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163: GreyBeards talk Ultra Ethernet with Dr J Metz, Chair of UEC steering committee, Chair of SNIA BoD, & Tech. Dir. AMD

Dr J Metz, (@drjmetz, blog) has been on our podcast before mostly in his role as SNIA spokesperson and BoD Chair, but this time he’s here discussing some of his latest work on the Ultra Ethernet Consortium (UEC) (LinkedIN: @ultraethernet, X: @ultraethernet)

The UEC is a full stack re-think of what Ethernet could do for large single application environments. UEC was originally focused on HPC, with 400-800 Gbps networks and single applications like simulating a hypersonic missile or airplane. But with the emergence of GenAI and LLMs, UEC could also be very effective for large AI model training with massive clusters doing a single LLM training job over months. Listen to the podcast to learn more.

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The UEC is outside the realm of normal enterprise environments. But as AI training becomes more ubiquitous, who knows whether UEC may not find a place in the enterprise. However, it’s not intended for mixed network environments with multiple applications. It’s a single application network.

One wouldn’t think, HPC was a big user of Ethernet for their main network. But Dr J pointed out that the top 3 of the HPC 500, all use Ethernet and more are looking to use it in the future.

UEC is essentially an optimized software stack and hardware for networking used by single application environments. These types of workloads are constantly pushing the networking envelope. And by taking advantage of the “special networking personalities” of these workloads, UEC can significantly reduce networking overheads, boosting bandwidth and workload execution.

The scale of networks is extreme. The UEC is targeting up to a million endpoints, over >100K servers, with each network link >100Gbps and more likely 400-800Gpbs. With the new (AMD and others) networking cards coming out that support 4 400/800Gbps network ports, having a pair of these on each server, with 100K server cluster gives one 800K endpoints. A million is not that far away when you think of it at that scale.

Moreover, LLM training and HPC work are starting to look more alike these days. Yes there are differences but the scale of their clusters are similar, and the way work is sometimes fed to them is similar, which leads to similar networking requirements

UEC is attempting to handle a 5% problem. That is 95% of the users will not have 1M endpoints in their LAN, but maybe 5% will and for these 5%, a more mixed networking workload is unnecessary. In fact, a mixed network becomes a burden slowing down packet transmission.

UEC is finding that with a few select networking parameters, almost like workload fingerprints, network stacks can be much more optimized than current Ethernet and thereby support reduced packet overheads, and more bandwidth.

AI and HPC networks share a very limited set of characteristics which can be used as fingerprints. These characteristics are like reliable or unreliable transport, ordered or unordered delivery, multi-path packet spraying or not, etc, With a set of these types of parameters, selected for an environment, UEC can optimize a network stack to better support a million networking endpoints

We asked where CXL fits in with UEC? DrJ said it could potentially be an entity on the network but he sees CXL more as a within server or between a tight (limited) cluster of servers, solution rather than something on a UEC network.

Just 12 months ago the UEC had 10 members or so and this past week they were up to 60. UEC seems to have struck a chord.

The UEC plans to release a 1.0 specification, near the end of this year. UEC 1.0 is intended to operate on current (>100Gbps) networking equipment with firmware/software changes.

Considering the UEC was just founded in 2023, putting out their 1.0 technical spec. within 1.5 years is astonishing. But also speaks volumes to the interest in the technology.

The UEC has a blog post which talks more about UEC 1.0 specification and the technology behind it.

Dr J Metz, Chair of UEC Steering Committee, Chair of SNIA BoD, Technical Director of Systems Design, AMD

J works to coordinate and lead strategy on various industry initiatives related to systems architecture. Recognized as a leading storage networking expert, J is an evangelist for all storage-related technology and has a unique ability to dissect and explain complex concepts and strategies. He is passionate about the innerworkings and application of emerging technologies.

J has previously held roles in both startups and Fortune 100 companies as a Field CTO,  R&D Engineer, Solutions Architect, and Systems Engineer. He has been a leader in several key industry standards groups, sitting on the Board of Directors for the SNIA, Fibre Channel Industry Association (FCIA), and Non-Volatile Memory Express (NVMe). A popular blogger and active on Twitter, his areas of expertise include NVMe, SANs, Fibre Channel, and computational storage.

J is an entertaining presenter and prolific writer. He has won multiple awards as a speaker and author, writing over 300 articles and giving presentations and webinars attended by over 10,000 people. He earned his PhD from the University of Georgia.

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162: GreyBeards talk cold storage with Steffen Hellmold, Dir. Cerabyte Inc.

Steffen Hellmold, Director, Cerabyte Inc. is extremely knowledgeable about the storage device business. He has worked for WDC in storage technology and possesses an in-depth understanding of tape and disk storage technology trends.

Cerabyte, a German startup, is developing cold storage. Steffen likened Cerabyte storage to ceramic punch cards that dominated IT and pre-IT over much of the last century. Once cards were punched, they created near-WORM storage that could be obliterated or shredded but was very hard to modify. Listen to the podcast to learn more.

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Cerabyte uses a unique combination of semiconductor (lithographic) technology, ceramic coated glass, LTO tape (form factor) cartridge and LTO automation in their solution. So, for the most part, their critical technologies all come from somewhere else.

Their main technology uses a laser-lithographic process to imprint onto a sheet (ceramic coated glass) a data page (block?). There are multiple sheets in each cartridge.

Their intent is to offer a robotic system (based on LTO technology) to retrieve and replace their multi-sheet cartridges and mount them in their read-write drive.

As mentioned above, the write operation is akin to a lithographic data encoded mask that is laser imprinted on the glass. Once written, the data cannot be erased. But it can be obliterated, by something akin to writing all ones or it can be shredded and recycled as glass.

The read operation uses a microscope and camera to take scans of the sheet’s imprint and convert that into data.

Cerabyte’s solution is cold or ultra-cold (frozen) storage. If LTO robotics are any indication, a Cerabyte cartridge with multiple sheets can be presented to a read-write drive in a matter of seconds. However, extracting the appropriate sheet in a cartridge, and mounting it in a read-write drive will take more time. But this may be similar in time to an LTO tape leader being threaded through a tape drive, again a matter of seconds

Steffen didn’t supply any specifications on how much data could be stored per sheet other than to say it’s on the order of many GB. He did say that both sides of a Cerabyte sheet could be recording surfaces.

With their current prototype, an LTO form factor cartridge holds less than 5 sheets of media but they are hoping that they can get this to a 100 or more. in time.

We talked about the history of disk and tape storage technology. Steffen is convinced (as are many in the industry) that disk-tape capacity increases have slowed over time and that this is unlikely to change. I happen to believe that storage density increases tend to happen in spurts, as new technology is adopted and then trails off as that technology is built up. We agreed to disagree on this point.

Steffen predicted that Cerabyte will be able to cross over disk cost/capacity this decade and LTO cost/capacity sometime in the next decade.

We discussed the market for cold and frozen storage. Steffen mentioned that the Office of the Director of National Intelligence (ODNI) has tasked the National Academies of Sciences, Engineering, and Medicine to conduct a rapid expert consultation on large-scale cold storage archives. And that most hyperscalers have use for cold and frozen storage in their environments and some even sell this (Glacier storage) to their customers.

The Library of Congress and similar entities in other nations are also interested in digital preservation that cold and frozen technology could provide. He also thinks that medical is a prime market that is required to retain information for the life of a patient. IBM, Cerabyte, and Fujifilm co-sponsored a report on sustainable digital preservation.

And of course, the media libraries for some entertainment companies represent a significant asset that if on tape has to be re-hosted every 5 years or so. Steffen and much of the industry are convinced that a sizeable market for cold and frozen storage exists.

I mentioned that long archives suffer from data format drift (data formats are no longer supported). Steffen mentioned there’s also software version drift (software that processed that data is no longer available/runnable on current OSs). And of course the current problem with tape is media drift (LTO media formats can be read only 2 versions back).

Steffen seemed to think format and software drift are industry-wide problems and they are being worked on. Cerabyte seems to have a great solution for media drift. As it can be read with a microscope. And the (ceramic glass) media has a predicted life of 100 years or more.

I mentioned the “new technology R&D” problem. Historically, as new storage technology has emerged, they have always end up being left behind (in capacity), because disk-tape-NAND R&D ($Bs each) over spends them. Steffen said it’s certainly NOT B$ of R&D for tape and disk.

Steffen countered by saying that all storage technology R&D spending pales in comparison to semiconductor R&D spending focused on reducing feature size. And as Cerabyte uses semiconductor technologies to write data, sheet capacity is directly a function of semiconductor technology. So, Cerabyte’s R&D technology budget should not be a problem. And in fact they have been able to develop their prototype, with just $7M in funding.

Steffen mentioned there is an upcoming Storage Technology Showcase conference in early March where Cerabyte will be at.

Steffen Hellmold, Director, Cerabyte Inc.

Steffen has more than 25 years of industry experience in product, technology, business & corporate development as well as strategy roles in semiconductor, memory, data storage and life sciences.

He served as Senior Vice President, Business Development, Data Storage at Twist Bioscience and held executive management positions at Western Digital, Everspin, SandForce, Seagate Technology, Lexar Media/Micron, Samsung Semiconductor, SMART Modular and Fujitsu.

He has been deeply engaged in various industry trade associations and standards organizations including co-founding the DNA Data Storage Alliance in 2020 as well as the USB Flash Drive Alliance, serving as their president from 2003 to 2007.

He holds an economic electrical engineering degree (EEE) from the Technical University of Darmstadt, Germany.

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161: Greybeards talk AWS S3 storage with Andy Warfield, VP Distinguished Engineer, Amazon

We talked with Andy Warfield (@AndyWarfield), VP Distinguished Engineer, Amazon, about 10 years ago, when at Coho Data (see our (005:) Greybeards talk scale out storage … podcast). Andy has been a good friend for a long time and he’s been with Amazon S3 for over 5 years now. Since the recent S3 announcements at AWS Re:Invent, we thought it a good time to have him back on the show. Andy has a great knack for explaining technology, I suppose that comes from his time as a professor but whatever the reason, he was great to have on the show again.

Lately, Andy’s been working on S3 Express, One Zone storage, announced last November, a new version of S3 object storage with lower response time. We talked about this later in the podcast but first we touched on S3’s history and other advances. S3 and its ancillary services have advanced considerably over the years. Listen to the podcast to learn more

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S3 is ~18 years old now and was one of the first AWS offerings. It was originally intended to be the internet’s file system which is why it was based on HTTP protocols.

Andy said that S3 was designed for 11-9s durability and high availability options. AWS constantly monitors server and storage failures/performance to insure that they can maintain this level of durability. The problem with durability is that when a drive/server goes down, the data needs to be rebuilt onto another drive before another drive fails. One way to do this is to have more replicas of the data. Another way is to speed up rebuild times. I’m sure AWS does both.

S3 high availability requires replicas across availability zones (AZ). AWS availability zone data centers are carefully located so that they are power-networking isolated from others data centers in the region. Further, AZ site locations are deliberately selected with an eye towards ensuring they are not susceptible to similar physical disasters.

Andy discussed other AWS file data services such as their FSx systems (Amazon FSx for Lustre, for OpenZFS, for Windows File Server, & for NetApp ONTAP) as well as Elastic File System (EFS). Andy said they sped up one of these FSx services by 3-5X over the last year.

Andy mentioned one of the guiding principles for lot of AWS storage is to try to eliminate any hard decisions for enterprise developers. By offering FSx files, S3 objects and their other storage and data services, customers already using similar systems in house can just migrate apps to AWS without having to modify code.

Andy said one thing that struck him as he came on the S3 team was the careful deliberation that occurred whenever they considered S3 API changes. He said the team is focused on the long term future of S3 and any API changes go through a long and deliberate review before implementation.

One workload that drove early S3 adoption was data analytics. Hadoop and BigTable have significant data requirements. Early on, someone wrote an HDFS interface to S3 and over time lots of data analytics activity moved to S3 object hosted data.

Databases have also changed over the last decade or so. Keith mentioned that many customers are foregoing traditional data bases to use open source database solutions with S3 as their backend storage. It turns out that Open Table Format database offerings such as Apache Iceberg, Apache Hudi and Delta Lake are all available on AWS use S3 objects as their storage

We talked a bit about Lambda Server-less processing triggered by S3 objects. This was a new paradigm for computing when it came out and many customers have adopted Lambda to reduce cloud compute spend.

Recently Amazon introduced a file system Mount point for S3 storage. Customers can now use an NFS mount point to access any S3 bucket.

Amazon also supports the Registry for Open Data, which holds just about every canonical data set (stored as S3 objects) used for AI training.

In the last ReInvent, Amazon announced S3 Express One Zone which is a high performance, low latency version of S3 storage. The goal for S3 express was to get latency down from 40-60 msec to less than 10 sec.

They ended up making a number of changes to S3 such as:

  • Redesigned/redeveloped some S3 micro services to reduce latency
  • Restricted S3 Express storage to a single zone reducing replication requirements, but maintained 11-9s durability
  • Used higher performing storage
  • Re-designed S3 API to move some authentication/verification to the beginning of object access from every object access call.

Somewhere during our talk Andy said that, in aggregate, S3 is providing 100TBytes/sec of data bandwidth. How’s that for a scale out storage.

Andy Warfield, VP Distinguished Engineer, Amazon

Andy is a Vice President and Distinguished Engineer in Amazon Web Services. He focusses primarily on data storage and analytics.

Andy holds a PhD from the University of Cambridge, where he was one of the authors of the Xen hypervisor. Xen is an open source hypervisor that was used as the initial virtualization layer in AWS, among multiple other early cloud companies. Andy was a founder at Xensource, a startup based on Xen that was subsequently acquired by Citrix Systems for $500M. Following XenSource,

Andy was a professor at the University of British Columbia (UBC), where he was awarded a Canada Research Chair, and a Sloan Research Fellowship. As a professor, Andy did systems research in areas including operating systems, networking, security, and storage.

Andy’s second startup, Coho Data, was a scale-out enterprise storage array that integrated NVMe SSDs with programmable networks. It raised over 80M in funding from VCs including Andreessen Horowitz, Intel Capital, and Ignition Partners.

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160: GreyBeard talks data security with Jonathan Halstuch, Co-Founder & CTO, RackTop Systems

Sponsored By:

This is the last in this year’s, GreyBeards-RackTop Systems podcast series and once again we are talking with Jonathan Halstuch (@JAHGT), Co-Founder and CTO, RackTop Systems. This time we discuss why traditional security practices can’t cut it alone, anymore. Listen to the podcast to learn more.

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Turns out traditional security practices are keeping the bad guys out or supplies perimeter security with networking equivalents. But the problem is sometimes the bad guy is internal and at other times the bad guys pretend to be good guys with good credentials. Both of these aren’t something that networking or perimeter security can catch.

As a result, the enterprise needs both traditional security practices as well as something else. Something that operates inside the network, in a more centralized place, that can be used to detect bad behavior in real time.

Jonathan talked about a typical attack:

  • A phishing email link is clicked on ==> attacker now owns the laptop/desktop user’s credentials
  • Attacker scans the laptop/desktop for admin credentials or one time pass codes which can be just as good, in some cases ==> the attacker attempts to escalate privileges above the user and starts scanning customer data for anything worthwhile to steal, e.g. crypto wallets, passwords, client data, IP, etc.
  • Attacker copies data of interest and continues to scan for more data and to escalate privileges ==> by now if not later, your data is compromised, either it’s in the hands of others that may want to harm you or extract money from you or it’s been copied by a competitor, or worse a nation state.
  • At some point the attacker has scanned and copied any data of interest ==> at this point, depending on the attacker, they could install malware which can be easily detected to signal the IT organization it’s been compromised.

By the time security systems detect the malware, the attacker has been in your systems and all over your network for months, and it’s way too late to stop them from doing anything they want with your data.

In the past detection like this could have been 3rd party tools that scanned backups for malware or storage systems copying logs to be assessed, on a periodic basis.

The problem with such tools is that they always lag behind the time when the theft/corruption has occurred.

The need to detect in real time, at something like the storage system, is self-evident. The storage is the central point of access to data. If you could detect illegal or bad behavior there, and stop it before it could cause more harm that would be ideal.

In the past, storage system processors were extremely busy, just doing IO. But with today’s modern, multi-core, NUMA CPUs, this is no longer be the case.

Along with high performing IO, RackTop Systems supports user and admin behavioral analysis and activity assessors. These processes run continuously, monitoring user and admin IO and command activity, looking for known, bad or suspect behaviors.

When such behavior is detected, the storage system can prevent further access automatically, if so configured, or at a minimum, warn the security operations center (SOC) that suspicious behavior is happening and inform SOC of who is doing what. In this case, with a click of a link in the warning message, SOC admins can immediately stop the activity.

If it turns out the suspicious behavior was illegal, having the detection at the storage system can also provide SOC a list of files that have been accessed/changed/deleted by the user/admin. With these lists, SOC has a rapid assessment of what’s at risk or been lost.

Jonathan and I talked about RackTop Systems deployment options, which span physical appliances, SAN gateways to virtual appliances. Jonathan mentioned that RackTop Systems has a free trial offer using their virtual appliance that any costumer can download to try them out.

Jonathan Halstuch, Co-Founder & CTO, Racktop Systems

Jonathan Halstuch is the Chief Technology Officer and Co-Founder of RackTop Systems. He holds a bachelor’s degree in computer engineering from Georgia Tech as well as a master’s degree in engineering and technology management from George Washington University.

With over 20-years of experience as an engineer, technologist, and manager for the federal government, he provides organizations the most efficient and secure data management solutions to accelerate operations while reducing the burden on admins, users, and executives.

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159: GreyBeards Year End 2023 Wrap Up

Jason and Keith joined Ray for our annual year end wrap up and look ahead to 2024. I planned to discuss infrastructure technical topics but was overruled. Once we started talking AI, we couldn’t stop.

It’s hard to realize that Generative AI and ChatGPT in particular, haven’t been around that long. We discussed some practical uses Keith and Jason had done with the technology.

Keith mentioned its primary skill is language expertise. He has used it to help write up proposals. He often struggles to convince CTO Advisor non-sponsors of the value they can bring and found that using GenAI has helped do this better.

Jason mentioned he uses it to create BASH, perl, and PowerShell scripts. He says it’s not perfect but can get ~80% there and with a few tweaks, is able to have something a lot faster than if he had to do it completely by hand. He also mentioned its skill in translating from one scripting language to others and how well the code it generates is documented (- that hurt).

I was the odd GreyBeard out, having not used any GenAI, proprietary or not. I’m still working to get a reinforcement learning task to work well and consistently. I figured once I mastered that, I train an LLM on my body of (text and code) work (assuming of course someone gifts me a gang of GPUs).

I agreed GenAI are good at (English) language and some coding tasks (where lot’s of source code exists, such as java, scripting, python, etc.).

However, I was on a MLops slack channel and someone asked if GenAI could help with IBM RPG II code. I answered, probably not. There’s just not a lot of RPG II code publicly accessible on the web and the structure of RPG was never line of text/commands oriented.

We had some heated discussion on where LLMs get the data to train with. Keith was fine with them using his data. I was not. Jason was neutral.

We then turned to what this means to the white collar workers who are coding and writing text. Keith made the point that this has been a concern throughout history, at least since the industrial revolution.

Machines come along, displace work that was done by hand, increase production immensely, reduce costs. Organizations benefit, but people doing those jobs need to up level their skills, to take advantage of the new capabilities.

Easy for us to say, as we, except for Jason, in his present job, are essentially entrepreneurs and anything that helps us deliver more value, faster, easier or less expensively, is a boon for our businesses.

Jason mentioned, Stephen Wolfram wrote a great blog post discussing LLM technology (see What is ChatGPT doing … and why does it work). Both Jason and Keith thought it did a great job about explaining the science and practice behind LLMs.

We moved on to a topic harder to discuss but of great relevance to our listeners, GenAI’s impact on the enterprise.

It reminds me of when Cloud became most prominent. Then “C” suites tasked their staff to adopt “the cloud” anyway they could. Today, “C” suites are tasking their staff to determine what their “AI strategy” is and when will it be implemented.

Keith mentioned that this is wrong headed. The true path forward (for the enterprise) is to focus on what are the business problems and how can (Gen)AI address (some of) them.

AI is so varied and its capabilities across so many fields, is so good nowadays ,that organizations should really look at AI as a new facility that can recognize patterns, index/analyze/transform images, summarize/understand/transform text/code, etc., in near real-time and see where in the enterprise that could help.

We talked about how enterprises can size AI infrastructure needed to perform these activities. And it’s more than just a gaggle of GPUs.

MLcommons’s MLperf benchmarks can help show the way, for some cases, but they are not exhaustive. But it’s a start.

The consensus was maybe deploy in the cloud first and when the workload is dialed in there, re-home it later. With the proviso that hardware needed is available.

Our final topic was the Broadcom VMware acquisition. Keith mentioned their recent subscription pricing announcements vastly simplified VMware licensing, that had grown way too complex over the decades.

And although everyone hates the expense of VMware solutions, they often forget the real value VMware brings to enterprise IT.

Yes hyperscalars and their clutch of coders, can roll their own hypervisor services stacks, using open source virtualization. But the enterprise has other needs for their developers. And the value of VMware virtualization services, now that 128 Core CPUs are out, is even higher.

We mentioned the need for hybrid cloud and how VCF can get you part of the way there. Keith said that dev teams really want something like “AWS software” services running on GCP or Azure.

Keith mentioned that IBM Cloud is the closest he’s seen so far to doing what Dev wants in a hybrid cloud.

We all thought when DNN’s came out and became trainable, and reinforcement learning started working well, that AI had turned a real corner. Turns out, that was just a start. GenAI has taken DNNs to a whole other level and Deepmind and others are doing the same with reinforcement learning.

This time AI may actually help advance mankind, if it doesn’t kill us first. On the latter topic you may want to checkout my RayOnStorage AGI series of blog posts (latest … AGI part-8)

Jason Collier, Principal Member Of Technical Staff at AMD, Data Center and Embedded Solutions Business Group

Jason Collier (@bocanuts) is a long time friend, technical guru and innovator who has over 25 years of experience as a serial entrepreneur in technology.

He was founder and CTO of Scale Computing and has been an innovator in the field of hyperconvergence and an expert in virtualization, data storage, networking, cloud computing, data centers, and edge computing for years.

He’s on LinkedIN. He’s currently working with AMD on new technology and he has been a GreyBeards on Storage co-host since the beginning of 2022

Keith Townsend, President of The CTO Advisor a Futurum Group Company

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.