Category: Storage

Data storage, storage subsystems, NAS systems, direct attached storage

Data and code versioning For MLops

Posted on by Ray in Artificial Intelligence, Data, Data ownership, Data versioning, Machine Learning, MLOps, Object storage

Read an interesting article (Ex-Apple engineers raise … data storage startup) and research paper (Git is for data) about a of group of ML engineers from Apple forming a new “data storage” startup targeted at MLOps teams just like Apple. It turns out that MLops has some very unique data requirements that go way beyond just data storage.

The paper discusses some of the unusual data requirements for MLOps such as:

  • Infrequent updates – yes there are some MLOps datasets where updates are streamed in but the vast majority of MLOps datasets are updated on a slower cadence. The authors think monthly works for most MLOps teams
  • Small changes/lots of copies – The changes to MLOps data are relatively small compared to the overall dataset size and usually consist of data additions, record deletions, label updates, etc. But uncommon to most data, MLOps data are often subsetted or extracted into smaller datasets used for testing, experimentation and other “off-label” activities.
  • Variety of file types – depending on the application domain, MLOps file types range all over the place. But there’s often a lot of CSV files in combination with text, images, audio, and semi-structured data (DICOM, FASTQ, sensor streams, etc.). However within a single domain, MLOps file types are pretty much all the same.
  • Variety of file directory trees – this is very MLOps team and model dependent. Usually there are train/validate/test splits to every MLOps dataset but what’s underneath each of these can vary a lot and needs to be user customizable.
  • Data often requires pre-processing to be cleansed and made into something appropriate and more useable by ML models
  • Code and data must co-evolve together, over time – as data changes, the code that uses them change. Adding more data may not cause changes to code but models are constantly under scrutiny to improve performance, accuracy or remove biases. Bias elimination often requires data changes but code changes may also be needed.

It’s that last requirement, MLOps data and code must co-evolve and thus, need to be versioned together that’s most unusual. Data-code co-evolution is needed for reproducibility, rollback and QA but also for many other reasons as well.

In the paper they show a typical MLOps data pipeline.

Versioning can also provide data (and code) provenance, identifying the origin of data (and code). MLOps teams undergoing continuous integration need to know where data and code came from and who changed them. And as most MLOps teams collaborate in the development, they also need a way to identify data and code conflicts when multiple changes occur to the same artifact.

Source version control

Code has had this versioning problem forever and the solution became revision control systems (RCS) or source version control (SVC) systems. The most popular solutions for code RCS are Git (software) and GitHub (SaaS). Both provide repositories and source code version control (clone, checkout, diff, add/merge, commit, etc.) as well as a number of other features that enable teams of developers to collaborate on code development.

The only thing holding Git/GitHub back from being the answer to MLOps data and code version control is that they don’t handle large (>1MB) files very well.

The solution seems to be adding better data handling capabilities to Git or GitHub. And that’s what XetHub has created for Git.

XetHub’s “Git is Data” paper (see link above) explains what they do in much detail, as to how they provide a better data layer to Git, but it boils down to using Git for code versioning and as a metadata database for their deduplicating data store. They are using a Merkle trees to track the chunks of data in a deduped dataset.

How XetHub works

XetHub support (dedupe) variable chunking capabilities for their data store. This allows them to use relatively small files checked into Git to provide the metadata to point to the current (and all) previous versions of data files checked into the system.

Their mean chunk size is ~4KB. Data chunks are stored in their data store. But the manifest for dataset versions is effectively stored in the Git repository.

The paper shows how using a deduplicated data store can support data versioning.

XetHub uses a content addressable store (CAS) to store the file data chunk(s) as objects or BLOBs. The key to getting good IO performance out of such a system is to have small chunks but large objects.

They map data chunks to files using a CDMT (content defined merkle tree[s]). Each chunk of data resides in at least two different CDMTs, one associated with the file version and the other associated with the data storage elements.

XetHub’s variable chunking approach is done using a statistical approach and multiple checksums but they also offer one specialized file type chunking for CSV files. As it is, even with their general purpose variable chunking method, they can offer ~9X dedupe ratio for text data (embeddings).

They end up using Git commands for code and data but provide hooks (Git filters) to support data cloning, add/checkin, commits, etc.). So they can take advantage of all the capabilities of Git that have grown up over the years to support code collaborative development but use these for data as well as code.

In addition to normal Git services for code and data, XetHub also offers a read-only, NFSv3 file system interface to XetHub datases. Doing this eliminates having to reconstitute and copy TB of data from their code-data repo to user workstations. With NFSv3 front end access to XetHub data, users can easily incorporate data access for experimentation, testing and other uses.

Results from using XetHub

XetHub showed some benchmarks comparing their solution to GIT LFS, another Git based large data storage solution. For their benchmark, they used the CORD-19 (and ArXiv paper, and Kaggle CORD-I9 dataset) which is a corpus of all COVID-19 papers since COVID started. The corpus is updated daily, released periodically and they used the last 50 versions (up to June 2022) of the research corpus for their benchmark.

Each version of the CORD-19 corpus consists of JSON files (research reports, up to 700K each) and 2 large CSV files one with paper information and the other paper (word?) embeddings (a more useable version of the paper text/tables used for ML modeling).

For CORD-19, XetHub are able to store all the 2.45TB of research reports and CSV files in only 287GB of Git (metadata) and datastore data, or with a dedupe factor 8.7X. With XetHub’s specialized CSV chunking (Xet w/ CSV chunking above), the CORD-19 50 versions can be stored in 87GB or with a 28.8X dedupe ratio. And of that 87GB, only 82GB is data and the rest ~5GB is metadata (of which 1.7GB is the merle tree).

In the paper, they also showed the cost of branching this data by extracting and adding one version which consisted of a 75-25% (random) split of a version. This split was accomplished by changing only the two (paper metadata and paper word embeddings) CSV files. Adding this single split version to their code-data repository/datastore only took an additional 11GB of space An aligned split (only partitioning on a CSV record boundary, unclear but presumably with CSV chunking), only added 185KB.

XETHUB Potential Enhancements

XetHub envisions many enhancements to their solution, including adding other specific file type chunking strategies, adding a “time series” view to their NFS frontend to view code/data versions over time, finer granularity data provenance (at the record level rather than at the change level), and RW NFS access to data. Further, XetHub’s dedupe metadata (on the Git repo) only grows over time, supporting updates and deletes to dedupe metadata would help reduce data requirements.

Read the paper to find out more.

Picture/Graphic credit(s):

The Hollowing out of enterprise IT

Posted on by Ray in Cloud services, Forecasting, IoT, Market dynamics, Storage, Strategic Inflection Points, Strategic planning, Visionary leadershp

We had a relatively long discussion yesterday, amongst a bunch of independent analysts and one topic that came up was my thesis that enterprise IT is being hollowed out by two forces pulling in opposite directions on their apps. Those forces are the cloud and the edge.

Western part of the abandoned Packard Automotive Plant in Detroit, Michigan. by Albert Duce

Cloud sirens

The siren call of the cloud for business units, developers and modern apps has been present for a long time now. And their call is more omnipresent than Odysseus ever had to deal with.

The cloud’s allure is primarily low cost-instant infrastructure that just works, a software solution/tool box that’s overflowing, with locations close to most major metropolitan areas, and the extreme ease of starting up.

If your app ever hopes to scale to meet customer demand, where else can you go. If your data can literally come in from anywhere, it usually lands on the cloud. And if you have need for modern solutions, tools, frameworks or just about anything the software world can create, there’s nowhere else with more of this than the cloud.

Pre-cloud, all those apps would have run in the enterprise or wouldn’t have run at all. And all that data would have been funneled back into the enterprise.

Not today, the cloud has it all, its siren call is getting louder everyday, ever ready to satisfy every IT desire anyone could possibly have, except for the edge.

The Edge, last bastion for onsite infrastructure

The edge sort of emerged over the last decade or so kind of in stealth mode. Yes there were always pockets of edge, with unique compute or storage needs. For example, video surveillance has been around forever but the real acceleration of edge deployments started over the last decade or so as compute and storage prices came down drastically.

These days, the data being generated is stagering and compute requirements that go along with all that data are all over the place, from a few ARMv/RISC V cores to a server farm.

For instance, CERN’s LHC creates a PB of data every second of operation (see IEEE Spectrum article, ML shaking up particle physics too). But they don’t store all that. So they use extensive compute (and ML) to try to only store interesting events.

Seismic ships roam the seas taking images of underground structures, generating gobs of data, some of which is processed on ship and the rest elsewhere. A friend of mine creates RPi enabled devices that measure tank liquid levels deployed in the field.

More recently, smart cars are like a data center on tires, rolling across roads around the world generating more data than you want can even imagine. 5G towers are data centers ontop of buildings, in farmland, and in cell towers doting the highways of today. All off the beaten path, and all places where no data center has ever gone before.

In olden days there would have been much less processing done at the edge and more in an enterprise data center. But nowadays, with the advent of relatively cheap computing and storage, data can be pre-processed, compressed, tagged all done at the edge, and then sent elsewhere for further processing (mostly done in the cloud of course).

IT Vendors at the crossroads

And what does the hollowing out of the enterprise data centers mean for IT server and storage vendors, mostly danger lies ahead. Enterprise IT hardware spend will stop growing, if it hasn’t already, and over time, shrink dramatically. It may be hard to see this today, but it’s only a matter of time.

Certainly, all these vendors can become more cloud like, on prem, offering compute and storage as a service, with various payment options to make it easier to consume. And for storage vendors, they can take advantage of their installed base by providing software versions of their systems running in the cloud that allows for easier migration and onboarding to the cloud. The server vendors have no such option. I see all the above as more of a defensive, delaying or holding action.

This is not to say the enterprise data centers will go away. Just like, mainframe and tape before them, on prem data centers will exist forever, but will be relegated to smaller and smaller, niche markets, that won’t grow anymore. But, only as long as vendor(s) continue to upgrade technology AND there’s profit to be made.

It’s just that that astronomical growth, that’s been happening ever since the middle of last century, happen in enterprise hardware anymore.

Long term life for enterprise vendors will be hard(er)

Over the long haul, some server vendors may be able to pivot to the edge. But the diversity of compute hardware there will make it difficult to generate enough volumes to make a decent profit there. However, it’s not to say that there will be 0 profits there, just less. So, when I see a Dell or HPE server, under the hood of my next smart car or inside the guts of my next drone, then and only then, will I see a path forward (or sustained revenue growth) for these guys.

For enterprise storage vendors, their future prospects look bleak in comparison. Despite the data generation and growth at the edge, I don’t see much of a role for them there. The enterprise class feature and functionality, they have spent the decades creating and nurturing aren’t valued as much in the cloud nor are they presently needed in the edge. Maybe I’m missing something here, but I just don’t see a long term play for them in the cloud or edge.

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For the record, all this is conjecture on my part. But I have always believed that if you follow where new apps are being created, there you will find a market ready to explode. And where the apps are no longer being created, there you will see a market in the throws of a slow death.

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Living forever – the end of evolution part-3

Posted on by Ray in Data consistency, Data integrity, Data precision, data protection, Data retention, Storage longevity, Storage reliability, Strategic Inflection Points

Read an article yesterday on researchers who had been studying various mammals and trying to determine the number of DNA mutations they accumulate at about the time they die. The researchers found that after about 800 mutations for mole rats, they die, see Nature article Somatic mutation rates scale with lifespan across mammals and Telegraph article reporting on the research, Mystery of why humans die around 80 may finally be solved.

Similarly, at around 3500 mutations humans die, at around 3000 mutations dogs die and at around 1500 mutations mice die. But the real interesting thing is that the DNA mutation rates and mammal lifespan are highly (negatively) correlated. That is higher mutation rates lead to mammals with shorter life spans.

C. Linear regression of somatic substitution burden (corrected for analysable genome size) on individual age for dog, human, mouse and naked mole-rat samples. Samples from the same individual are shown in the same colour. Regression was performed using mean mutation burdens per individual. Shaded areas indicate 95% confidence intervals of the regression line. A shows microscopic images of sample mammalian cels and the DNA strands examined and B shows the distribution of different types of DNA mutations (substitutions or indels [insertion/deletions of DNA]).

The Telegraph article seems to imply that at 800 mutations all mammals die. But the Nature Article clearly indicates that death is at different mutation counts for each different type of mammal.

Such research show one way on how to live forever. We have talked about similar topics in the distant past see …-the end of evolution part 1 & part 2

But in any case it turns out that one of the leading factors that explains the average age of a mammal at death is its DNA mutation rate. Again, mammals with lower DNA mutation rates live longer on average and mammals with higher DNA mutation rates live shorter lives on average.

Moral of the story

if you want to live longer reduce your DNA mutation rates.

c, Zero-intercept LME regression of somatic mutation rate on inverse lifespan (1/lifespan), presented on the scale of untransformed lifespan (x axis). For simplicity, the y axis shows mean mutation rates per species, although rates per crypt were used in the regression. The darker shaded area indicates 95% CI of the regression line, and the lighter shaded area marks a twofold deviation from the line. Point estimate and 95% CI of the regression slope (k), FVE and range of end-of-lifespan burden are indicated.

All astronauts are subject to significant forms of cosmic radiation which can’t help but accelerate DNA mutations. So one would have to say that the risk of being an astronaut is that you will die younger.

Moon and Martian colonists will also have the same problem. People traveling, living and working there will have an increased risk of dying young. And of course anyone that works around radiation has the same risk.

Note, the mutation counts/mutation rates, that seem to govern life span are averages. Some individuals have lower mutation rates than their species and some (no doubt) have higher rates. These should have shorter and longer lives on average, respectively.

Given this variability in DNA mutation rates, I would propose that space agencies use as one selection criteria, the astronauts/colonists DNA mutation rate. So that humans which have lower than average DNA mutation rates have a higher priority of being selected to become astronauts/extra-earth colonists. One could using this research and assaying astronauts as they come back to earth for their DNA mutation counts, could theoretically determine the impact to their average life span.

In addition, most life extension research is focused on rejuvenating cellular or organism functionality, mainly through the use of young blood, other select nutrients, stem cells that target specific organs, etc. For example, see MIT Scientists Say They’ve Invented a Treatment That Reverses Hearing Loss which involves taking human cells, transform them into stem cells (at a certain maturity) and injecting them into the ear drum.

Living forever

In prior posts on this topic (see parts 1 &2 linked above) we suggested that with DNA computation and DNA storage (see or listen rather, to our GBoS podcast with CTO of Catalog) now becoming viable, one could potentially come up with a DNA program that could

  • Store an individuals DNA using some very reliable and long lived coding fashion (inside a cell or external to the cell) and
  • Craft a DNA program that could periodically be activated (cellular crontab) to access the stored DNA for the individual(in the cell would be easiest) and use this copy to replace/correct any DNA mutation throughout an individuals cells.

And we would need a very reliable and correct copy of that person’s DNA (using SHA256 hashing, CRCs, ECC, Parity and every other way to insure the DNA as captured is stored correctly forever). And the earlier we obtained the DNA copy for an individual human, the better.

Also, we would need a copy of the program (and probably the DNA) to be present in every cell in a human for this to work effectively. .

However, if we could capture a good copy of a person’s DNA early in their life we could, perhaps, sometime later, incorporate DNA code/program into the individual to use this copy and sweep through a person’s body (at that point in time) and correct any mutations that have accumulated to date. Ultimately, one could schedule this activity to occur like an annual checkup.

So yeah, life extension research can continue along the lines they are going and you can have a bunch of point solutions for cellular/organism malfunction OR it can focus on correctly copying and storing DNA forever and creating a DNA program that can correct DNA defects in every individual cell, using the stored DNA.

End of evolution

Yes mammals and that means any human could live forever this way. But it would signify the start of the end of evolution for the human species. That is whenever we captured their DNA copy, from that point on evolution (by mutating DNA) of that individual and any offspring of that individual could no longer take place. And if enough humans do this, throughout their lifespan, it means the end of evolution for humanity as a species

This assumes that evolution (which is natural variation driven by genetic mutation & survival of the fittest) requires DNA variation (essentially mutation) to drive the species forward.

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So my guess, is either we can live forever and stagnate as a species OR live normal lifespans and evolve as a species into something better over time. I believe nature has made it’s choice.

The surprising thing is that we are at a point in humanities existence where we can conceive of doing away with this natural process – evolution, forever.

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NASA’s journey to the cloud – part 1

Posted on by Ray in Cloud storage, Data, Data growth, data services, storage economics, Strategic Inflection Points

Read an article the other day, NASA Turns to the Cloud for Help With Next-Generation Earth Missions about how NASA was had started to migrate all their data to the cloud and intended to store all new data there as well. The hope is that researchers would no longer need to download NASA data but rather could access it directly using cloud compute resources.

It turns out that newer earth science satellites are generating so much data that hosting all this data is becoming a challenge and with the quantities being discussed, researchers downloading the data, to perform research in their own environments may take days.

Until recently, earth science data has been hosted and downloadable from NASA, ESA and other space organization sites. For example, see NASA’s GHCR DAAC (Global Hydrometerological Resource Center Distributed Active Archive Center), ESA EarthOnline, JAXA GPM website, etc. Generally one could download a time series of data from any of their prior and current earth/planetary science missions without too much trouble.

The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) (https://earthdata.nasa.gov/community/community-data-system-programs/measures-projects) Program….

But NASA’s newest earth science satellites will be generating lot’s of data. For instance, the SWOT (Surface Water and Ocean Topography) mission data load will be 20TB/day and the NISAR (NASA-Indian Synthetic Aperture Radar) mission data load will be 80TB/day. And it’s only getting worse as more missions with newer instruments come online.

NASA estimates that, over time, they will store 247PB of data in their EarthData Cloud. At the moment, they have already migrated some (all of ASF [Alaska Satellite Facility] DAAC and some of PO.DAAC [Physical Ocean]) of their Earth Science data to AWS (us-west-2) and over time all of it will migrate there.

NASA will eat any egress charges for EOSDIS data and are also paying any and all hosting fees to storage the data in AWS. Unclear whether they are using standard S3 or S3-Intelligent Tiering. And presumably they are using S3 replication to ensure they don’t lose DAAC data in the cloud, but I don’t see any evidence of that in the literature I’ve read. Of course this doubles the storage costs for their 247PB of DAAC data.

Access to all this data is available to anyone with an EarthData login. There you can register for a profile to access NASA earth sciences data.

NASA’s EarthData also offers a number of AWS cloud based services to help one access this data:

  • EarthData search – filtered search facility to access NASA EarthData by platform (e.g. satellite), instrument (e.g. camera/visual data), organization (e.g. NASA/JPL), etc.
  • EarthData Common Metadata Repository – API driven metadata repository that ” catalogs all data and service metadata records for NASA’s EOSDIS (Earth Observing System Data and Information System) system” data, that can be accessed by anyone, which includes programatic access to EarthData search.
  • EarthData Harmony – which is a EarthData Jupyter notebook examples and API documentation to perform research on earth science data in the EarthData cloud.

One reason to movie EOSDIS DAAC data to the cloud is to allow researchers to not have to download data to run their analysis. By using in cloud EC2 compute instances, they can run their research in AWS with direct , high speed access to the EarthData.

Of course, the researcher would need to purchase their EC2 compute facility directly from AWS. w. NASA publishes a sort of AWS pricing primer for researchers to use AWS EC2 compute to do research directly on the data in the cloud. Also NASA offers a series of tutorials on how to use the AWS cloud for doing research on NASA DAAC data.

Where to from here?

I find this all somewhat discouraging. Yes it’s the Gov’t but one needs to wonder what the overall costs of hosting NASA DAAC data on the AWS cloud will be over the long haul. Most organizations use the cloud to prototype and scale up services but once these services have stabilized, theymigrate them back to onprem/CoLoinfrastructure. See for example, Dropbox’s move away from the [AWS] cloud for ~600PB of data.

I get it, the public cloud allows for nearly infinite data scaleability. But cloud storage costs is not cheap, especially when you are talking about 100s of PBs. And in today’s world, with a whole bunch of open source solutions for object storage and services, one can almost recreate any cloud service in your own data center, at much lower price.

Sure it will still take IT infrastructure and personnel to put it all together. But NASA doesn’t seem to be lacking in infrastructure or IT personnel. Even if you are enamored with AWS services and software infrastructure, one can always run AWS Outpost in your data centers. And DAAC services seem to be pretty stable over time. Yes new satellites will generate more data, but the data load is understood and very predictable. So one should be able to anticipate all this and have infrastructure in place to deal with it.

Yes, having the ability to run analysis in the cloud directly on the data sitting also in the cloud is useful, especially not having to download TB of data. But these costs can also be significant and they are born by the researcher not NASA.

Another grip is why use AWS alone. The other cloud providers all have similar object storage and compute capabilities. It seems wiser to me to set up the EarthData service such that, different DAACs reside in different clouds. This would he more complex and harder to administer and use but I believe in the long run would lead to better more effective services at a more reasonable price.

Going to the cloud doesn’t have to be a one way endeavor. After using the cloud for a while, NASA should have a better idea of the costs of doing so and at that time understand better what it can and cannot afford to do on its own.

It will be interesting to see what ESA, JAXA, CERN and other big science organizations do as they are all in the same bind, data seems to be growing unbounded.

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CTERA, Cloud NAS on steroids

Posted on by Ray in Cloud services, Cloud storage, data access, Data consistency, Data security, Distributed computing, File Storage, Mobile computing, Object storage, storage scalability, Strategic Inflection Points

We attended SFD22 last week and one of the presenters was CTERA, (for more information please see SFD22 videos of their session) discussing their enterprise class, cloud NAS solution.

We’ve heard a lot about cloud NAS systems lately (see our/listen to our GreyBeards on Storage podcast with LucidLink from last month). Cloud NAS systems provide a NAS (SMB, NFS, and S3 object storage) front-end system that uses the cloud or onprem object storage to hold customer data which is accessed through the use of (virtual or hardware) caching appliances.

These differ from file synch and share in that Cloud NAS systems

  • Don’t copy lots or all customer data to user devices, the only data that resides locally is metadata and the user’s or site’s working set (of files).
  • Do cache working set data locally to provide faster access
  • Do provide NFS, SMB and S3 access along with user drive, mobile app, API and web based access to customer data.
  • Do provide multiple options to host user data in multiple clouds or on prem
  • Do allow for some levels of collaboration on the same files

Although admittedly, the boundary lines between synch and share and Cloud NAS are starting to blur.

CTERA is a software defined solution. But, they also offer a whole gaggle of hardware options for edge filers, ranging from smart phone sized, 1TB flash cache for home office user to a multi-RU media edge server with 128TB of hybrid disk-SSD solution for 8K video editing.

They have HC100 edge filers, X-Series HCI edge servers, branch in a box, edge and Media edge filers. These later systems have specialized support for MacOS and Adobe suite systems. For their HCI edge systems they support Nutanix, Simplicity, HyperFlex and VxRail systems.

CTERA edge filers/servers can be clustered together to provide higher performance and HA. This way customers can scale-out their filers to supply whatever levels of IO performance they need. And CTERA allows customers to segregate (file workloads/directories) to be serviced by specific edge filer devices to minimize noisy neighbor performance problems.

CTERA supports a number of ways to access cloud NAS data:

  • Through (virtual or real) edge filers which present NFS, SMB or S3 access protocols
  • Through the use of CTERA Drive on MacOS or Windows desktop/laptop devices
  • Through a mobile device app for IOS or Android
  • Through their web portal
  • Through their API

CTERA uses a, HA, dual redundant, Portal service which is a cloud (or on prem) service that provides CTERA metadata database, edge filer/server management and other services, such as web access, cloud drive end points, mobile apps, API, etc.

CTERA uses S3 or Azure compatible object storage for its backend, source of truth repository to hold customer file data. CTERA currently supports 36 on-prem and in cloud object storage services. Customers can have their data in multiple object storage repositories. Customer files are mapped one to one to objects.

CTERA offers global dedupe, virus scanning, policy based scheduled snapshots and end to end encryption of customer data. Encryption keys can be held in the Portals or in a KMIP service that’s connected to the Portals.

CTERA has impressive data security support. As mentioned above end-to-end data encryption but they also support dark sites, zero-trust authentication and are DISA (Defense Information Systems Agency) certified.

Customer data can also be pinned to edge filers, Moreover, specific customer (director/sub-directorydirectories) data can be hosted on specific buckets so that data can:

  • Stay within specified geographies,
  • Support multi-cloud services to eliminate vendor lock-in

CTERA file locking is what I would call hybrid. They offer strict consistency for file locking within sites but eventual consistency for file locking across sites. There are performance tradeoffs for strict consistency, so by using a hybrid approach, they offer most of what the world needs from file locking without incurring the performance overhead of strict consistency across sites. For another way to do support hybrid file locking consistency check out LucidLink’s approach (see the GreyBeards podcast with LucidLink above).

At the end of their session Aron Brand got up and took us into a deep dive on select portions of their system software. One thing I noticed is that the portal is NOT in the data path. Once the edge filers want to access a file, the Portal provides the credential verification and points the filer(s) to the appropriate object and the filers take off from there.

CTERA’s customer list is very impressive. It seems that many (50 of WW F500) large enterprises are customers of theirs. Some of the more prominent include GE, McDonalds, US Navy, and the US Air Force.

Oh and besides supporting potentially 1000s of sites, 100K users in the same name space, and they also have intrinsic support for multi-tenancy and offer cloud data migration services. For example, one can use Portal services to migrate cloud data from one cloud object storage provider to another.

They also mentioned they are working on supplying K8S container access to CTERA’s global file system data.

There’s a lot to like in CTERA. We hadn’t heard of them before but they seem focused on enterprise’s with lots of sites, boatloads of users and massive amounts of data. It seems like our kind of storage system.

Comments?

Storageless data!?

Posted on by Ray in data access, data mobility, Data QoS, Distributed computing, File Storage, Strategic Inflection Points

I (virtually) attended SFD21 earlier this year and a company called Hammerspace presented discussing their vision for storageless data (see videos of their session at SFD21).

We’ve talked them before but now they have something to offer the enterprise – data mobility or storageless data.

The white board after David Flynn’s session at SFD8

In essence, customers want to be able to run their workloads wherever it makes the most sense, on prem, in private cloud, and in the public cloud among other places. Historically, it’s been relatively painless to transfer an application’s binary from one to another data center, to a managed service provider or to the public cloud.

And with VMware Cloud Foundation, Kubernetes, Docker and Linux operating everywhere, the runtime environment and other OS services that applications depend on are pretty much available in any of those locations. So now customers have 2 out of 3, what’s left?

It’s all about the Data

Data can take a very long time to move around a data center, let alone across the web between locations. MBs and even GBs of data may be relatively painless to move, but TBs of data can be take days, and moving PBs of data is suicidal.

For instance, when we signed up for a globally accessible file synch and share storage service, I probably had 75GB or so of data I wanted managed. It took literally several days of time to upload this. Yes, I didn’t have data center class internet access, but even that might have only sped this up 2-5X. Ok, now try this with 1TB or more and it’s pretty much going to take days, and you can easily multiple that by 10 to do a PB or more. And that’s if it happens to continue to perform the transfer without disruption.

So what’s Hammerspace storageless data got to do with any of this.

Hammerspace’s idea

It’s been sort of a ground truth of storage, since I’ve been in the industry (40+ years now), that not all random IO data is accessed at the same frequency. That is, some data is accessed a lot and other data accessed hardly at all. That’s why DRAM caching of data can be so important to a host or storage system.

Similarly for sequential access, if you can get the first blocks of data to the host and then stream the rest in time, a storage system can appear to read fast.

Now I won’t go into all the tricks of doing good data caching, (the secret sauce to every vendor’s enterprise storage), but if you can appear to cache data well, you don’t actually need to transfer all the data associated with an application to a location it’s running in, you can appear as if all the data is there, when actually only some of it is present.

Essentially, Hammerspace creates a global file system for your data, across any locations you wish to use it, with great caching, optimized data transfer and with real storage behind it. Servers running your applications mount a Hammerspace file system/share that stitches together all the file storage behind it, across all the locations it’s operating in.

An application request goes to Hammerspace and if the data is not present there, Hammerspace goes and fetches and caches blocks of data as fast as it can. This will let the application start performing IO while the rest of the data is being cached and if allowed, moved to the new location.

Storage can be not managed by Hammerspace, read-write managed by Hammerspace or read-only managed by Hammerspace. For customers who want the whole Hammerspace storageless data functionality they would use read-write mode. For those who just want to access data elsewhere read-only would suffice. Customers who want to continue to access data directly but want read access globally, would use the read-only mode.

Once read-write storage is assigned to Hammerspace grabs all the file metadata information on the storage system. Once this process completes, customers no longer access this file data directly, but rather must access it through Hammerspace. At that point, this data is essentially storageless and can be accessed wherever Hammerspace services are available.

How does Hammerspace do it

Behind the scenes is a lot of technology. Some of which is discussed in the SFD21 sessions (see video’s above). Hammerspace is not in the data path but rather in the control path of data access. But it does orchestrate data movement, and it does route data IO requests from an application to where the data (currently) resides.

Hammerspace also supports Service Level Objectives (SLOs) for performance, geolocation, security, data protection options,, etc. These can be used to keep data in particular regions, to encrypt data (using KMIP), ensure high performance, high data availability, etc.

Hammerspace can manage data across 32 separate sites. It takes a couple of hours to deploy. per site. Each site has a Hammerspace metadata service with standalone access to all data within that site. For example, standalone access could be used, in the event of a network loss.

At the moment, they support eventual consistency and don’t support a global lock service. Rather, Hammerspace uses a conflict resolution service in the event data is overwritten by two or more applications. For any file that was being updated in two or more locations, that file would be flagged as in conflict, Hammerspace would provide snapshots of the various versions of the file(s) and it would require some sort of manual intervention to resolve the conflict. Each location would have (temporary) access to the data it had written directly, but at some point the conflict would need resolution.

They also support NFS and SMB file access for the front end and use object storage services for backend data. Data is copied on demand to the local site’s storage when accessed based on the SLO policies in effect for it. During data movement it is copied up, temporarily into objects on AWS, Microsoft Azure, or GCP, and then copied down to the location it’s being moved to. I believe this temporary object data is encrypted and compressed. Hammerspace support KMIP key providers.

Pricing for Hammerspace is on a managed capacity basis. But anyone can use Hammerspace for up to 10TB for free. Hammerspace is available in AWS marketplace for configuration there.

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Well it’s been a long time coming, but it appears to be here. Any customers wanting hybrid-cloud operations or global access to their data would be remiss to not check out Hammerspace.

[Edited after posting, The Eds.]

Data Science storage with NetApp’s Python Toolkit

Posted on by Ray in data access, Data analytics, data logistics, Data science, Deep Learning, Machine Learning, Storage

I’ve got a book someplace (yet to be read completely) with the title Data science with Python. At a recent Storage Field Day 21 last month, NetApp was there discussing a number of their product offerings one of which was their Python SDK to manage NetApp storage for data scientists and AI researchers (see videos of their sessions here).

I’m not a data science expert but a Python SDK for storage management just makes so much sense to me I just had to take a look. Their GitHub repo is available online and they call it the NetApp Data Science Toolkit.

But first please take our new poll:

The challenge for data science and AI researchers is that it’s all about the data. How do you find the data, gain access to it, clean it, and process it quickly so you can do it all over again. Having some sort of Python SDK that allows you to do some rudimentary storage volume configuration, access, snapshotting etc. can make these sorts of pipelines be self-serviced rather than going back and forth with operations to get volumes configured, mounted, and services established.

NetApp Data Science Toolkit

The NetApp Data Science Toolkit can be PIP installed into anything with Python 3.5 or later and can be invoked via a command line or as a library of Python functions that can be invoked. The command line utility and the Python calls appear to be functionally equivalent. 

pip3 install netapp-ontap pandas tabulate requests boto3

The Toolkit must be configured for your environment and NetApp storage but once that’s done your ready to rock and roll.

MLOps pipeline from Google

The command line is invoked with 

./ntap_dsutil.py

following that command are subcommands and parameters specifying what ONTAP operation you want to perform and how it is to be done. Python function calls seem to follow the same parameterization as the CLI.

The CLI and Python function calls can run on MacOS or any Linux distribution. There’s a paper that discusses how to use the SDK to accelerate AI pipelines as well as another ReadMe that describes it’s use in Kubernetes with NetApp’s Trident CSI plugin.

The functionality supports NetApp AFF, FAS, Cloud Volumes and Select that are running ONTAP 9.7 or later. For a current list of ONTAP functions available, check out the toolkit. But for a overview these ONTAP functions were available.

  • For Volume Management – cloning, creating, listing all, deleting or mounting a volume,
  • For Snapshot Management – creating, deleting, listing and restoring snapshots (of volumes)
  • For Data Fabric Management – listing all cloud sync relationships, triggering a cloud sync operation, multi-thread pulling a bucket down from S3 storage (into a NetApp volume directory), pulling a single object down from S3 into a file, pushing the contents of a directory to bucket on S3 and pushing a file into an object on S3.
  • For Advanced Data Fabric Management – listing all SnapMirror relationships and triggering a sync operation for an existing SnapMirror relationship.

This is a pretty comprehensive list of NetApp ONTAP storage functionality. Having all this under control of Python and CLI for data scientist or AI researcher seems pretty impressive.

Of course not every option for all those functions are supported but it’s just a start (V1.1 of the toolkit). I’m sure there’s more to come, especially if customers demand it.

However, it would be nice to have an ONTAP simulator available with the toolkit that could be used to test out your Python code and CLI commands before using real NetApp storage. This would be very useful for those of us lacking our own test ONTAP storage, just hanging around on prem or in the cloud.

As Python becomes the language of choice for AI and now data science, it seems only natural that storage and data protection companies would start releasing Python SDKs/APIs for their product functionality. That way AI and data science researchers could embed any storage functionality they needed directly into their Python code or Jupyter Notebook application.

Having a Python SDK for NetApp ONTAP storage, means using data storage for your MLops or data science pipelines is that much easier.

Great move by NetApp. Ok where’s the rest of the industry?

Picture credit(s):

New DRAM can be layered on top of CPU cores

Posted on by Ray in Artificial Intelligence, Deep Learning, Machine Learning, Market dynamics, Processing performance, Storage class memories, Strategic Inflection Points

At the last IEDM (IEEE International ElectronDevices Meenting), there were two sessions devoted to a new type of DRAM cell that consists or 2 transistors and no capacitors (2TOC) that can be built in layers on top of a micro processor which doesn’t disturb the microprocessor silicon. I couldn’t access (behind paywalls) the actual research but one of the research groups was from Belgium (IMEC) and the other from the US (Notre Dame and R.I.T). This was written up in a couple of teaser articles in the tech press (see IEEE Spectrum tech talk article).

DRAM today is built using 1 transistor and 1 capacitor (1T1C). And it appears that capacitors and logic used for microprocessors aren’t very compatible. As such, most DRAM lives outside the CPU (or microprocessor cores) chip and is attached over a memory bus.

New 2T0C DRAM Bit Cell: Data is written by appliying current to the WBL and WWL and bit’s are read by seeing if acurrent can pass through the RWL RBL

Memory busses have gotten faster in order to allow faster access to DRAM but this to is starting to reach fundamental physical limits and DRAM memory sizes aren’t scaling like the used to.

Wouldn’t it be nice if there were a new type of DRAM that could be easlly built closer or even layered on top of a CPU chip, with faster direct access from/to CPU cores. through inter chip electronics.

Oxide based 2T0C DRAM

DRAM was designed from the start with 1T1C so that it could hold a charge. With a charge in place it could be read out quickly and refreshed periodically without much of a problem.

The researcher found that at certain sizes (and with proper dopants) small transistors can also hold a (small) charge without needing any capacitor.

By optimizing the chemistry used to produce those transistors they were able to make 2T0C transistors hold memory values. And given the fabrication ease of these new transistors, they can easily be built on top of CPU cores, at a low enough temperature so as not to disturb the CPU core logic.

But, given these characteristics the new 2T0C DRAMB can also be built up in layers. Just like 3D NAND and unlike current DRAM technologies.

Today 3D NAND is being built at over 64 layers, with Flash NAND roadmap’s showing double or quadruple that number of layers on the horizon. Researchers presenting at IMEC were able to fabricate an 8 layer 2T0C DRAM on top of a microprocessor and provide direct, lightening fast access to it.

The other thing about the new DRAM technology is that it doesn’t need to be refreshed as often. Current DRAM must be refreshed every 64 msec. This new 2T0C technology has a much longer retention time and currently only needs to be refreshed every 400s and much longer retention times are technically feasible.

Some examples of processing needing more memory:

  • AI/ML and the memory wall -Deep learning models are getting so big that memory size is starting to become a limiting factor in AI model effectiveness. And this is just with DRAM today. Optane and other SCM can start to address some of this problem but ithe problem doesn’t go away, AI DL models are just getting more complex I recently read an article where Google trained a trillion parameter language model.
  • In memory databases – SAP HANA is just one example but they are other startups as well as traditional database providers that are starting to use huge amounts of memory to process data at lightening fast speeds. Data only seems to grow not shrink.

Yes Optane and other SCM today can solve some of thise problems. But having a 3D scaleable DRAM memory, that can be built right on chip core, with longer hold times and faster direct access can be a game changer.

It’s unclear whether we will see all DRAM move to the new 2T0C format, but if it can scale well in the Z direction has better access times, and longer retention, it’s unclear why this wouldn’t displace all current 1T1C DRAM over time. However, given the $Bs of R&D spend on new and current DRAM 1T1C fabrication technology, it’s going to be a tough and long battle.

Now if the new 2T0C DRAM could only move from 1 bit per cell to multiple bits per cell, like SLC to MLC NAND, the battle would heat up considerably.

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