108: GreyBeards talk DNA storage with David Turek, CTO, Catalog DNA

The Greybeards get off the beaten (enterprise) path this month, to see what lies ahead with a discussion on DNA storage. David Turek, CTO, Catalog DNA (@CatalogDNA) is a long time IBMer that had been focused on HPC systems at IBM but left and went to Catalog DNA to pursue the commercialization of DNA storage, an “emerging” technology. CatalogDNA is a company out of Boston that had recently closed a round of funding and are focused on bringing DNA storage out into the world of IT.

David was a pleasure to talk and has lot’s of knowledge on HPC and enterprise data center solutions. He also has a good grasp of what it will take to bring DNA storage to market. Keith has had some prior experience with DNA technologies in BioPharma so could talk in more detail about the technology and its ecosystem. [We’re trying out a new format, let us know what you think; The Eds.]

Ray has written about DNA storage in his RayOnStorage Blog, most recently in April of this year and May of last year. It’s been an ongoing blog topic of his for almost a decade now. When Ray was interviewed about the technology he thought it interesting but had serious obstacles with read and write latencies and throughput as well as the size of the storage device.

Well CatalogDNA seems to have got a good handle on write throughput and are seriously working on the rest.

However, DNA storage’- volumetric density was always of exceptional. Early on in the podcast, David mentioned that DNA storage was 6 orders of magnitude (1 million times) more dense in bytes/mm**3 than magnetic tape today. An LTO8 tape device stores 12TB (uncompressed) in a tape cartridge, 14.2 in**3 (230.3 cm**3) or roughly 845GB/in**3 (52GB/cm**3). One million times this, would be 12EB in the same volume.

The challenge with LTO8, disk or SSD storage today is at some point you have to move the data from one device to a more modern device. This could be every 3-5 years (for disk or SSD) or 25-30 years for tape. In either case, at some point IT would need to incur the cost and time to move the data. Not much of a problem for 100TB or so but when you start talking PB or EB of data, it can be a never ending task.

DNA storage

David mentioned Catalog uses “synthetic DNA” in their storage. This means the DNA it uses is designed to be incompatible with natural DNA such that it wouldn’t work in a cell. It has stops or other biological mechanisms to inhibit it’s use in nature. Yes it uses the same sugars, backbones, and other chemistry of biologically active DNA, but it has been specifically modified to inhibit its use by normal cellular machinery. 

DNA storage has a number of unique capabilities :

  • It can be made to last forever, by being dried out (dessicated) and encased in a crystal and takes 0 power/energy to be stored for eons.
  • It can be cheaply and easily replicated, almost an infinite number of times, for only the cost of chemical feedstock, chemical interactions and energy. Yes, this may take time but the process scales up nicely. One could make 2 copies in first cycle, 4 in the 2nd, 8 in the 3rd, etc and by doing this it would only take 20 cycles to create a million copies. If each cycle takes 10 minutes, in 3:20, you could have a million copies of 1EB of data.
  • It can be easily searched for target information. This involves fabricating a DNA search molecule and inserting it into the storage solution. Once there it would match up with the DNA segment that held your key. And of course, the search molecule and the data could be replicated to speed up any search process.
  • We already mentioned the extreme density advantage above.

Speed of DNA storage access

David said they can already write Catalog DNA storage in MB/sec.

The process they use to write is like a conveyer belt which starts off with a polyethylene sheet (web actually). Somewhere, the digital data comes in, is chunked and transformed into DNA strand (25-50 base pairs) molecules or dots. The polyethylene sheet rolls into a machine that uses multiple 3D print heads to deposit dots (the DNA strand data chunks) at web points. This machine/process deposits 100K or more of these dots onto the web. The sheet then moves to the next stage where the DNA molecules are scraped off and drained into a solution. Then a wet process occurs which uses chemistry to make the DNA more readable and enables the separate DNA molecules to connect into a data strand. Then this data strand goes into another process where it gets reduced in volume and so that it is more stable.

If needed, one can add another step that dries out or desiccates the data strand into even a smaller volume which can then be embedded into a crystalline structure which could last for centuries.

David compared the DNA molecules (data chunks) to Legos, only they are the same pieces in a million different colors Each piece represents some segment of data bits/bytes. Using chemistry and proprietary IP each separate DNA molecule self organizes (connects) into a data strand, representing the information you want to store.

Reading DNA involves, off the shelf, DNA sequencers. The one Catalog currently uses is the Oxford NanoPore device, but there are others. David didn’t say how fast they could read DNA data. But current DNA reading devices destroy the data. So making replicas of the data would be required to read it.

David said their current write device is L shaped with one leg about 14’ (4.3m) long and the other about 12’ (3.7m) long with each leg being about 3’ (0.9m) wide.

Searching EB of data in minutes?!

DNA strands can be searched (matched) using a search molecule and inserting this into the storage solution (that holds the data strands). Such a molecule will find a place in the data that has a matching (DNA) data element and I believe attach itself to the data strand.

For example, lets say you had recorded all of a country’s emails for a month or so and you wanted to search them for the words, “bomb”, “terrorist”, “kill”, etc. One could create a set of search molecules, replicate them any number of times (depending on how quickly you wanted to search the data and how many matches you expected), and insert them into a data pool with multiple data strands that stored the email traffic.

After some time, you’d come back and your search would be done. You’d need to then extract the search hits, and read out the portion of the data strands (emails) that matched. I’m guessing extraction would involve some sort of (wet) chemical process or filtration.

State of Catalog DNA storage

David mentioned that as a publicity stunt they wrote the whole Wikipedia onto Catalog DNA storage. The whole Wikipedia fit into a cylinder about the height of a big knuckle on your hand and in a width smaller than a finger. The size of the whole Wikipedia, with complete edit history is 10TB uncompressed and if they stored all the edit versions plus its media such as images, videos, audio and other graphics, that would add another 23TB (as of end of 2014), so ~33TB uncompressed.

David believes in 18 months they could have a WORM (write once, read many times) data storage solution that could be deployed in customer data centers which would supply immense data repositories in relatively small solution containers.

CatalogDNA is currently in a number of PoCs with major corporations (not labs or universities) to show how DNA storage technology can be used to solve problems.

David believes that at some point they will be able to make compute engines entirely of DNA. At that point, one could have a combined compute and storage (HCI-like) DNA server using the same technology in a solution. And as mentioned previously, one could replicate from one DNA server & storage to a million DNA servers & storage in just 20 cycles. How’s that for scale out.


David Turek, CTO Catalog DNA

Dave Turek is Catalog’s Chief Technology Officer. He comes to Catalog from IBM where he held numerous executive positions in High Performance Computing and emerging technologies.

He was the development executive for the IBM SP program which produced the first commercially successful massively parallel system; he started IBM’s Linux Cluster business; launched an early offering in Cloud computing called Deep Computing Capacity on Demand; produced the Roadrunner system, the world’s first petascale computer; and was responsible for IBM’s exascale strategy which led to the deployment of the Summit and Sierra systems at Oak Ridge and Lawrence Livermore National Laboratories respectively.

David has been invited to testify to Congress on numerous occasions regarding the future of computing in the US and has helped establish technical collaborations with universities, businesses, and government agencies around the world.

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

Sponsored by:

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.]


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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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.

92: Ray talks AI with Mike McNamara, Sr. Manager, AI Solution Mkt., NetApp

Sponsored By: NetApp

NetApp’s been working in the AI DL (deep learning) space for a long time now and announced their partnership with NVIDIA DGX systems, back in August of 2018. At NetApp Insight, this week they were showing off their new NVIDIA DGX systems reference architectures. These architectures use NetApp AFF A800 storage (for more info on AI DL, checkout Ray’s Learning Machine (deep) Learning posts – part 1, – part 2 and – part3).

Besides the ONTAP AI systems, NetApp also offers

  • FlexPod AI solution based on their partnership with Cisco using UCS C480 ML M5 rack servers which include 8 NVIDA Tesla V100 GPUs and also features NetApp AFF A800 storage for use in core AI DL.
  • NetApp HCI has two configurations with 2- or 3-NVIDIA GPUs that come in 1U or 2U rack servers and run VMware vSphere or RedHad OpenStack/OpenShift software hypervisors suitable for edge or core AI DL.
  • E-series reference architecture that uses the BeeGFS parallel file system and offers InfiniBAND data access for HPC or core AI DL.

On the conference floor, NetApp showed AI DL demos for automotive, financial services, Public Sector and healthcare verticals. They also had a facial recognition application running that could estimate your age and emotional state (I didn’t try it, but Mike said they were hedging the model so it predicted a lower age).

Mike said one healthcare solution was focused on radiological image scans, to identify pathologies from x-Ray, MRI, or CAT scan images. Mike mentioned there was a lot of radiological technologists burn-out due to the volume of work caused by the medical imaging explosion over the last decade or so. Mike said image analysis is something that h AI DL can perform very effectively and doing so would improve the accuracy and reduce the volume of work being done by technologists.

He also mentioned another healthcare application that uses an AI DL app to count TB cells in blood samples and estimate the extent of TB infections. Historically, this has been time consuming, error prone and hard to do in the field. The app uses a microscope with a smart phone and can be deployed and run anywhere in the world.

Mike mentioned a genomics AI DL application that examined DNA sequences and tried to determine its functionality. He also mentioned a retail AI DL facial recognition application that would help women “see” what they would look like with different makeup on.

There was a lot of discussion on NetApp Cloud services at the show, such as Cloud Volume Services and Azure NetApp File (ANF). Both of these could easily be used to implement an AI DL application or be part of an edge to core to cloud data flow for an AI DL application deployment using NetApp Data Fabric.

NetApp also announced a new, all flash StorageGRID appliance that was targeted at heavy IO intensive uses of object store like AI DL model training and data analytics.

Finally, Mike mentioned NetApp’s ecosystem of partners working in the AI space to help customers deploy AI DL algorithms in their industries. Some of these include:

  1. Flexential, Try and Buy AI so that customers could bring them in to supply AI DL expertise to generate an AI DL application using customer data and deploy it on customer cloud or on prem infrastructure .
  2. Core Scientific, AI-as-a-Service, so that customers could purchase a service to implement an AI DL application using customer data and running on Core Scientific infrastructure..
  3. Scale Matrix, Mobile data center AI, so that customers could create an AI DL application and run it on Scale Matrix infrastructure that was transported to wherever the customer wanted it to be run.

We recorded the podcast on the show floor, in a glass booth, so there’s some background noise (sorry about that, but can’t be helped). The podcast is ~27 minutes. Mike is a long time friend and NetApp product expert, recently working in AI DL solutions at NetApp. When I saw Mike at Insight, I just had to ask him about what NetApp’s been doing in the AI DL space. Listen to the podcast to learn more.

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Mike McNamara, Senior Manager AI Solution Marketing, NetApp

With over 25 years of data management product and solution marketing experience, Mike’s background includes roles of increasing responsibility at NetApp (10+ years), Adaptec, EMC and Digital Equipment Corporation. 

In addition to his past role as marketing chairperson for the Fibre Channel Industry Association, he was a member of the Ethernet Technology Summit Conference Advisory Board, a member of the Ethernet Alliance, and a regular contributor to industry journals, and a frequent speaker at events.

83: GreyBeards talk NVMeoF/TCP with Muli Ben-Yehuda, Co-founder & CTO and Kam Eshghi, VP Strategy & Bus. Dev., Lightbits Labs

This is the first time we’ve talked with Muli Ben-Yehuda (@Muliby), Co-founder & CTO and Kam Eshghi (@KamEshghi), VP of Strategy & Business Development, Lightbits Labs. Keith and I first saw them at Dell Tech World 2019, in Vegas as they are a Dell Ventures funded organization. The company has 70 (mostly engineering) employees and is based in Israel, with offices in NY and the Valley as well as elsewhere around the world. Kam was previously with (Dell) EMC DSSD and Muli’s spent years as a Master Inventor with IBM Research.

[This was Keith Townsend’s (@CTOAdvisor & The CTO Advisor), first time as a GreyBeard co-host and we had a great time with him on the show.]

I would have to say it was a far ranging discussion but focused on their software defined, NVMeoF/TCP storage. As you may recall we talked with Solarflare Communications last year who were also working on a NVMeoF/TCP, only in their case it was an accelerator board. After the recording, Muli said the hardware accelerator they have is their own design.

Why NVMeoF/TCP?

Most NVMeoF today, that uses Ethernet, requires RoCE or iWARP compatible NICs and switches. Lightbits Labs has long been active in the NVMeoF/RoCE-iWARP market place. Early on they noticed that enterprise and cloud service providers were reluctant to adopt NVMeoF technology because of the need to change out all their networking equipment to use it. This is what brought about their focus on NVMeoF/TCP.

The advantage of NVMeoF/TCP is that it can be run on any Ethernet NIC and switch available today. From Muli’s perspective, NVMeoF/TCP is going to become the next SAN of choice for the data center. They were active, early on, in the standards committee to push for NVMeoF/TCP adoption.

How does it work?

Their software defined solution runs LightOS® storage software, a Linux based package, and uses off the shelf, server hardware with persistent storage (Optane DC PM/SSDs, NV DIMMs, V-NAND, etc.). They use persistent memory for a FAST write buffer and a place where they can “mold” the written data into something that can be better written to backend NVMe SSDs.

One surprise about Lightbits solution is that it offers a decent set of data services. These include erasure coding, thin provisioning, wire-speed inline compression, QoS and wide striping. It seems like any of these can be disabled by a customers want. But they only add very little overhead. I think Muli mentioned one Lightbits customer with encrypted data that disabled compression.

Lightbits also offers a global FTL (flash translation layer), which means they control SSD addressing which maps data to physical/raw NAND locations at the storage system level. If done well, a global FTL can help improve flash endurance and may offer better write performance (through increased parallelism).

Lightbits claim to inline, wire speed data compression is premised on the use of more current CPUs with high (>=28) core counts in a storage server. If the storage server has older CPUs (<28 cores), they suggest you install their LightField™ hardware accelerator add in card. LightField offers a number of hardware based, performance accelerations in addition to compression speedups.

LightOS requires no host (client) software. Muli’s a long time Linux kernel contributor and indicated that the only thing LightOS needs is a current Linux Kernel (5.0 or later) which has the NVMeoF/TCP driver software (and persistent memory). Lightbits believes that it’s only a matter of time until other OSs also implement NVMeoF/TCP drivers.

Lightbits business considerations

Long term, Lightbits sees a need for compute-storage disaggregation in hyper scalar and enterprise cloud environments. Early on it was relatively easy to replicate servers with DAS storage but as NVMe SSDs came out the expense to do this throughout their >>1000 server environment starts to become exorbitant. If they only had an easy way to disaggregate their storage from compute and still enjoy all the performance advantages of DAS NVMe SSDS. With LightOS they can do that.

Lightbits can be sold today through Dell, as a partner solution, which means that Dell can integrate, test and validate their servers with LightField accelerator card and deliver that package to your data center. I believe you still need to purchase and install their LightOS software yourself.

Lightbits charges for LightOS software on a per storage node basis, but they have different charges based on the maximum number of NVMe SSD slots available is in a server. There is no capacity charge. They also offer worldwide service and support for LightOS software and LightField hardware.

It’s all about performance

From a performance perspective, one Fortune 500 hyper-scalar benchmarked their storage solution against a DAS NVMe server and found it added about 30 µsec to the IO latency as compare to DAS NVMe SSDs. From their perspective, the added data services, better endurance, and disaggregated compute-storage environment provided by LightOS more than made up for the additional overhead.

Finally, I asked about whether multiple LightOS storage servers could be clustered together. Muli intervened, after stating some legal stuff, said they were working on the next generation LightOS and it will support clustered storage servers, local data replication as well as distributed (across storage servers) erasure coding.

The podcast is a long one and runs over ~47 minutes. There was a lot to talk about and Kam and Muli seem to know it all. It was interesting to hear the history of their pivot to TCP. They seem to have the right technology to address the market. Listen to the podcast to learn more.

Muli Ben-Yehuda, Co-founder and CTO, Lightbits Labs

Muli Ben-Yehuda is the CTO and Co-Founder of Lightbits Labs, where he leads technological developments.

Prior to founding Lightbits, he was chief scientist at Stratoscale and a researcher and Master Inventor at IBM Research.

He holds an M.Sc. in Computer Science (summa cum laude) from the Technion — Israel Institute of Technology and a B.A. (cum laude) from the Open University of Israel.

He is a long time Linux kernel contributor and his code and ideas are most likely included in an operating system or hypervisor running near you. He is also one of the authors of the NVMe/TCP standard and technology. 

Kam Eshghi, VP Strategy & Business Development, Lightbits Labs

Kam joined Lightbits Labs from Dell EMC and has over 20yrs of experience in strategic marketing and business development with startups and public companies.

Most recently as VP of strategic alliances at startup DSSD, Kam led business development with technology partners and developed DSSD’s partnership with EMC, leading to EMC’s acquisition of DSSD.

Previously as Sr. Director of Marketing & Business Development at IDT, Kam built their NVMe Controller business from scratch. Previous to that, Kam worked in data center storage, compute and networking markets at HP, Intel, and Crosslayer Networks. 

Kam is a U.C. Berkeley and MIT graduate with a BS and MS in Electrical Engineering and Computer Science and an MBA.