way customer data is never in plain text as it travels across the nPod or even within the server from the SPU to SSD storage. Also any customer data written to an nPod can be optionally mirrored and as noted above, is protected via erasure coding.

The SPU also supports snapshotting of customer LUN data. So clients can take copies of LUNs and use these for backups, test, dev, etc. SPUs also support asynchronous or synchronous replication between nPods. For synchronous replication and mirrored data, the originating host only sees the IO complete after the data has been received at the target SPU or nPod.

Metadata for the nPod that defines LUN configurations and which server has LUN data is kept across the cluster in each SPU. But metadata on the location of user data within a server is only kept in that server’s SPU.

We asked Siamak whether nPods support SCM (storage class memory). He said not yet, but they’re looking at SCM NVMe storage for use as a potential metadata and data cache for SPUs.

Nebulon Application Centric storage

All the above storage features are present in most enterprise class storage systems. But what sets Nebulon apart from all other shared storage arrays is that their control plane is entirely in the cloud. That is customers point their browser to Nebulon’s control plane and use it to configure, provision and manage the nPod storage pool. Nebulon supports application templates that can be used to configure nPod storage to support standardized applications, such as VMware VMs, MongoDB, persistent storage for K8S containers, bare metal Linux apps, etc.

With the nPod’s control plane in the cloud it makes provisioning, managing and monitoring storage services much more agile. Nebulon can literally roll out new control plane updatesy to their install base on an almost daily basis. Just like any other cloud based or SAAS application. Customers receive the updated nPod control plane functionality by simply refreshing their browser page.

Nebulon’s GoToMarket

Near the end of our podcast, we asked Siamak about how Nebulon was going to access the market. Nebulon’s goto market is to use server OEMs. That is, they have signed agreements with two (and working on a third) server vendors to sell SPU cards with Nebulon control plane access.

During server purchases, customers configure their servers but now along with SAS RAID card options they will now see an Nebulon SPU option. OEM server vendors will bundle SPU hardware and Nebulon control plane access along with all other server components such as CPU’s, SSDs, NICs, etc, This way, the customer will receive a pre-installed SPU card in their server and will be ready to configure nPod LUNs as soon as the server powers on in their network.

Nebulon will go GA in the 3rd quarter.

The podcast ran ~43 minutes. Siamak has always been a pleasure to talk with and is very knowledgeable about the problems customers have in today’s data center environments. Nebulon has given him and his team the way to rethink storage and address these serious issues. Matt and I had a good time talking with Siamak. Listen to the podcast to learn more.

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Siamak Nazari, CEO Nebulon

Siamak Nazari is the CEO and Co-founder of Nebulon. Siamak has over 25 years of experience working on distributed and highly available systems.

In his position as HPE Fellow and VP, he was responsible for setting technical direction for HPE 3PAR and its portfolio of software and hardware. He worked on HPE 3PAR technology from 2000 to 2018, responsible for designing and implementing distributed memory management and the high availability features of the system.

Prior to joining 3PAR, Siamak was the technical lead for distributed highly available Proxy Filesystem (pxfs) of Sun Cluster 3.0.

0102 GreyBeards talk big memory data with Charles Fan, CEO & Co-founder, MemVerge

It’s been a couple of months since we last talked with a startup, so the GreyBeards thought it was time. We reached out to Charles Fan (@CharlesFan14), CEO and Co-Founder of MemVerge to find out about their big memory solution or as Charles likes to call it, “software defined (big) memory”. Although neither Matt or I had ever talked with Charles before, he’s been just about everywhere in the storage industry throughout his career.

If you have been following my RayOnStorage blog you will have seen a post (Need memory, Intel’s Optane DC PM to the rescue) last year on Intel’s new Persistent Memory solutions using 3D XPoint, called Optane DC PM (data center, persistent memory) . At the announcement Intel made available a couple of ways customers could use Optane DC PM (PMem).

Optane DC PM primer

Native Optane DC PM access modes include:

  • A Memory Mode, which has Pmem emulating a large volatile memory space and uses a defined ratio of DRAM to PMem as a cache to access the Optane DC PM memory behind it.
  • An Application Direct (AppDirect) Mode which supports two sub-modes: a storage device mode that uses Pmem to emulate a persistent, 4KB block storage device; and a byte addressable, persistent memory address space mode that uses Pmem to emulate a large, non-volatile memory space . AppDirect memory content persists across boots, power failures and other system crashes.

Native PMem modes are selectected in the BIOS and are deployed at Boot time. Optane DC PM on a server can be split up into any of the three modes. And currently with Optane DC PM (Gen 1), a single server can have up to 6TB of DC PM which will go up to 8TB with Optane DC PM Gen 2 coming out later this year.

MemVerge Memory Machine

MemVerge has written a “software defined memory” service called the Memory Machine, that sits above the Intel Optane DC PM in server(s) and provides application access AND data services for PMem. .

Charles likens their Memory Machine to what VMware did for CPU cores, ie. they provide memory virtualization. This, Charles believes will bring on the age of Big Memory applications. He feels that PMem, with Memory Machine on top of it, will eliminate the need for high performance, tier 0 storage. Tier 0 storage is ~$10B market today, which he sees shifting from networked storage to PMem solutions. 

Memory Machine Data Services

One of the data services that the Memory Machine offers is a Pmem snapshot service. PMem thick or thin snapshots can be taken any (infinite) number of times (for thick snapshots storage space availability may limit their number) and can be taken up to once per minute. PMem thin snapshots take little time to accomplish and are very PMem space efficient but thick snapshots are a PMem to PMem copy of data, which will take longer to accomplish and will take double the memory of the original PMem being snapshot.

One significant use case for Pmem snapshots is for checkpoint crash recovery. Charles mentioned many securities and financial analysis firms use KDB as streaming data base service to monitor/analyze market activity and provide automated trading and other market services. These firms are always trying to gain an advantage through speed and reduced latency and as a result have moved their time sensitive processing to use in memory data structures/databases.

However, because checkpointing for crash recovery takes time, they usually checkpoint in memory databases only once a day (after market close) and maintain a log of database transactions on SSD. If there’s a system crash, they reload the last checkpoint and re-play all the transaction logs since that checkpoint to bring their in memory database back to the point of crash. Due to the number of transactions these firms do, this sort of crash recoverys can take hours.

With Memory Machine, these customers can take in memory checkpoints every minute and in the event of a crash, only have to re-play a minutes worth of transaction logs which could be done in no time to get back up

Other environments do similar checkpoint crash recoveries all of which could also take advantage of PMem snapshots to take more frequent checkpoints. Charles mentioned Rendering farms on the podcast but long scientific simulations (HPC) and others use checkpoints for crash recovery.

Another data (or application) service offered by Memory Machine is application cloning. Most in memory applications are single threaded. meaning they can only take advantage of a single CPU core (thread). In order to speed up processing, customers must shard (split up) or copy their database and application onto other servers/CPU/cores to provide more processing power. Memory Machine can use its thick or thin snapshots to clone applications in seconds.

Charles also mentioned that Memory Machine offers PMem dynamic reconfiguration. That is instead of having to make BIOS changes and re-boot server(s) to re-allocate PMem across different applications, Memory Machine is allocated 100% of the PMem at boot time but then, on demand, anytime its operating, operators using MemVerge’s GUI/CLI can carve Pmem up into any number of application memory spaces. That is as application demand for in memory data changes, operations can use the Memory Machine to re-allocate PMem to keep up.

Memory Machine also supports PMem clustering or scaling across servers. With the current 6TB (and soon 8TB) per server PMem limit, some customer applications still run out of memory. Memory Machine is able to cluster or aggregate PMem across up to 32 servers to support a single larger, PMem address space of 192TB (Gen 1) or 256TB (Gen 2) DC PM. The Memory Machine uses an RDMA (RoCE Ethernet or InfiniBand) cluster interconnect which adds ~1 microsecond of overhead to access PMem in another server. This comes with PMem automatic data tiering using DRAM, local (on the server) PMem and remote (across cluster interconnect) PMem.

Charles mentioned another data service provided by Memory Machine is (Synch or Asynch) replication. One use case for replication is to create a Pub-Sub service for market data.

Charles believes that in memory databases and data processing workloads are just starting to become popular these days. Besides KDB and rendering, other data processing such as AI training/inferencing, Reddis applications, and other database systems are able to take advantage of in memory, large data structures to speed up their data processing

MemVerge’s EAP (early access program) opened up recently (5/19/2020). Charles suggested anyone using large, in memory data processing, take a look at what the Memory Machine can do and contact them to sign up.

The podcast runs ~45 minutes. Charles was very articulate as well as knowledgeable about the technology and its applications. He was great to talk tech with. Matt and I had a fun time talking Optane DC PM and Memory Machine functionality/applications with him. Listen to the podcast to learn more.

Charles Fan, CEO & Co-founder, MemVerge

Charles Fan is co-founder and CEO of MemVerge. Prior to MemVerge, Charles was a SVP/GM at VMware, founding the storage business unit that developed the Virtual SAN product.

Charles also worked at EMC and was the founder of the EMC China R&D Center. Charles joined EMC via the acquisition of Rainfinity, where he was a co-founder and CTO.

Charles received his Ph.D. and M.S. in Electrical Engineering from the California Institute of Technology, and his B.E. in Electrical Engineering from the Cooper Union.

0101: Greybeards talk with Howard Marks, Technologist Extraordinary & Plenipotentiary at VAST

As most of you know, Howard Marks (@deepstoragenet), Technologist Extraordinary & Plenipotentiary at VAST Data used to be a Greybeards co-host and is still on our roster as a co-host emeritus. When I started to schedule this podcast, it was going to be our 100th podcast and we wanted to invite Howard and the rest of the co-hosts to be on the call to discuss our podcast. But alas, the 100th Greybeards podcast came and went, before we could get it done. So we decided to refocus this podcast back on VAST Data.

We talked with Howard last year about VAST and some of this podcast covers the same ground (see last year’s podcast with Howard on VAST Data) but I highlighted below different aspects of their product that we also discussed.

For starters, VAST just finalized a recent round of funding, which if I recall, valued them at over $1B USD, or yet another data storage unicorn.

VAST is a scale out, disaggregated, unstructured data platform that takes advantage of the economics of QLC SSD (from Intel) combined with the speed of 3D XPoint storage class memory (Optane SSD, also from Intel) to support customer data. Intel is an investor in VAST.

VAST uses mutliple front end (controller) servers, with one or more HA NVMe drive module(s) connected via a dual infiniband or 100Gbps Ethernet RDMA cluster interconnect. The HA NVMe drive module has two (IO modules) adapter cards, one for each connection that takes IO and data requests and transfers them across a PCIe bus which connects to QLC and Optane SSDs. They also have a Mellanox (another investor) switch on their backend with a (round robin) DNS router to connect hosts to their storage (front-end) servers.

Each backend HA NVMe drive module has 12 1.5TB Optane U.2 SSDs and 44 15.4TB QLC SSDs, for a total of 56 drives. Customer data is first written to Optane and then destaged to QLC SSD.

QLC has the advantage of being 4 bits per cell (for a lower $/GB stored) but it’s endurance or drive writes/day (dw/d)) is significantly worse than TLC. So VAST has had to work to increase QLC endurance in their system.

Natively, QLC offers ~0.2 dw/d when doing random 4K writes. However, if your system does 128KB sequential writes, it offers 4.0 dw/d. VAST destages data from Optane SSDs to QLC in 1MB chunks which both optimizes endurance and reduces garbage collection write amplification within the drive.

Howard mentioned their frontend servers are stateless, i.e., maintain no state information about any IO activity going on. Any IO state information is maintained by their system in Optane SSDs. Each server maintains a work log (like) structure on Optane that describes what they are doing in support of host IO and other activities. That way, if one front end server goes down, another one can access its log and take over its activity.

Metadata is also maintained only on Optane SSDs. Howard called their metadata structure a V-tree (B-tree). VAST mirrors all meta-data and customer data to two Optane SSDs. So if one Optane SSD goes down, its pair can be used to continue operations.

In last years podcast we talked at length about VAST data protection and data reduction capabilities so we won’t discuss these any further here.

However, one thing worth noting is that VAST has a very large RAID (erasure code protection) stripe. Data is written to the QLC SSDs in a VAST designed, locally decodable erasure coding format.

One problem with large stripes is rebuild time. VAST’s locally decodable parity codes help with this but the other thing that helps is distributing rebuild IO activity to all front end servers in the system.

The other problem with large stripe sizes is garbage collection. VAST segregates customer data by “temporariness” based on their best guess. In this way all data in one stripe should have similar lifetimes. When it’s time for stripe garbage collection, having all temporary data allows VAST to jettison the whole stripe (or most of it) rather than having to collect and re-write old stripe data to another new stripe.

VAST came out supporting NFSv3 and S3 object storage protocols, Their next release adds support for SMB 2.2, data-at-rest encryption and snapshotting to an external S3 store. As you may recall SMB is a stateful protocol. In VAST’s home grown, SMB implementation, front end servers can take over SMB transactions from other failed servers, without having to fail the whole transaction and start over again.

VAST uses a fail in place, maintenance policy. That is failed SSDs are not normally replaced in customer deployments, rather blocks, pages, or SSDs are marked as failed and the spare capacity available in the drive enclosure is used to provide space for any needed rebuilt data.

VAST offers a 10 year maintenance option where the customer keeps the same storage for 10 full years. That way customers don’t have to migrate data from one system to another until their 10 years are up.

The podcast runs a little under 44 minutes. Howard and I can talk forever. He is always a pleasure to talk with as well as extremely knowledgeable about (VAST) storage and other industry solutions.  The co-hosts and I had a great time talking with him again. Listen to the podcast to learn more.

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Howard Marks, Technologist Extraordinary and Plenipotentiary, VAST Data, Inc.

Howard Marks brings over forty years of experience as a technology architect for hire and Industry observer to his role as VAST Data’s Technologist Extraordinary and Plienopotentary. In this role, Howard demystifies VAST’s technologies for customers and customer requirements for VAST’s engineers.

Before joining VAST, Howard ran DeepStorage an industry test lab and analyst firm. An award-winning speaker, he has appeared at events on three continents including Comdex, Interop and VMworld.

Howard is the author of several books (all gratefully out of print) and hundreds of articles since Bill Machrone taught him journalism at PC Magazine in the 1980s.

Listeners may also remember that Howard was a founding co-Host of the Greybeards-on-Storage Podcast.

098: GreyBeards talk data protection & visualization for massive unstructured data repositories with Christian Smith, VP Product at Igneous

Sponsored By:

Even before COVID-19 there was a lot of file data being created and mined, but with the advent of the pandemic, this has accelerated considerably. As such, it seemed an appropriate time to talk with Christian Smith, VP of Product at Igneous, (@IgneousIO) a company that targets the protection and visibility of massive quantities of unstructured data, on premise, in the cloud, or just about anywhere else it may live.

Let me state at the outset, that my belief had always been, that you don’t backup 10PB of data, rather you bite the (big expense) bullet to replicate it and hope for the best. After talking with Christian and Igneous I am going to have to modify that belief by a couple of more orders of magnitude.

All this data is coming from: LIDAR, RADAR, audio, video, pictures, medical film, MRI/CAT Scans, etc., and as noted above, it’s exploding. Christian talked about one customer of theirs that supplies aerial photography/LIDAR/RADAR scans of areas on request. This can used to better understand crop, forest, wildlife, land health and use. One surprise Igneous found with this customer is that the data is typically archived after first use, but within a month or so it’s moved back online for some other purpose.

Igneous heritage

Many of the people who started up and currently work at Igneous have been around file storage for some time having, primarily coming from (Dell EMC) Isilon, NetApp, Qumulo and other industry heavyweights. When they started Igneous, they realized the world didn’t need another NAS box or file system. Rather, with the advent of 10-100PB unstructured data farms, what was needed was an effective way to protect and understand that data.

When they considered how to protect and visualize 100PB of unstructured data, the only they found to do this was to build a scale-out solution that used on premise and cloud infrastructure and was offered as a service.

Igneous DataProtect solution

With 10PB or 100PB of files, located across a gaggle of heterogeneous file servers, with billions of files across ~100s of servers, each of with has ~1K or more file shares, just scanning all the file servers would take weeks, if not longer and then you need to move the data someplace to protect it. Seems like an impossible task.

Igneous immediately figured out the first thing they needed was a radically new, scale out architecture to rapidly scan of the file servers. Thus was born ActiveScan. Christian said it was designed to scan a trillion files and they have customers with a billion files using their service today. ActiveScan doesn’t use NFS/SMB/Object (S3) access protocols to talk with file servers rather it uses internal APIs to access file metadata. DataProtect currently supports APIs for NetApp, Dell EMC Isilon, Pure FlashBlade, Qumulo, Gluster, Lustre, & GPFS (IBM Spectrum Scale) file systems. They use ActiveScan to build a file index database.

Their other major concern was hot to move PBs of data rapidly across to the cloud and other locations. Again they created a scale out, multi-threaded service to do this and also made use of internal APIs rather than standard file or object protocols. This became IntelliMove. That same customer above with billions of files, has 6PB of file data to protect.

Normal data movement is fine for largish, files but bogs down with lots of small files or extremely large files to back up. DataProtect gathers together small files into a large chunks and splits up extremely large files into smaller chunks and moves these chunks to secondary storage.

Data expiration is another problem, especially when you chunk files together. Here they came up with an intelligent garbage collection algorithm which only collects free space when it makes the most sense but deletes data access at the time of expiration.

DataProtect uses a cloud based, SaaS control plane that manages and coordinates its activities across data centers, sites and cloud instances. It also has a client VM (OVA, with 8 core CPU, 32GB DRAM, ~100MB) that runs in the customers infrastructure, on site, in CoLo’s or in the cloud that is used to scan-move-protect customer unstructured data. If more scan and data movement performance is needed, the VM can spawn additional threads automatically and more VMs can be added to provide even more throughput.

DataDiscover solution

The other service that Igneous offers is DataDiscover a data visualization tool. DataDiscover uses ActiveScan and its database to provide customers a way to understand the file data that resides in their massive unstructured data farms across the data center, cloud or wherever else it resides.

We didn’t discuss this solution as much but having a way to better understand the files in a 10-100PB unstructured data farm could be very useful and a great way to keep that 100PB from growing to 1EB faster than it has too.

As part of their outreach to the world, Igneous is giving away free DataProtect services to organizations that are focused on COVID-19 research. Check out their offer here

The podcast ran ~24 minutes. Christian was extremely knowledgeable about the problems that happen with very large unstructured data farms and how Igneous solutions can provide a better way to protect and visualize that data. Matt and I had a fun time discussing Igneous’s approach with Christian. Listen to the podcast to learn more.

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Christian Smith, VP Product at Igneous

Christian is VP of Product, responsible for product management, solutions, and customer success. Prior to Igneous, Christian spent 15 years running field engineering organizations at EMC, Isilon Systems, NetApp and Silicon Graphics.

Christian has been working with organizations that work with file data since working at Silicon Graphics. Before that Christian was co-founder of a small management consulting company associated with Y2K and deregulation.

Christian received dual bachelor’s degrees in Chemistry and Computer Science from the University of Missouri-Columbia. Christian is an avid camper, skier and traveler and has long since traveled through all of the continental 48 states.

096: GreyBeards YE2019 IT Industry Trends podcast

In this, our yearend industry wrap up episode, the GreyBeards discuss trends and technologdies impacting the IT industry in 2019 and what’s ahead for 2020. This year we have Matt and Keith on the podcast along with Ray. Just like last year, we start off with NVMeoF.

NVMeoF unleashed

This year just about every major storage vendor announced new systems that either have support for NVMeoF or currently offer NVMeoF on their storage systems. Most offer FC based NVMeoF but a few offer NVMeoF/Ethernet, fewer still offer both.

All of the NVMeoF/Ethernet seem to be using RoCE or iWARP. Unclear if one is more often used that the other, so for now both continue to be used in the market. Some storage vendors are offering NVMeoF as an internal fabric to access storage while still using iSCSI or FC/SCSI to access the data. This works better than SAS but won’t provide all the performance you can get from end-to-end NVMeoF.

NVMeoF is all about increasing IOPS and reducing response times. That and getting ready for SCM SSDs. In the mean time the SSD industry has introduced some very attractive NVMe (NAND) SSDs that in NVMeoF storage system can increase IOPS and reduce latencies.

We talked last year about NVMeoF standards finally stabilizing and this year the rollout across enterprise storage systems is testament to that.

SCM hits the enterprise

Most of us attended an Intel Data Center Event earlier this past yea,r where Optane DC PM was introduced. Optane DC PM is the memory version of Optane SCM (3DX Crosspoint) technology. Intel offers two distinct modes of accessing Optane DC PM as memory: 1) App Direct mode, where data in Optane DC PM persists across power cycles but requires one to use a special AP; and 2) Memory mode where Optane DC PM is cleared during a power cycle, (see our RayOnStorage post Need memory, Intel’s Optane DC PM…).

Vendors seem to be using Optane both memory and SCM technology differently. Pure is using Optane SSDs plugged into their FlashArray as sort of a read cache for customer IO. They suggest for well behaved applications this can reduce IO response times considerably.

Dell EMC introduced SCM as a storage tier and are using their automated storage tiering to move the hottest data to SCM. Oracle’s latest Exadata appliance uses Optane DC PM as both a read and write caching layer.

It won’t be long before every enterprise vendor offers SCM drives in their storage systems with a few offering Optane DC PM as in memory caching technology.

Of course, the big news for Optane DC PM is its use in memory databases, specifically SAP HANA. HANA can take advantage of the (6) TB of memory to to handle larger databases. Keith mentioned that even Microsoft SQL server can take advantage of the additional memory to provide faster responses to queries.

Keith also mentioned that there are some systems out there that can be configured to share Optane memory (or storage). When SAP or other databases use this solution they are able to amortize the cost of the technology over more use cases.

Of course, Optane DC PM are only available on the lastest generation Intel processors. None of us have heard anything from AMD (or Micron) on providing a second source for support of Optane DC PM (or the memory technology itself). Presumably most customers would want a second source for Optane DC PM processor support (as well as the technology)

Cloud enterprise storage hits mainstream

The other thing we saw more of this year is enterprise vendors offering versions of storage in public cloud environments. NetApp was an early proponent of doing this.

We saw at Pure that they have a new Cloud Block Store witch is a re-architected version of FlashArray//X storage using AWS hardware and networking services. We were very impressed with what they have accomplished and it was the subject of more than one late night discussion. Listen to the Keith & Ray show at Pure//Accelerate2019 podcast to learn more.

Matt mentioned Nimble’s cloud volume storage which is cloud adjacent. Most enterprise vendors offer something similar today. They differentiate on how easy it is to configure, use and where (which regions) it’s available in.

NetApp has arguably been at this the longest and has the deepest offerings available from cloud adjacent file and block storage, to offering native enterprise file services for all public cloud environments, to supplying a suite of dedicated data services to surround all of their storage technology operating in public clouds and on premises.

While Dell EMC may have missed the turn to the cloud, they are quickly trying to catch up. Keith mentioned Faction, a Dell partner that offers cloud storage services using VMware with VMC. With Faction and vSAN customers have access to software defined storage that uses cloud hardware to support data services.

What’s driving data growth

There seems to be no end for the need for storage to store data. The GreyBeards point to three trends driving data growth today.

  1. IoT seems to have no bounds. A recent RayOnStorage post Internet of Tires discussed how tire companies were tying their tires to the internet. And that’s just the start, pretty soon every artifact, every device, every manufactured item will have a number of sensors attached all of which will be creating massive amounts of data.
  2. AI ML DL has an insatiable appetite for data. IoT is being used largely to optimize products and services. But it’s DL, with a large dollop of data, that is behind much of that optmization.
  3. SaaS applications is a relatively new application approach that’s being rolled out to more arenas and as it’s online and user oriented, seems to generate lots of data.

Containers storage debate

We closed the podcast with a heavy debate on whether container applications have need for storage. Keith was adamant that containers by their very nature are stateless and that Kubernetes ability to stop and start container applications at will almost requires stateless operations.

Ray was a bit more theoretical on the topic and believed that most container applications today take advantage of some sort of database or other services to store state and that state is just another word for storage.

Keith mentioned encoding as a typical container app. Encoding containers can be fired up and taken down at will without hurting anything but throughput. Yes, but those encoder container apps must access some database or other state information to find out what work is left to do and as they complete their work they update this data as well as store their newly encoded segments. This all involves the use of state information.

In the end, I think we were talking about the same thing but using different terminology. Keith believes that persistent state information is needed and Ray says that this is just another word for (containers) storage. Matt said we probably need Nigel (@NigelPoulton) on the podcast to straighten us both out.

The podcast ran a bit long and could have run longer. Keith and Matt bring systems level perspective to what’s happening in the storage market. But they come at it from different sides. Ray seems to frame everything from a storage perspective. Diverse perspectives lead to a more fuller and interesting discussion. Listen to the podcast to learn more.

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Ray Lucchesi ( @RayLucchesi) is the host of GreyBeardsOnStorage and is President/Founder of Silverton Consulting, and a prominent blogger at RayOnStorage.com.

Keith Townsend (@CTOAdvisor) is a IT thought leader who has written articles for many industry publications, interviewed many industry heavyweights, worked with Silicon Valley startups, and engineered cloud infrastructure for large government organizations. Keith is the co-founder of The CTO Advisor, blogs at Virtualized Geek

Matt Leib (@MBLeib), one of our co-hosts, has been blogging in the storage space for over 10 years, with work experience both on the engineering and presales/product marketing. His blog is at Virtually Tied to My Desktop.