112: GreyBeards annual year end wrap-up with Keith & Matt

It’s the end of the year, so time for our regular year end wrap up discussion with the GreyBeards. 2020 has been an interesting year to say the least. It started out just fine, then COVID19 showed up and threw a wrench in everyone’s plans and as the year closes, we were just starting to see some semblance of the new normal, when one of the largest security breaches in years shows up. Whew, almost glad that’s over and onto 2021.

As always the GreyBeards had a great discussion on these and other topics to highlight the year just past. The talk was wide ranging and hard to characterize but I did my best below. Listen to the podcast to learn more.

COVID19s impact on the enterprise

It will probably take some time before we learn the true, long term impacts of COVID19 on IT but one major change has to be the massive Work From Home (WFH) transition that took place overnight.

While WFH can be more productive for some, the lack of face2face interaction can be challenging for others. The fact that many of the GreyBeards have been working from home for decades now, left us a bit oblivious to how jarring this transition can be for newcomers.

There’s definitely some psychological changes that need to occur to be productive at WFH. Organization skills become even more important. Structured interactions (read conference calls, zoom/webex and other forms of communication become much more important. And then there’s security.

Turns out VMware and others have been touting VDI solutions for the past decade or so to better support remote work and at the same time providing corporate levels of security for remote work. While occasionally this doesn’t work quite as well as expected, it’s certainly much much better than having end users access corporate data without any security around that data or worse yet, the “bring your own device”. All these VDI solutions had a field day when WFH happened.

Many workers found they could be more productive at WFH, due the less distractions, no commute time and more flexible hours. What happens when COVID19 is vanquished to all these current WFHers is anyone’s guess.

We thought there might be less need for large office campuses/buildings. But there’s something to be said for more collaboration and random interactions through face2face meetings that can only occur in an office setting with workers present at the same time. Some organizations will take to this new way of work while others will try to dial WFH back to non-existent. Where your organization fits on this spectrum and why, will be telling across a number of dimensions.

The rise of ARM

There’s been a slow but steady improvement in ARM processors over the last almost half century. Nowadays it’s starting to make a place for itself in the enterprise. ARH has always been the goto microprocessor for low power solutions (like smartphones) but nowadays they are being deployed in the cloud and even the enterprise. These can be used as server processors but even outside servers, ARM cores are showing up in hardware accelerators as the brains behind SmartNICs, DPUs, SPUs, etc.

Keith made mention AWS 2nd generation Graviton 64-bit ARM processor EC2 instances. And yes there’s significant cost ( & power) savings that can be had using AWS Graviton ARM instances. So the cloud is starting to adopt them. Somewhere over the past couple of years I heard that VMware was porting ESX to work on ARM cores.

But apparently, it’s not just as simple as dropping an ARM multi-core processor into a server and recompiling your code and away you go. Applications need a certain amount of optimization to run effectively on ARM processors. And the speed up between non-optimized and optimized versions of an application running on ARM cores is significant.

As for SmartNICs and DPUs, these are data networking hardware accelerators that provide real time processing capabilities needed to keep up with higher speed networking, 100GbE and beyond. These DPUs perform deep packet inspection, data compression, encryption and other services all at wire speeds.. Yes you could devote 1 or more X86 cores to do this, but it’s much cheaper (and more effective) to do this outside the CPU core. Moreover, performing this activity at the network entry point to the server means that much of this data doesn’t have to be transferred back and forth through server memory. So not only does it save CPU core cycles but also memory size and memory & PCIe bus bandwidth. We published a recent podcast with Kevin Deierling, NVIDIA Networking discussing DPUs if you want to learn more.

Pat made mention at (virtual) VMworld their plans to port ESX to the DPU. Keith followed up on this and asked some other exec’s at VMware about this and they said VMware will more likely support DPUs as just another hardware accelerator in their cluster. In either case, CPU cycles should be freed up and this should help VMware use X86 cores more efficiently. And perhaps this will help them engage in more CPU constrained environments such as Telcom.

Then there’s computational storage. We have been watching this technology for a couple of years now and it’s seeing some success in being deployed to public cloud environments. They seem to be being used to provide outboard data compression. It’s unclear whether these systems depend on ARM processing or not but my bet is that they do. To learn more about computational storage check out these podcasts, FMS2020 wrap up with Jim Handy and our talk with Scott Shadley on NGD’s computational storage.

System security

At yearend, we are learning of a massive security breach throughout US government IT facilities. All based on what is believed to be a Russian hack to a software package that is embedded in a popular networking tool software solution, SolarWinds. They are calling this a software supply chain hack. Although we are mainly hearing about government agencies being hacked, SolarWinds is also pervasive in the enterprise as well.

There have been many hardware supply chain hacks in the past, where a board supplier used chips or logic that weren’t properly vetted. Over time, hardware suppliers have started to scrutinize their supply chains better and have reduced this risk.

And the US government have been lobbying for the industry to use a security chip with a backdoor or to supply back doors to smartphone encryption capabilities. Luckily, so far, none of these have been implemented by industry.

What Russia has shown us is that this particular hack is not limited to the hardware sphere. Software supply chain risk can’t be ignored anymore.

This means that any software application supplier will need to secure their supply chain or bring it all in house. Which may mean that costs for these packages will go up. It’s possible that using a pure open source supply chain may reduce this risk as well. At least that’s the promise of open source.

We said 2020 was an interesting year and it’s going out with a bang.

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 and he’s on LinkedIN.

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, and can be found on LinkedIN.

111: GreyBeards talk data analytics with Matthew Tyrer, Sr. Mgr. Solutions Mkt & Competitive Intelligence, Commvault

Sponsored by:

I’ve known Matthew Tyrer, Senior Manager Solutions Marketing and Competitive Intelligence, Commvault for quite awhile now and he’s always been knowledgeable about the problems the enterprise has in supporting and backing up large file data repositories. But lately he’s been focused on Commvault Activate their data analytics solution.

We had a great talk with Matthew. He was easy to talk to and knew a lot about how data analytics can ease the operational burden of the enterprise growing file data environments. .Remind me not to have two Matthew’s on the same program ever again. Listen to the podcast to learn more.

Matthew mentioned that their Activate was built on the Commvault platform software stack, which has had a rich and long history of development and customer deployments. It seems that Activate data analytics had been an early part of the platform but recently was split out as a separate solution.

One capability that Activate has that many other data analytics solutions do not, is the ability to examine both online data as well as data in backups. Most analytics solution can do one or the other, only a few do both. But if a solution only has access to online or backup data, they are missing half the story.

In addition, Activate can operate across multiple data centers as well as across multiple public cloud environments to provide analytics for an enterprise’s file data where it may reside.

Given the proliferation of file data these days, data analytics has become a necessity to most large IT shops. In the past, an admin could track some data over time but with the volumes of file data today, this is no longer tenable. At PB or more of file data, located in on prem data centers as well as across multiple clouds, there’s just too much file data to keep track of manually anymore.

Activate also indexes file content to provide more visibility and tracking of the different types of data under management in the enterprise. This is in addition to the extensive metadata that is collected and analyzed so it can better understand data access rights, copies and physical locations around the enterprise.

Activate can help organizations govern their data flows in support of industry as well as government data compliance requirements. Activate Data Governance, one of the three Activate solutions, is focused exclusively on providing enterprises the tools needed to manage any and all data that exists under compliance regulation environments.

Mat Leib had worked in eDiscovery before and it had always been a pain to extract “legally relevant” data from online and backup repositories. With the Activate eDiscovery solution and Activate’s content indexing of all file data, legal can perform their own relevant data searches to create eDiscovery data sets in support of litigation activities. Self service legal extracts like this vastly reduces the admin time and cost needed for eDiscovery.

The Activate File Space Optimization solution was deployed in one environment that had ~20PB of data online. By using File Space Optimization, the customer was able to cut 20PB down to 10PB. Any customer could benefit from such a reduction but customers doing data migration would see even more benefit.

At the end of the podcast, Matthew mentioned some videos that show Activate solution use cases.

Matthew Tyrer, Senior Solutions Marketing and Competitive Intelligence

Having worked at Commvault for over twelve years, after 8 years as a Sales Engineer Matt took that technical knowledge and transitioned to marketing where he is currently serving as a Senior Manager in Commvault’s Solution Marketing team. He is also heavily involved in Competitive Intelligence initiatives, and actively participates in field enablement programs.

He brings over 20 years’ experience in the IT industry, including within the fields of data and information management, cloud, data governance, enterprise storage, disaster recovery, and ultimately both implementing and supporting those projects and endeavours for public and private sector clients across Canada and around the globe.

Matt’s passion, deep product knowledge, and broad field experiences have enabled him to translate Commvault technology and vision such that their value is easily understood in the market and amongst client and partner families.

A self-described geek-dad, Matt is an avid boardgame enthusiast, firmly believes that Han shot first, and enjoys tormenting his girls with bad dad jokes.

109: GreyBeards talk SmartNICs & DPUs with Kevin Deierling, Head of Marketing at NVIDIA Networking

We decided to take a short break (of sorts) from storage to talk about something equally important to the enterprise, networking. At (virtual) VMworld a month or so ago, Pat made mention of developing support for SmartNIC-DPUs and even porting vSphere to run on top of a DPU. So we thought it best to go to the source of this technology and talk with Kevin Deierling (TechSeerKD), Head of Marketing at NVIDIA Networking who are the ones supplying these SmartNICs to VMware and others in the industry.

Kevin is always a pleasure to talk with and comes with a wealth of expertise and understanding of the technology underlying data centers today. The GreyBeards found our discussion to be very educational on what a SmartNIC or DPU can do and why VMware and others would be driving to rapidly adopt the technology. Listen to the podcast to learn more.

NVIDIA’s recent acquisition of Mellanox brought them Mellanox’s NIC, switch and router technology. And while Mellanox, and now NVIDIA have some pretty impressive switches and routers, what interested the GreyBeards was their SmartNIC technology.

Essentially, SmartNICS provide acceleration and offload of data handling needs required to move data around an enterprise network. These offload services include at a minimum, encryption/decryption, packet pacing (delivering gadzillion video streams at the right speed to insure proper playback by all), compression, firewalls, NVMeoF/RoCE, TCP/IP, GPU direct storage (GDS) transfers, VLAN micro-segmentation, scaling, and anything else that requires real time processing to perform at line speeds.

For those who haven’t heard of it, GDS transfers data from storage directly into GPU memory and from GPU memory directly to storage without any CPU cycles or server memory involvement, other than to set up the transfer. This extends NVMeoF RDMA tech to/from storage and server memory, to GPUs. That is, GDS offers a RDMA like path between storage and GPU memory. GPU to/from server memory direct interface already exists over the PCIe bus.

But even with all the offloads and accelerators above, they can also offer an additional a secure enclave outside the TPM in the CPU, to better isolate security sensitive functionality for a data center. (See DPU below).

Kevin mentioned multiple times that the new unit of computation is no longer a server but rather is now a data center. When you have public cloud, private cloud and other systems that all serve up virtual CPUs, NICs, GPUs and storage, what’s really being supplied to a user is a virtual data center. Cloud providers can carve up their hardware and serve it to you any way you want or need it. Virtual data centers can provide a multitude of VMs and any infrastructure that customers need to use to run their workloads.

Kevin mentioned by using SmartNics, IT or cloud providers can return 30% of the processor cycles (that were being spent doing networking work on CPUs) back to workloads that run on CPUs. Any data center can effectively obtain 30% more CPU cycles and increased networking speed and performance just by deploying SmartNICs throughout all the servers in their environment.

SmartNICs are an outgrowth of Mellanox technology embedded in their HPC InfiniBAND and high end Ethernet switches/routers. Mellanox had been well known for their support of NVMeoF/RoCE to supply high IOPs/low-latency IO activity for NVMe storage over Ethernet and before that their InfiniBAND RDMA technologies.

As Mellanox came out with their 2nd Gen SmartNIC they began to call their solution a “DPU” (data processing unit), which they see forming part of a “holy trinity” underpinning the new data center which has CPUs, GPUs and now DPUs. But a DPU is more than just a SmartNIC.

All NVIDIA SmartNICs and DPUs are based on Mellanox’s BlueField cards and chip technology. Their DPU uses BlueField2 (gen 2 technology) chips, which has a multi-core ARM engine inside of it and memory which can be used to perform computational processing in addition to the onboard offload/acceleration capabilities.

Besides adding VMware support for SmartNICs, PatG also mentioned that they were porting vSphere (ESX) to run on top of NVIDIA Networking DPUs. This would move the core VMware’s hypervisor functionality from running on CPUs, to running on DPUs. This of course would free up most if not all VMware Hypervisor CPU cycles for use by customer workloads.

During our discussion with Kevin, we talked a lot about the coming of AI-ML-DL workloads, which will require ever more bandwidth, ever lower latencies and ever more compute power. NVIDIA was a significant early enabler of the AI-ML-DL with their CUDA API that allowed a GPU to be used to perform DL network training and inferencing. As such, CUDA became an industry wide phenomenon allowing industry wide GPUs to be used as DL compute engines.

NVIDIA plans to do the same with their SmartNICs and DPUs. NVIDIA Networking is releasing the DOCA (Data center On a Chip Architecture) SDK and API. DOCA provides the API to use the BlueField2 chips and cards which are the central techonology behind their DPU. They have also announced a roadmap to continue enhancing DOCA, as they have done with CUDA, over the foreseeable future, to add more bandwidth, speed and functionality to DPUs.

It turns out the real problem which forced Mellanox and now NVIDIA to create SmartNics was the need to support the extremely low latencies required for NVMeoF and GDS IO.

It wasn’t clear that the public cloud providers were using SmartNICS but Kevin said it’s been sort of a widely known secret that they have been using the tech. The public clouds (AWS, Azure, Alibaba) have been deploying SmartNICS in their environments for some time now. Always on the lookout for any technology that frees up compute resources to be deployed for cloud users, it appears that public cloud providers were early adopters of SmartNICS.

Kevin Deierling, Head of Marketing NVIDIA Networking

Kevin is an entrepreneur, innovator, and technology executive with a proven track record of creating profitable businesses in highly competitive markets.

Kevin has been a founder or senior executive at five startups that have achieved positive outcomes (3 IPOs, 2 acquisitions). Combining both technical and business expertise, he has variously served as the chief officer of technology, architecture, and marketing of these companies where he led the development of strategy and products across a broad range of disciplines including: networking, security, cloud, Big Data, machine learning, virtualization, storage, smart energy, bio-sensors, and DNA sequencing.


Kevin has over 25 patents in the fields of networking, wireless, security, error correction, video compression, smart energy, bio-electronics, and DNA sequencing technologies.

When not driving new technology, he finds time for fly-fishing, cycling, bee keeping, & organic farming.

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106: Greybeards talk Intel’s new HPC file system with Kelsey Prantis, Senior Software Eng. Manager, Intel

We had talked with Intel at Storage Field Day 20 (SFD20), about a month ago. At the virtual event, Intel’s focus was on their Optane PMEM (persistent memory) technology. Kelsey Prantis (@kelseyprantis), Senior Software Engineering Manager, Intel was on the show and gave an introduction into Intel’s DAOS (Distributed Architecture Object Storage, DAOS.io) a new HPC (high performance computing, super computers) file system they developed from scratch to use leading edge, Intel technologies, and Optane PMEM was one of them.

Kelsey has worked on LUSTRE and other HPC file systems for a long time now and came into the company from the acquisition of Whamcloud. Currently, she manages the development team working on DAOS. DAOS is a new HPC object storage file system which is completely open source (available on GitHub).

DAOS was designed from the start to take advantage of NVMe SSDs and Optane PMEM. With PMEM, current servers can support up to 20TB of memory. Besides the large memory sizes, Optane PMEM also offers non-volatile memory and byte addressability (just like DRAM). These two characteristics opens up new functionality that allows DAOS to move beyond legacy, block oriented, storage architectures that have been the only storage solution for HPC (and the enterprise) for decades now.

What’s different about DAOS

DAOS uses PMEM for all metadata and for storing small files. HPC IO has always focused on heavy bandwidth (IO using large blocks) oriented but lately newer applications have emerged, such as AI/ML/DL, data analytics and others, that use smaller files/blocks. Indeed, most new HPC clusters and supercomputers are deploying almost as many GPUs as CPUs in their configurations to support AI activities.

The problem is that these newer applications typically consume much smaller files. Matt mentioned one HPC client he worked with was processing small batches of seismic data, to predict, in real time, earthquakes that were happening around the world.

By using PMEM for metadata and small files, DAOS can be much more responsive to file requests (open, close, delete, status) as well as provide higher performing IO for small files. All this leads to a much better performing system for the new HPC workloads as well as great sustainable performance for the more traditional large file workloads.

DAOS storage

DAOS provides a cluster storage system that can be configured with from 1 (no data protection), but more normally 3 nodes (with data protection) at a minimum to 512 nodes (lab tested). Data protection in DAOS is currently based on mirroring data and can use from 0 to the number of nodes in a cluster as data mirrors.

DAOS system nodes are homogeneous. That is they all come with the same amount of PMEM and NVMe SSDs. Note, DAOS doesn’t support disk drives. Kelsey mentioned DAOS node hardware can be tailored to suit any particular application environment. But they typically require an average of 6% of overall DAOS system capacity in PMEM for metadata and small file activity.

DAOS current supports their own API, POSIX, HDFS5, MPIIO and Apache Spark storage protocols. Kelsey mentioned that standard POSIX uses a pessimistic conflict resolution mode which leads to performance bottlenecks during parallel access. In contrast, DAOS’s versos of POSIX uses optimistic conflict resolution, which means DAOS starts writes assuming there’s no conflict, but if one occurs it handles the conflict in real time. Of course with all the metadata byte addressable and in PMEM this doesn’t take up a lot of (IO) time.

As mentioned earlier, DAOS data protection uses mirror-replicas. However, unlike most other major file systems, DAOS mirroring can be done at the object level. DAOS internally is an object store. Data organization on DAOS starts at the pool level, underneath that is data containers, and then under that are objects. Any object in DAOS can have its own mirroring configuration. DAOS is working towards supporting Erasure Coding as another form of data protection for a future release.

DAOS performance

There’s a new storage benchmark that was developed specifically for HPC, called the IO500. The IO500 benchmark simulates a number of different HPC workloads, measures performance for each of them, and computes an (aggregate) performance score to rank HPC storage systems.

IO500 ranks system performance using two lists: one is for any sized configuration that typically range from 50 to 1000s of nodes and their other list limits the configuration to 10 nodes. The first performance ranking can sometimes be gamed by throwing more hardware into a cluster. The 10 node rankings are much harder to game this way and from our perspective, show a fairer comparison of system performance.

As presented (virtually) at ISC 2020, DAOS took the top spot on the IO500 any size configuration list and performed better than 2X the next best solution. And on the IO500 10 node list, Intel’s DAOS configuration, Texas Advanced Computing (TAC) DAOS configuration, and Argonne Nat Labs DAOS configuration took the top 3 spots and had 3X better performance than the next best, non-DAOS storage system.

The Argonne National Labs has already stated that they will be using DAOS in their new HPC system to be deployed in the near future. Early specifications for storage at the new Argonne Lab required support for 230PB of data and 25TB/sec of bandwidth.

The podcast ran ~43 minutes. Kelsey was great to talk with and very knowledgeable about HPC systems and HPC IO in particular. Matt has worked at Argonne in the past so understood these systems better than I. Sadly, we lost Matt’s end of the conversation about 1/2 way into the recording. Both Matt and I thought that DAOS represents the birth of a new generation of HPC storage. Listen to the podcast to learn more.


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Kelsey Prantis, Senior Software Engineering Manager, Intel

 Kelsey Prantis heads the Extreme Storage Architecture and Development division at Intel Corporation. She leads the development of Distributed Asynchronous Object Storage (DAOS), an open-source, low-latency and high IOPS object store designed from the ground up for massively distributed Non-Volatile Memory (NVM).

She joined Intel in 2012 with the acquisition of Whamcloud, where she led the development of the Intel Manager for Lustre* product.

Prior to Whamcloud, she was a software developer at personal genomics and biotechnology company 23andMe.

Prantis holds a Bachelor’s degree in Computer Science from Rochester Institute of Technology

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.