Cambrian Explosion of AI DL app’s in industry and the world

I was at the NetApp Insight conference last week and recorded a podcast (see: GreyBeards Podcast) on what NetApp is doing in the AI DL (Deep Learning) space. On the podcast, we talked about a number of verticals that were deploying AI DL right now and using it to improve outcomes.

It was only is 2012 that AI DL broke out and pretty much conquered the speech recognition contest by improving recognition accuracy by leaps and bounds. Prior to that improvements had been very small and incremental at best. Here we are, just 7 years later and AI DL models are proliferating across industry and every other sector of the world economy.

DL applications in the real world

At the show. we talked about AI DL models being used in healthcare (radiological image analysis, cell counts for infection assessments), automotive (self driving cars), financial services (fraud detection), and retail (predicting how make up would look on someone).

And early this year, at HPE Discover, they discussed a new technique to share training data but still keep it private. In this case, they use block chain technology to publish and share a DL neural network model weights and other hyper parameters trained for some real world purpose.

Customers download and use the model in their day to day activities but record the data that their model analyzes and its predictions. They use this data to update (re-train) their DL neural net. They then publish their new neural net model weights and other parameters to all the other customers. Each customer of the model do the same, updating (re-training) their DL neural net.

At some point an owner or global model arbitrator takes all these individual model updates and aggregates the neural net weights, into a new neural net model and publishes the new model. And then the process starts over again. In this way, training data is never revealed, kept secure and private but DL model updates that result from re-training the model with secured private data would be available to any customer.

Recently, there’s been a slew of articles across many different organizations that show how AI DL is being adopted to work in different areas:

And that’s just a sample of the last few weeks of papers of AI DL activity.

Next Steps

All it takes is data, that can be quantified and classified. With data and classifications in hand, anyone can train a DL model that performs that classification. It doesn’t require GPU farms, decent CPUs are up to the task for TB of data.

But if you want better prediction/classificatoin accuracy, you will need more data which means longer AI DL training runs. So at some point, maybe at >100TB of data, or use AI DL training a lot, you may want that GPU farm.

The Deep Learning with Python book (my favorite) has a number of examples such as, sentiment analysis of text, median real estate pricing predictions, generating text that looks like an authors work, with maybe a dozen more that one can use to understand AI DL technology. But it’s not rocket science, I believe any qualified programmer could do it, with some serious study.

So the real question is what are you doing with your data to make use of AI DLmodels now?

I suppose the other question ought to be, how can you collect more data and classification information, to train more AI DL models?

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It’s great to be in the storage business.

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Where should IoT data be processed – part 1

I was at FlashMemorySummit 2019 (FMS2019) this week and there was a lot of talk about computational storage (see our GBoS podcast with Scott Shadley, NGD Systems). There was also a lot of discussion about IoT and the need for data processing done at the edge (or in near-edge computing centers/edge clouds).

At the show, I was talking with Tom Leyden of Excelero and he mentioned there was a real need for some insight on how to determine where IoT data should be processed.

For our discussion let’s assume a multi-layered IoT architecture, with 1000s of sensors at the edge, 100s of near-edge processing/multiplexing stations, and 1 to 3 core data center or cloud regions. Data comes in from the sensors, is sent to near-edge processing/multiplexing and then to the core data center/cloud.

Data size

Dans la nuit des images (Grand Palais) by dalbera (cc) (from flickr)
Dans la nuit des images (Grand Palais) by dalbera (cc) (from flickr)

When deciding where to process data one key aspect is the size of the data. Tin GB or TB but given today’s world, can be PB as well. This lone parameter has multiple impacts and can affect many other considerations, such as the cost and time to transfer the data, cost of data storage, amount of time to process the data, etc. All of these sub-factors include the size of the data to be processed.

Data size can be the largest single determinant of where to process the data. If we are talking about GB of data, it could probably be processed anywhere from the sensor edge, to near-edge station, to core. But if we are talking about TB the processing requirements and time go up substantially and are unlikely to be available at the sensor edge, and may not be available at the near-edge station. And PB take this up to a whole other level and may require processing only at the core due to the infrastructure requirements.

Processing criticality

Human or machine safety may depend on quick processing of sensor data, e. g. in a self-driving car or a factory floor, flood guages, etc.. In these cases, some amount of data (sufficient to insure human/machinge safety) needs to be done at the lowest point in the hierarchy, with the processing power to perform this activity.

This could be in the self-driving car or factory automation that controls a mechanism. Similar situations would probably apply for any robots and auto pilots. Anywhere some IoT sensor array was used to control an entity, that could jeopardize the life of human(s) or the safety of machines would need to do safety level processing at the lowest level in the hierarchy.

If processing doesn’t involve safety, then it could potentially be done at the near-edge stations or at the core. .

Processing time and infrastructure requirements

Although we talked about this in data size above, infrastructure requirements must also play a part in where data is processed. Yes sensors are getting more intelligent and the same goes for near-edge stations. But if you’re processing the data multiple times, say for deep learning, it’s probably better to do this where there’s a bunch of GPUs and some way of keeping the data pipeline running efficiently. The same applies to any data analytics that distributes workloads and data across a gaggle of CPU cores, storage devices, network nodes, etc.

There’s also an efficiency component to this. Computational storage is all about how some workloads can better be accomplished at the storage layer. But the concept applies throughout the hierarchy. Given the infrastructure requirements to process the data, there’s probably one place where it makes the most sense to do this. If it takes a 100 CPU cores to process the data in a timely fashion, it’s probably not going to be done at the sensor level.

Data information funnel

We make the assumption that raw data comes in through sensors, and more processed data is sent to higher layers. This would mean at a minimum, some sort of data compression/compaction would need to be done at each layer below the core.

We were at a conference a while back where they talked about updating deep learning neural networks. It’s possible that each near-edge station could perform a mini-deep learning training cycle and share their learning with the core periodicals, which could then send this information back down to the lowest level to be used, (see our Swarm Intelligence @ #HPEDiscover post).

All this means that there’s a minimal level of processing of the data that needs to go on throughout the hierarchy between access point connections.

Pipe availability

binary data flow

The availability of a networking access point may also have some bearing on where data is processed. For example, a self driving car could generate TB of data a day, but access to a high speed, inexpensive data pipe to send that data may be limited to a service bay and/or a garage connection.

So some processing may need to be done between access point connections. This will need to take place at lower levels. That way, there would be no need to send the data while the car is out on the road but rather it could be sent whenever it’s attached to an access point.

Compliance/archive requirements

Any sensor data probably needs to be stored for a long time and as such will need access to a long term archive. Depending on the extent of this data, it may help dictate where processing is done. That is, if all the raw data needs to be held, then maybe the processing of that data can be deferred until it’s already at the core and on it’s way to archive.

However, any safety oriented data processing needs to be done at the lowest level and may need to be reprocessed higher up in the hierachy. This would be done to insure proper safety decisions were made. And needless the say all this data would need to be held.

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I started this post with 40 or more factors but that was overkill. In the above, I tried to summarize the 6 critical factors which I would use to determine where IoT data should be processed.

My intent is in a part 2 to this post to work through some examples. If there’s anyone example that you feel may be instructive, please let me know.

Also, if there’s other factors that you would use to determine where to process IoT data let me know.

Improving floating point

Read a post this week in Reddit pointing to an article that was from The Next Platform (New approach could sink floating point computation). It was all about changing IEEE floating point format to something better called posits, which was designed by noted computer architect, John Gustafson, et al, (see their paper Beating floating point at its own game: Posit arithmetic, for more info).

The problems with standard floating point have been known since they were first defined, in 1985 by the IEEE. As you may recall, an IEEE 754 floating point number has three parts a sign, an exponent and a mantissa (fraction or significand part). Both the exponent and mantissa can be negative.

IEEE defined floating point numbers

The IEEE 754 standard defines the following formats (see Floating point Floating -point arithmetic, for more info)

  • Half precision floating point, (added in 2008), has 1 sign bit (for the significand or mantissa), 5 exponent bits (indicating 2**-62 to 2**+64) and 10 significand bits for a total of 16 bits.
  • Single precision floating point, has 1 sign bit, 8 exponent bits (indicating 2**-126 to 2**+128) and 23 significand bits for a total of32 bits.
  • Double precision floating point, has 1 sign bit, 11 exponent bits (2**-1022 to 2**+1024) and 52 significand bits.
  • Quadrouple precision floating point, has 1 sign bit, 15 exponent bits (2**-16,382 to 2**+16,384) and 112 significand bits.

I believe Half precision was introduced to help speed up AI deep learning training and inferencing.

Some problems with the IEEE standard include, it supports -0 and +0 which have different representations and -∞ and +∞ as well as can be used to represent a number of unique, Not-a-Numbers or NaNs which are illegal floating point numbers. So when performing IEEE standard floating point arithmetic, one needs to check to see if a result is a NaN which would make it an illegal result, and must be wary when comparing numbers such as -0, +0 and -∞ , +∞. because, sigh, they are not equal.

Posits to the rescue

It’s all a bit technical (read the paper to find out) but posits don’t support -0 and +0, just 0 and there’s no -∞ or +∞ in posits either, just ∞. Posits also allow for a variable number of exponent bits (which are encoded into Regime scale factor bits [whose value is determined by a useed factor] and Exponent scale factor bits) which means that the number of significand bits can also vary.

So, with a 32 bit, single precision Posit, the number range represented can be quite a bit larger than single precision floating point. Indeed, with the approach put forward by Gustafson, a single 32 bit posit has more numeric range than a single precision IEEE 754 float and about as 1/2 as much range as double precision IEEE floating point number but only uses 32 bits.

Presently, there’s no commercial hardware implementations of posits, but there’s a lot of interest. Mostly because, the same number of bits can represent a lot more numeric range than equivalently sized IEEE 754 floats. And for HPC environments, AI deep learning applications, scientific computing, etc. having more numeric range (or precision), in less space, means they can jam more data in the same storage, transfer more data over the same networking bandwidth and save more numbers in limited amounts of DRAM.

Although, commercial implementations do not exist, there’s been some FPGA simulations of posit floating point arithmetic. Those simulations have shown it to be more energy efficient than IEEE 754 floating point arithmetic for the same number of bits. So, you need to add better energy efficiency to the advantages of posit arithmetic.

Is it any wonder that HPC/big science (weather prediction, Square Kilometer Array, energy simulations, etc.) and many AI hardware accelerator chip designers are examining posits as a potential way to boost precision, reduce storage/memory footprint and reduce energy consumption.

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Yet, standards have a way of persisting. Just look at how long the QWERTY keyboard has lasted. It was originally designed in the 1870’s to slow down typing and reduce jamming, when typewriters were mechanical devices. But ever since 1934, when the DVORAK keyboard was patented, there’s been much better layouts for keyboards. And there’s no arguing that the DVORAK keyboard is better for typing on non-mechanical typewriters. Yet today, I know of no computer vendor that ships DVORAK labeled keyboards. Once a standard becomes set, it’s very hard to dislodge.

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All that AI DL training data comes from us

Read a couple of articles the past few weeks that highlighted something that not many of us are aware of, most of the data used to train AI deep learning (DL) models comes from us.

That is through our ignorance or tacit acceptation of licenses for apps that we use every day and for just walking around/interacting with the world.

The article in Atlantic, The AI supply chain runs on ignorance, talks about Ever, a picture sharing app (like Flickr), where users opted in to its facial recognition software to tag people in pictures. Ever also used that (tagged by machine or person) data to train its facial recognition software which it sells to government agencies throughout the world.

The second article, in Engadget , Colorado College students were secretly used to train AI facial recognition (software), talks about a group using a telephoto security camera than was pointed at a high traffic area on campus. The data obtained was used to help train an AI DL model to identify facial characteristics from far away.

The article went on to say that gathering photos from people in public places is not against the law. The study was also cleared by the school. The database was not released until after the students graduated but it did have information about the time and date the photos were taken.

But that’s nothing…

The same thing applies to video sharing and photo animation models, podcasting and text speaking models, blogging and written word generation models, etc. All this data is just lying around the web, freely available for any AI DL data engineer to grab and use to train their models. The article which included the image below talks about a new dataset of millions of webpages.

From an OpenAI paper on better language models showing the accuracy of some AI DL models “trained on a new dataset of millions of webpages called WebText.”

,Google photo search is scanning the web and has access to any photo posted to use for training data. Facebook, IG, and others have millions of photos that people are posting online every day, many of which are tagged, with information identifying people in the photos. I’m sure some where there’s a clause in a license agreement that says your photos, when posted on our app, no longer belong to you alone.

As security cameras become more pervasive, camera data will readily be used to train even more advanced facial recognition models without your say so, approval or even appreciation that it is happening. And this is in the first world, with data privacy and identity security protections paramount, imagine how the rest of the world’s data will be used.

With AI DL models, it’s all about the data. Yes much of it is messy and has to be cleaned up, massaged and sometimes annotated to be useful for DL training. But the origins of that training data are typically not disclosed to the AI data engineers nor the people that created it.

We all thought China would have a lead in AI DL because of their unfettered access to data, but the west has its own way to gain unconstrained access to vast amounts of data. And we are living through it today.

Yes AI DL models have the potential to drastically help the world, humanity and government do good things better. But a dark side to AI DL models also exist to help bad actors, organizations and even some government agencies do evil.

Caveat usor (May the user beware)

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Photo Credit(s): “Still Watching You” by jhcrow is licensed under CC BY-NC 2.0 

Computational Photography Homework 1 Results.” by kscottz is licensed under CC BY-NC 2.0 

From Language models are unsupervised multi-task learners OpenAI research paper

ZooArchNet.org, a new collaboration for zoological-archeological data

Read an article the other day about a new collaboration data platform, the ZooArchNet, for archeological and zoological data ( data about animals and the history of humankind).

The collaboration was started at the Florida Museum at the University of Florida. They intend to construct a database that would allow researchers to track the history of animals and how humans have interacted with them over time.

The problem is there’s a lot of historical animal specimen information available in various locations/sites around the world and similarly, there’s a lot of data about the history of humanity, but there’s little that cross links the two. And by missing those cross links, researchers aren’t seeing the big picture, that humankind and animal-kind have co-existed since the dawn of time and have impacted each other throughout their history.

However, if there was a site where one could trace the history of animal and human life, across time, in a region, one could develop a better understanding of how they interact and impact one another.

Humankind interacting with animalkind

In the article, they discuss a number of examples where animals have been impacted by humankind over time. For example, originally the Mexican Turkey was domesticated for its feathers during Mayan, Aztec and other civilizations of Central America,. but over time it became a prized for use as food. While this was occurring, its range expanded considerably throughout North (and South) America.

It’s the understanding of habitat range over time and how humankind helped or hindered this range that’s best served by linking zoological and archeological data sets that exist in research libraries throughout the world.

How it works

One problem in cross linking such data is that it often exists in different formats and uses different metadata to describe it.

A key, early decision was to use a standard metadata format ,the Darwin Core (DwC) an outgrowth of the Dublin Core which is more focused on zoological data.

With this in place, the next problem was to translate specimen metadata into the DwC and extract the actual data (or URI’s) that described the specimen for harvesting. Once all that was accomplished they could migrate the specimen data or archeological data and host it/cross link it in their ZooArchNet database.

For example, the researchers at Florida Museum used the Open Context database to provide archeological informationand the Global Biodiversity Information Facility (GBIF) to supply biological diversity information and together the two were linked and cross indexed in the ZooArchNet database.

Once the data was available and located in Google Cloud storage, researchers could use Google BigQuery data analytics as well as other apps like (Google) indexers to create more data rich views and searchable indices for their ZooArchNet and VertNet web portals.

ZooArchNet is just starting. Most of the information currently available is about the few examples chosen to demonstrate the technology. As with anything like this, there’s a certain amount of crowd sourcing needed to make it worthwhile. It’s popularity will be a prime determinant on its usefulness over time. But anything that helps the world understand the true history of humanity’s impact on this life of this planet is worthwhile.

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Photo Credit(s): “turkey bird” by watts photos1 is licensed under CC BY 2.0 

Workflow from ZooArchNet: Connecting zooarchaeological specimens to the biodiversity and archaeology data networks article

Darwin Core overview from Darwin Core: An Evolving Community-Developed Biodiversity Data Standard article

Need memory, Intel’s Optane DC PM to the rescue

I attended Intel’s DataCentric Innovation Conference Tech Field Day eXclusive (TFDx) last April. There were a couple of items Intel presented at the show that peaked my interest there, one of which was DL Boost (see my Intel’s new DL Boost for AI inferencing blog post) and the other was Optane DC PM (data center persistent memory). This post is about Optane DC PM.

As you already know, Optane SSDs have been on the market now for at least a year or so and have not gained much market traction as of yet. I and others attribute this to the high price differential between Optane SSDs and NVMe Flash SSDs but others may say it’s a matter of production yields – probably a little of both.

But Optane, as announced, always had another form factor (if that’s the right term), as persistent memory that could dramatically increase the size of server memory to support new memory intensive applications at a lower price than DRAM.

I was at Nutanix .NEXT conference last week and saw a 4 socket server from DELL that had 6TB of DRAM in it (and 4-44 core CPUs). I didn’t ask the price but when I mentioned I wanted one for my home office, they said it could easily heat my house. So the other problem with a lot of DRAM is power consumption.

Optane DC PM (data center persistent) memory is intended to solve both the high cost and high power consumption problems of DRAM.

How does it work in a server

The newer Intel motherboards support up to 12 slots of memory per socket. And up to 6 of these can be Optane DC PM (512GB DIMM) or 3TB per socket. Optane DC PM is accessed via L1-L2 caching just like any other memory. Apparently with a dual socket system you can have up to 11 Optane DC PM DIMMs on the motherboard.

L1-L2 cache access times are on the order of picoseconds (10**[-12] seconds), DRAM is on the order of nanoseconds (10**[-9] seconds) and flash is on the order of 100 microseconds (100*10**[-6] seconds). So there’s a vast access time gulf between DRAM and Flash that could be exploited with the right technology – enter Optane DC PM.

The only detailed info I could find on Optane DC PM access times was in a research paper (see Basic performance of Intel Optane DC PMM research paper) and it said Optane DC PM assessing times are ~350 nanoseconds, or close to right between DRAM and Flash. At the show the development team indicated that Optane DC PM support about 3GB/sec of bandwidth per module (DIMM).

There are two ways to use Optane DC PM:

  1. Memory mode – in Memory mode, the data in Optane DC PM is thrown away during a power cycle. You must use a block of DRAM as a cache or rather a virtual memory block to the Optane DC PM acting as a paging store. Data is brought into the DRAM cache when accessed using its (virtual) DRAM address and when no longer used. it gets evicted (destaged) back out to Optane DC PM. When power is cycled the data in Optane DC PM is cleared out. Optane DC PM supports AES XTS-256 bit encryption and can easily be cleared by throwing away encryption keys during a power cycle.
  2. App Direct mode – in App Direct mode, Optane DC PM is accessed directly using application APIs and its data persists across power cycles. For App Direct mode, Optane DC PM is still AES 256 encrypted but here the encryption key is maintained across power cycles but is locked on power up and you need to use a pass phrase to unlock it. In this mode, applications are responsible for flushing (L1-L2) caches to Optane to retains all data written through L1-L2 to the Optane DC PM. There’s a GitHub Persistent Memory Development Kit (PMDK) library for that supports the App Direct mode API that applications need to use.

Both modes use DDR-T, (transactional DDR4) a new memory bus protocol for Optane DC PM access. In the DDR-T protocol, access to the memory bus is requested by a CPU and is granted by an Optane DC PM DIMM. All Optane DC PM DIMMs on a system can be accessed in parrallel.

You can use RDMA to replicate (App direct?) Optane DC PM data from one system to another. In order to support Memory and App Direct mode, Optane DC PM required CPU, BIOS and (application) software changes.

Most of the Optane DC PM support and cryptology logic is implemented in hardware. Optane DC PM has an address indirection table (AIT) to support 3D XPoint wear leveling maintained in DRAM but flushed to Optane during power loss. Transfers to 3D XPoint media is in 256 byte cache lines but the memory bus operates in 64 byte cache lines, so there’s a (DRAM) buffer between media and memory bus.

Optane also supports a high availability, or up to two device failure mode. In this scenario, if one Optane DC PM DIMM fails, the system can swap another spare Optane DC PM DIMM into that address space and continue to operate. If a 2nd Optane DC PM fails then the system fails. Not sure what happens to the data on the original Optane DC PM DIMM during a failure. It seems to me data would be lost, but it could depend on its failure mode.

In Memory mode, the expected ratio between DRAM size and Optane DC PM size is should be 32GB DRAM/6TB Optane DC PM. At the TFDx event, the Optane DC PM team had some performance charts showing different DRAM cache miss rates. Intel also announced new CPU monitoring statistics to track application/workloads impacting DRAM/Optane DC PM in Memory mode and to track Optane DC PM health.

Optane DC PM usage modes are established by the BIOS. It’s flexible enough to have the Optane DC PM usage modes be defined on a region by region basis. Not exactly sure what a region is, but it could be an address range spanning MB(?) of Optane DC PM. With both modes in operation on a system, data can be moved from Memory mode Optane to App direct mode Optane or vice versa.

Intel expects that lifetime of an Optane DC PM DIMM to be from 200-370PB of data writes. Optane DC PMs have a 5 year warrantee. Given its bandwidth (3GB/sec), 200PB of data writes should last ~2 years but that’s at 100% duty cycle, writing 3GB of data, every second of every day. So, 5 years should be a reasonable guarantee using a more realistic ~40% duty cycle.

What applications use Optane DC PM

The one of interest to most people seems to be SAP HANA. According to the development team, SAP HANA could use App Direct mode for main database storage and use DRAM for its delta column store. Cassandra could also use Optane in App Direct mode in a similar fashion.

Also something like a REDIS for key-value store could use Optane DC PM to store Values and use DRAM to store Keys.

But any application could take advantage of the extra memory made available with Optane DC PM DIMMs in Memory mode today. Of course any use of Optane DC PM would require the right levels of Intel Xeon CPUs (Cascade Lake), BIOSes and motherboards.

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Interested in learning more, TFDx videos of the event are available on the website noted previously. Also these TFDx bloggers also have posts specifically on Optane DC PM.

The coolest thing since sliced bread – Optane by Matt Leib, (@MBLeib)

Intel’s crossover point: A 3D spork by Stephen Foskett (@SFoskett)

Intel answering SAP HANA’s tough questions by Keith Townsend (@CTOAdvisor)

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For data that never rests, NetApp NDAS

NetApp co-founder, Dave Hitz announced he was becoming a NetApp Founder Emeritus at the Storage Field Day (SFD18) show. He gave a great session about what he and his Hitz foundation’s been doing (for one example see our Archeology meets big data, post). He also discussed at length where he felt the storage world (and NetApp) must do to address the opportunities of the new cloud world. But this post isn’t about Dave, it’s about NetApp Data Availability Service, NDAS.

NetApp NDAS, currently in Beta but GAing (hopefully) later this year, is an AWS marketplace data orchestration solution that manages primary to secondary to S3 movement for ONTAP data. Essentially, NetApp Data Availability Services extends ONTAP data lifecycle management to AWS cloud. But it’s more than just a way to archive ONTAP data.

NDAS orchestrates Snapmirror services across ONTAP systems and AWS. But once your ONTAP data is in S3 it supplies access to that data for authorized AWS applications and services. That way one can use their ONTAP data to provide data analytics, train AI models, and do just about anything you can do with AWS applications today. By using NDAS, customers can extract more value from their ONTAP data.

NDAS is not just copying data to S3 but is also copying ONTAP metadata, catalogues and other information that provides context for that data. By copying ONTAP catalog information, customers and authorized end users can have file level access to ONTAP data residing in S3 objects.

NDAS today, only supports copying data from secondary ONTAP systems to S3. But a future enhancement will expand this to copy primary ONTAP data to S3.

How does NDAS work

NDAS provisions (your) EC2 instances, and middleware to read the data from the secondary systems and copy it to S3 buckets which you provide. NDAS after initial configuration to point to your ONTAP secondary storage systems, will autodiscover all the data available that can be copied to the cloud.

NDAS will start cataloguing your ONTAP data. NDAS EC2 instances support the NDAS copy, view and a Google-like search processes.

NDAS search presents a simplified file system view into your ONTAP data copied to S3. That way customers can identify data that could be used for AI training or data analytics that run in the cloud to access the data.

There’s extensive security to insure that NDAS is properly authorized to access your ONTAP data. Normal S3 security options also apply such as to have the data be encrypted on S3. NDAS data is automatically encrypted in flight.

Moreover, NDAS S3 bucket data can be replicated across AWS regions . Also serverless/lambda funationality are fully supported from or NDAS S3 buckets. .

What can it do with the data

AWS applications can access the data directly through NDAS APIs. Or customers can manually extract data they want to further process using the NDAS GUI to identify and copy data of interests. NDAS essentially creates a small app layer that allows users to view and access the ONTAP data in S3 as a file system.

One can have different NDAS AMIs operating in different regions for faster access or to support GDPR compliance requirements. Alternatively, a customer could have one NDAS AMI accessing all their secondary ONTAP instances.

NDAS is intended to provide a data analyst or IT generalist access to ONTAP data. This way AI training and big data analytics applications which run easily in the cloud, can have access to ONTAP data. In this way, customers can more effectively utilize data that IT has been storing and maintaining, since time began.

One NDAS beta customer is a MLB team. They have over time instrumented their stadiums to generate lot’s of data about pitch speed, rotation, ball location as it crosses the plate, etc.   The problem with all this data is siloed in onprem or IOT systems that generated it. But the customer wants to use the data to improve players, coaches and the viewer experience. And all that needs tools, applications and software that’s just not available to run in the data center. But with NDAS all this data is now available to cloud applications.

NDAS is supported by any ONTAP 9.5 or later (FAS, AFF, Cloud ONTAP, ONTAPselect) secondary storage system. ONTAP 9.5 software contains all the services required to support NDAS. This includes the copy-to-cloud APIs, as well as the NDAS proxy, which supplies the secure interface to NDAS operating in the cloud.

NetApp’s NDAS sessions are pretty informative. Anyone interested in finding out more should checkout the videos available on TechFieldDay website and Dave’s session is also worth a view.

For more information on Dave’s session and NDAS check out:

NetApp, Cloudier than ever by Enrico Signoretti (@ESignoretti)

NetApp and the space in between by Dan Frith (@PenguinPunk)

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IT in space

Read an article last week about all the startup activity that’s taking place in space systems and infrastructure (see: As rocket companies proliferate … new tech emerges leading to a new space race). This is a consequence of cheap(er) launch systems from SpaceX, Blue Origin, Rocket Lab and others.

SpaceBelt, storage in space

One startup that caught my eye was SpaceBelt from Cloud Constellation Corporation, that’s planning to put PB (4X library of congress) of data storage in a constellation of LEO satellites.

The LEO storage pool will be populated by multiple nodes (satellites) with a set of geo-synchronous access points to the LEO storage pool. Customers use ground based secure terminals to talk with geosynchronous access satellites which communicate to the LEO storage nodes to access data.

Their main selling points appear to be data security and availability. The only way to access the data is through secured satellite downlinks/uplinks and then you only get to the geo-synchronous satellites. From there, those satellites access the LEO storage cloud directly. Customers can’t access the storage cloud without going through the geo-synchronous layer first and the secured terminals.

The problem with terrestrial data is that it is prone to security threats as well as natural disasters which take out a data center or a region. But with all your data residing in a space cloud, such concerns shouldn’t be a problem. (However, gaining access to your ground stations is a whole different story.

AWS and Lockheed-Martin supply new ground station service

The other company of interest is not a startup but a link up between Amazon and Lockheed Martin (see: Amazon-Lockheed Martin …) that supplies a new cloud based, satellite ground station as a service offering. The new service will use Lockheed Martin ground stations.

Currently, the service is limited to S-Band and attennas located in Denver, but plans are to expand to X-Band and locations throughout the world. The plan is to have ground stations located close to AWS data centers, so data center customers can have high speed, access to satellite data.

There are other startups in the ground station as a service space, but none with the resources of Amazon-Lockheed. All of this competition is just getting off the ground, but a few have been leasing idle ground station resources to customers. The AWS service already has a few big customers, like DigitalGlobe.

One thing we have learned, is that the appeal of cloud services is as much about the ecosystem that surrounds it, as the service offering itself. So having satellite ground stations as a service is good, but having these services, tied directly into other public cloud computing infrastructure, is much much better. Google, Microsoft, IBM are you listening?

Data centers in space

Why stop at storage? Wouldn’t it be better to support both storage and computation in space. That way access latencies wouldn’t be a concern. When terrestrial disasters occur, it’s not just data at risk. Ditto, for security threats.

Having whole data centers, would represent a whole new stratum of cloud computing. Also, now IT could implement space native applications.

If Microsoft can run a data center under the oceans, I see no reason they couldn’t do so in orbit. Especially when human flight returns to NASA/SpaceX. Just imagine admins and service techs as astronauts.

And yet, security and availability aren’t the only threats one has to deal with. What happens to the space cloud when war breaks out and satellite killers are set loose.

Yes, space infrastructure is not subject to terrestrial disasters or internet based security risks, but there are other problems besides those and war that exist such as solar storms and space debris clouds. .

In the end, it’s important to have multiple, non-overlapping risk profiles for your IT infrastructure. That is each IT deployment, may be subject to one set of risks but those sets are disjoint with another IT deployment option. IT in space, that is subject to solar storms, space debris, and satellite killers is a nice complement to terrestrial cloud data centers, subject to natural disasters, internet security risks, and other earth-based, man made disasters.

On the other hand, a large, solar storm like the 1859 one, could knock every data system on the world or in orbit, out. As for under the sea, it probably depends on how deep it was submerged!!

Photo Credit(s): Screen shots from SpaceBelt youtube video (c) SpaceBelt

Screens shot from AWS Ground Station as a Service sign up page (c) Amazon-Lockheed

Screen shots from Microsoft’s Under the sea news feature (c) Microsoft