VMworld 2014 projects Marvin, Mystic, and more

IMG_2902[This post was updated after being published to delete NDA material - sorry, RL] Attended VMworld2014 in San Francisco this past week. Lots of news, mostly about vSphere 6 beta functionality and how the new AirWatch acquisition will be rolled into VMware’s End-User Computing framework.

vSphere 6.0 beta

Virtual Volumes (VVOLs) is in beta and extends VMware’s software-defined storage model to external NAS and SAN storage.  VVOLs transforms SAN/NAS  storage into VM-centric devices by making the virtual disk a native representation of the VM at the array level, and enables app-centric, policy-based automation of SAN and NAS based storage services, somewhat similar to the capabilities used in a more limited fashion by Virtual SAN today.

Storage system features have proliferated and differentiated over time and to be able to specify and register any and all of these functional nuances to VMware storage policy based management (SPBM) service is a significant undertaking in and of itself. I guess we will have to wait until it comes out of beta to see more. NetApp had a functioning VVOL storage implementation on the show floor.

Virtual SAN 1.0/5.5 currently has 300+ customers with 30+ ready storage nodes from all major vendors, There are reference architecture documents and system bundles available.

Current enhancements outside of vSphere 6 beta

vRealize Suite extends automation and monitoring support for a broad mix of VMware and non VMware infrastructure and services including OpenStack, Amazon Web Services, Azure, Hyper-V, KVM, NSX, VSAN and vCloud Air (formerly vCloud Hybrid Services), as well as vSphere.

New VMware functionality being released:

  • vCenter Site Recovery Manager (SRM) 5.8 – provides self service DR through vCloud Automation Center (vRealize Automation) integration, with up to 5000 protected VMs per vCenter and up to 2000 VM concurrent recoveries. SRM UI will move to be supported under vSphere’s Web Client.
  • vSphere Data Protection Advanced 5.8 – provides configurable parallel backups (up to 64 streams) to reduce backup duration/shorten backup windows, access and restore backups from anywhere, and provides support for Microsoft Exchange DAGs, and SQL Clusters, as well as Linux LVMs and EXT4 file systems.

VMware NSX 6.1 (in beta) has 150+ customers and provides micro segmentation security levels which essentially supports fine grained security firewall definitions almost at the VM level, there are over 150 NSX customers today.

vCloud Hybrid Cloud Services is being rebranded as vCloud Air, and is currently available globally through data centers in the US, UK, and Japan. vCloud Air is part of the vCloud Air Network, an ecosystem of over 3,800 service providers with presence in 100+ countries that are based on common VMware technology.  VMware also announced a number of new partnerships to support development of mobile applications on vCloud Air.  Some additional functionality for vCloud Air that was announced at VMworld includes:

  • vCloud Air Virtual Private Cloud On Demand beta program supports instant, on demand consumption model for vCloud services based on a pay as you go model.
  • VMware vCloud Air Object Storage based on EMC ViPR is in beta and will be coming out shortly.
  • DevOps/continuous integration as a service, vRealize Air automation as a service, and DB as a service (MySQL/SQL server) will also be coming out soon

End-User Computing: VMware is integrating AirWatch‘s (another acquisition) enterprise mobility management solutions for mobile device management/mobile security/content collaboration (Secure Content Locker) with their current Horizon suite for virtual desktop/laptop support. VMware End User Computing now supports desktop/laptop virtualization, mobile device management and security, and content security and file collaboration. Also VMware’s recent CloudVolumes acquisition supports a light weight desktop/laptop app deployment solution for Horizon environments. AirWatch already has a similar solution for mobile.

OpenStack, Containers and other collaborations

VMware is starting to expand their footprint into other arenas, with new support, collaboration and joint ventures.

A new VMware OpenStack Distribution is in beta now to be available shortly, which supports VMware as underlying infrastructure for OpenStack applications that use  OpenStack APIs. VMware has become a contributor to OpenStack open source. There are other OpenStack distributions that support VMware infrastructure available from HP, Cannonical, Mirantis and one other company I neglected to write down.

VMware has started a joint initiative with Docker and Pivotal to broaden support for Linux containers. Containers are light weight packaging for applications that strip out the OS, hypervisor, frameworks etc and allow an application to be run on mobile, desktops, servers and anything else that runs Linux O/S (for Docker Linux 3.8 kernel level or better). Rumor has it that Google launches over 15M Docker containers a day.

VMware container support expands from Pivotal Warden containers, to now also include Docker containers. VMware is also working with Google and others on the Kubernetes project which supports container POD management (logical groups of containers). In addition Project Fargo is in development which is VMware’s own lightweight packaging solution for VMs. Now customers can run VMs, Docker containers, or Pivotal (Warden) containers on the same VMware infrastructure.

AT&T and VMware have a joint initiative to bring enterprise grade network security, speed and reliablity to vCloud Air customers which essentially allows customers to use AT&T VPNs with vCloud Air. There’s more to this but that’s all I noted.

VMware EVO, the next evolution in hyper-convergence has emerged.

  • EVO RAIL (formerly known as project Marvin) is appliance package from VMware hardware partners that runs vSphere Suite and Virtual SAN and vCenter Log Insight. The hardware supports 4 compute/storage nodes in a 2U tall rack mounted appliance. 4 of these appliances can be connected together into a cluster. Each compute/storage node supports ~100VMs or ~150 virtual desktops. VMware states that the goal is to have an EVO RAIL implementation take at most 15 minutes from power on to running VMs. Current hardware partners include Dell, EMC (formerly named project Mystic), Inspur (China), Net One (Japan), and SuperMicro.
  • EVO RACK is a data center level hardware appliance with vCloud Suite installed and includes Virtual SAN and NSX. The goal is for EVO RACK hardware to support a 2hr window from power on to a private cloud environment/datacenter deployed and running VMs. VMware expects a range of hardware partners to support EVO RACK but none were named. They did specifically mention that EVO RACK is intended to support hardware from the Open Compute Project (OCP). VMware is providing contributions to OCP to facilitate EVO RACK deployment.

~~~~

Sorry about the stream of consciousness approach to this. We got a deep dive on what’s in vSphere 6 but it was all under NDA. So this just represents what was discussed openly in keynotes and other public sessions.

Comments?

 

Posted in Block Storage, Cloud services, Clustered storage, data protection, desktop virtualization, Disk storage, Distributed computing, Mobile computing, Networking, Object storage, Server virtualization, Software Defined Network, Storage, Strategic Inflection Points, Strategy, Systems | Tagged , , , , , , , , , , , , , , , , , , | Leave a comment

Cloud storage growth is hurting NAS & SAN storage vendors

Strange Clouds by michaelroper (cc) (from Flickr)

Strange Clouds by michaelroper (cc) (from Flickr)

My friend Alex Teu (@alexteu), from Oxygen Cloud wrote a post today about how Cloud Storage is Eating the World Alive. Alex reports that all major NAS and SAN storage vendors lost revenue this year over the previous year ranging from a ~3% loss to over a 20% loss (Q1-2014 compared to Q1-2013, from IDC).

Although an interesting development, it’s hard to say that this is the end of enterprise storage as we know it.  I believe there are a number of factors that are impacting  enterprise storage revenues and Cloud storage adoption may be only one of them.

Other trends impacting NAS & SAN storage adoption

One thing that has emerged over the last decade or so is the advance of Flash storage. Some of this is used in storage controllers to speed up IO access and some is used in servers to speed up IO access. But any speedup of IO could potentially reduce the need for high-performing disk drives and could allow customers to use higher capacity/slower disk drives instead. This could definitely reduce the cost of storage systems. A little bit of flash goes  long way to speed up IO access.

The other thing is that disk capacity is trending upward, at exponential rates. Yesterday,s 2TB disk drive is todays 4TB disk drive and we are already seeing 6TB from Seagate, HGST and others. And this is also driving down the cost of NAS and SAN storage.

Nowadays you can configure 1PB of storage with just over 170 drives. Somewhere in there you might want a couple 100TB of Flash to speed up IO access to these slow disks but Flash is also coming down in ($/GB) price (see SanDISK’s recent consumer grade TLC drive at $0.44/GB). Also the move to MLC flash has increased the capacity of flash devices, leading to less SSDs/flash cache cards to store/speed up more data.

Finally, the other trend which seems to have emerged recently is the movement away from enterprise class storage to server storage. One can see this in VMware’s VSAN, HyperConverged systems such as Nutanix and Scale Computing, as well as a general trend in Windows Server applications (SQL Server, Exchange Server, etc.) to make better use of DAS storage. So some customers are moving their data to shared DAS storage today, whereas before this was more difficult to accomplish effectively and because of that they previously purchased networked storage.

What about cloud storage?

Yes, as Alex has noted, the price of cloud storage has declined precipitously over the last year or so. Alex’s cloud storage pricing graph is shows how the entry of Microsoft and Google has seemingly forced Amazon to match their price reductions. But the other thing of note is that they have all come down to about the same basic price of $0.024/GB/Month.

It’s interesting that Amazon delayed their first S3 serious price reductions by about 4 months after Azure and Google Cloud Storage dropped there’s and then within another month after that, they all were at price parity.

What’s cloud storage real growth?

I reported last August that Microsoft Azure and Amazon S3 were respectively storing 8 trillion and over 2 trillion objects (see my Is object storage outpacing structured and unstructured data growth). This year (April 2014) Microsoft mentioned at TechEd that Azure was storing 20 Trillion object and servicing 2 million request per second.

I could find no update to Amazon S3 numbers from last year but the 10x  2.5x growth in Azure’s object count in ~8 months and the roughly doubling of request/second (In my post I didn’t mention last year they were processing 900K requests/second) say something interesting is going on in cloud storage.

I suppose Google’s cloud storage service is too new to report serious results and maybe Amazon wants to keep their growth a secret. But considering Amazon’s recent matching of Azure’s and Google’s pricing, it probably means that their growth wasn’t what they expected.

The other interesting item from the Microsoft discussions on Azure, was that they were already hosting 1M SQL databases in Azure and that 57% of Fortune 500 customers are currently using Azure.

In the “olden days”, before cloud storage, all these SQL databases and Fortune 500 data sets would have more than likely resided on NAS or SAN storage of some kind. And possibly due to the traditional storage’s higher cost and greater complexity, some of this data would never have been spun up in the first place if they had to use traditional storage, but with cloud storage so cheap, rapidly configurable and easy to use all this new data was placed in the cloud.

So I must conclude from Microsofts growth numbers and their implication for the rest of the cloud storage industry that maybe Alex was right, more data is moving to the cloud and this is impacting traditional storage revenues.  With IDC’s (2013) data growth at ~43% per year, it would seem that Microsoft’s cloud storage is growing more rapidly than the worldwide data growth, ~14X faster!

On the other hand, if cloud storage was consuming most of the world’s data growth, it would seem to precipitate the collapse of traditional storage revenues, not just a ~3-20% decline. So maybe the most new cloud storage applications would never have been implemented before if they had to use traditional storage, which means that only some of this new data would ever have been stored on traditional storage in the first place, leading to a relatively smaller decline in revenue.

One question remains: is this a short term impact or more of a long running trend that will play out over the next decade or so? From my perspective, new applications spinning up on non-traditional storage is a long running threat to traditional NAS and SAN storage which will ultimately see traditional storage relegated to a niche. How big this niche will ultimately be and how well it can be defended needs to be the subject for another post?

~~~~

Comments?

Posted in Cloud services, Cloud storage, DAS, Information economy, Object storage, SSD storage, Storage, storage economics, Strategic Inflection Points, Strategy, Systems | Tagged , , , , , , , , | Leave a comment

Another Y2K-like problem, this time Internet routers are the problem

Read an article today in Wired about The Internet has grown too big for its aging infrastructure showing up as a serious problem that’s soon to be more widespread.

This Y2K-like problem is associated with the Border Gateway Protocol  (BGP) routing tables entries which represent IP address prefixes.  Internet routers keep BGP tables in Tertiary Content Addressable Memory (TCAM, sort of like a virtual memory page table only for router addresses) and there are physical limits as to how many BGP entries will fit into any specific Internet router.  Some routers crash when they exceed their TCAM limit and others just ignore the BGP entries that exceed their limits – neither approach seems workable long term.

Apparently we are approaching one of those hard and fast limits, at least for older routers, as the BGP routing tables reach over 512K entries.  As of May 2014, there were in excess of 500,000 BGP prefixes (table entries).

Smoking gun points to …

It appears that this time Verizon was the perpetrator. Yesterday they added 15K BGP entries to the Internet BGP table, kicking some routers over their 512K limit. This was no doubt in anticipation of some growth in Internet addresses on their networks.

The result was that LiquidWeb’s network went down. Supposedly they have an older Cisco 7600 router and the latest addition to BGP entries exceeded its TCAM capacity, crashing their router. Oops!

Verizon quickly withdrew the offending 15K BGP entry addition and things seem back to normal for the moment. But we are once again close to some arbitrary computerized limit. Only this problem won’t happen at midnight December 31st. It won’t take that long to exceed the current BGP entry limits again and next time it might not be that easy to back out.

But it’s almost like there’s no stopping it…

Just guessing here but these types of routers probably have similar limits for BGP entries exceeding 1024K entries, 2048K, 4096K, etc. With the number of internet connected devices growing exponentially, especially with the Internet of Things, I predict similar problems over the coming years. Indeed, we went from ~400K to ~500K BGP entries in just under two years and the rate of growth seems to be accelerating.

It’s really just a matter of time before even todays routers run out of TCAM slots. Y2K-like, only this time there’s no way to stop it from happening again and again in the future.  I suppose it would be better if the routers just ignored the new BGP entries rather than crashing but that would seem to put some segment of Internet routers out of their reach?  There’s got to be a way to intelligently ignore some updates or summarize prefix updates when a router runs out of TCAM entries.

Welcome to the new 512K problem.

~~~~

Comments?

Photo Credit(s): Cisco 7609 @ itb for INHERENT by Affan Basalamah

 

 

 

Posted in Distributed computing, Internet of Things, Internet traffic, Networking, System effectiveness | Tagged , , , , | Leave a comment

IBM’s next generation, TrueNorth neuromorphic chip

Ok, I admit it, besides being a storage nut I also have an enduring interest in AI. And as the technology of more sophisticated neuromorphic chips starts to emerge it seems to me to herald a whole new class of AI capabilities coming online. I suppose it’s both a bit frightening as well as exciting which is why it interests me so.

IBM announced a new version of their neuromorphic chip line, called TrueNorth with +5B transistors and the equivalent of ~1M neurons. There were a number of articles on this yesterday but the one I found most interesting was in MIT Technical Review, IBM’s new brainlike chip processes data the way your brain does, (based on a Journal Science article requires login, A million spiking neuron integrated circuit with a scaleable communications network and interface).  We discussed an earlier generation of their SyNAPSE chip in a previous post (see my IBM research introduces SyNAPSE chip post).

How does TrueNorth compare to the previous chip?

The previous generation SyNAPSE chip had a multi-mode approach which used  65K “learning synapses” together with ~256K “programming synapses”. Their current generation, TrueNorth chip has 256M “configurable synapses” and 1M “programmable spiking neurons”.  So the current chip has quadrupled the previous chips “programmable synapses” and multiplied the “configurable synapses” by a factor of a 1000.

Not sure why the configurable synapses went up so high but it could be an aspect of connectivity, something akin to what happens to a “complete graph” which has a direct edge connection to every node in the graph. In a complete graph if you have N nodes then the number of edges is given as [N*(N-1)]/2, which for 1M nodes would be ~500M edges. So it must not be a complete graph, but it’s “close to complete” with 1/2 the number of edges.

Analog vs. Digital?

When last I talked with IBM on their earlier version chip I wondered why they used digital logic to create it rather than analog. They said to be able to better follow along the technology curve of normal chip electronics digital was the way to go.

It seemed to me at the time that if you really  wanted to simulate a brains neural processing then you would want to use an analog approach and this should use much less power. I wrote a couple of posts on the subject, one of which was on MIT’s analog neuromorphic chip (see my MIT builds analog neuromorphic chip post) and the other was on why analog made more sense than digital technology for neuromorphic computation (see my Analog neural simulation or Digital neuromorphic computing vs. AI post).

The funny thing is that IBM’s TrueNorth chip uses a lot less power (1000X, milliwatts vs watts) than normal CMOS chips in e use today. Not sure why this would be the case with digital logic but if this is true maybe there’s more of a potential to utilize these sorts of chips in wider applications beyond just traditional AI domains.

How do you program it?

I would really like to get a deeper look at the specs for TrueNorth and its programming model.  But there was a conference last year where IBM presented three technical papers on TrueNorth architecture and programming capabilities (see MIT Technical Report: IBM scientists show blueprints for brain like computing).

Apparently the 1M programming spike neurons are organized into blocks of 256 neurons each (with a prodigious amount of “configurable” synapses as well). These seem equivalent to what I would call a computational unit. One programs these blockss with “corelets” which map out the neural activity that the 256-neuron blocks can perform. Also these corelets “programs” can be linked together or one be subsumed within another sort of like subroutines.  IBM as of last year had a library of 150 corelets which do stuff like detect visual artifacts, motion in a visual image, detect color, etc.

Scale-out neuromorphic chips?

The abstract of the Journal Science paper talked specifically about a communications network interface that allows the TrueNorth chips to be “tiled in two dimensions” to some arbitrary size. So it is apparent that with the TrueNorth design, IBM has somehow extended a within chip block interface that allows corelets to call one another, to go off chip as well. With this capability they have created a scale-out model with the TrueNorth chip.

Unclear why they felt it had to go only two dimensional rather than three but, it seems to mimic the sort of cortex layer connections we have in our brains today. But even with only two dimensional scaling there are all sorts of interesting topologies that are possible.

There doesn’t appear to be any theoretical limit to the number of chips that can be connected in this fashion but I would suppose they would all need to be on a single board or at least “close” together because there’s some sort of time frame that couldn’t be exceeded for propagation delay, i.e., the time it takes for a spike to transverse from one chip to the farthest chip in the chain couldn’t exceed say 10msec. or so.

So how close are we to brain level computations?

In one of my previous post I reported Wikipedia stating that  a typical brain has 86B neurons with between 100M and 500M synapses. I was able to find the 86B number reference today but couldn’t find the 100M to 500M synapses quote again.  However, if these numbers are close to the truth, the ratio between human neurons and synapses is much less in a human brain than in the TrueNorth chip. And TrueNorth would need about 86,000 chips connected together to match the neuronal computation of a human brain.

I suppose the excess synapses in the TrueNorth chip is due to the fact that electronic connection have to be fixed in place for a neuron to neuron connection to exist. Whereas in the brain, we can always grow synapse connections as needed. Also, I read somewhere (can’t remember where) that a human brain at birth has a lot more synapse connections than an adult brain and that part of the learning process that goes on during early life is to trim excess synapses down to something that is more manageable or at least needed.

So to conclude, we (or at least IBM) seem to be making good strides in coming up with a neuromorphic computational model and physical hardware, but we are still six or seven generations away from a human brain’s capabilities (assuming a 1000 of these chips could be connected together into one “brain”).  If a neuromorphic chip generation takes ~2 years then we should be getting pretty close to human levels of computation by 2028 or so.

The Tech Review article said that the 5B transistors on TrueNorth are more transistors than any other chip that IBM has produced. So they seem to be at current technology capabilities with this chip design (which is probably proof that their selection of digital logic was a wise decision).

Let’s just hope it doesn’t take it 18 years of programming/education to attain college level understanding…

Comments?

Photo Credit(s): New 20x [view of mouse cortex] by Robert Cudmore

Posted in Artificial Intelligence, Cognitive computing, Cognitive science, Distributed computing, IBM SyNAPSE chip, Information economy, MIT analog brain chip, Neuron connection mapping, Processing performance, Strategic Inflection Points, Strategy, Systems | Tagged , , , , | Leave a comment

Extremely low power transistors open up new IoT applications

We have written before about the computational power efficiency law know as Koomley’s Law which states that the computations one can do with the same amount of energy has been doubling every 1.57 years (for more info, please see my No power sensors surface … post).

The dawn of sub-threshold electronics

But just this week there was another article this time about electronics that use much less power than normal transistors. Achieving this in Internet of Thing (IoT) type sensors would take the computations/joule up by a orders of magnitude, not just ~1.6X as in Koomley’s law, although how long it will take to come out commercially is another issue

This new technology is called sub-threshold transistors and they use much less power than normal transistors. The article in MIT Technical Review, A batteryless sensor chip for the IoT, discusses the phenomenon used by sub-threshold transistors that normal transistors, even when they are technically in the “off” state, leak some amount of current.  This CMOS transistor parasitic leakage had been considered a current drain that couldn’t be eliminated and as such, wasted energy up until recently.

Not so any longer, with the new sub-threshold transistor design paradigm, electronics  could now take advantage of this “leakage” current to perform actual computations. And that opens up a whole new level of IoT sensors that could be deployed.

Prototype sub-threshold circuits coming out

One company PsiKick is using this phenomenon to design ASIC/chips that, depending on the application, using sub-threshold transistors plus extensive power reduction design techniques, only use 0.1 to 1% of the energy of similar functioning chips. Their first prototype was a portable EKG that uses body heat to power itself with a thermo-electric generator rather than a battery.  The prototype was just a proof of concept but they seem to be at work trying to open the technology to broader applications.

One serious consideration limiting the types of sensors that could be deployed in IoT applications was how to get power to these sensors. The other thing was how to get information out of the sensor and out to the real world.  There are a few ways to attack the power issue for IoT sensors, creating more efficient electronics, more effective/long lasting batteries, and smaller electronic generators. Sub-threshold transistor electronics takes a major leap forward to more efficient electronics.

In my previous post we discussed ways to construct smaller electronic generators used by low-power systems/chips. One approach highlighted in that paper used small antennas to extract power from ambient radio waves. But that’s not the only way to generate small amounts of power. I have also heard of piezoelectric generators that use force and movement (such as foot falls) to generate energy. And of course, small solar panels could do the same trick.

Any of these micro energy generators could be made to work, and together with the ability to design circuits that use 0.1 to 1% of the electricity used by normal circuits, this  should just about eliminate any computational/power limits to the sorts of IoT sensors that could be deployed.

What about non-sensor/non-IoT electronics?

Not sure if this works for IoT sensors why it couldn’t be used for something more substantial like mobile/smart phones, desktop computers, enterprise servers, etc. To that end, it seems that ARM Holdings and IMEC are also looking at the technology.

Only a couple of years ago, everybody was up in arms about all the energy consumption of server farms, especially on the west coast of the USA. But with this sort of sub-threshold transistor electronics coming online, maybe servers could run on ambient radio wave energy, data centers could run desktop computers and led lighting off of thermo-electric generators inside their heat exchangers, and iPhones could run off of accelerometer piezoelectric generators using the motion a phone undergoes while sitting in a pocket of a moving person.

Almost gives the impression of perpetual motion machines but rather than motion we are talking electronics, sort of like perpetual electronics…

So can a no-battery iPhone be in our future, I wouldn’t bet against it. Remember, the compute engine inside all iPhones is based on ARM technology.

Comments?

Photo credit(s): Intel Free Press: Joshua R. Smith holding a sensor 

Posted in Distributed computing, Energy efficiency, Information economy, Internet of Things, Mobile computing, Strategic Inflection Points, Strategy | Tagged , , , | Leave a comment

Replacing the Internet?

safe 'n green by Robert S. Donovan (cc) (from flickr)

safe ‘n green by Robert S. Donovan (cc) (from flickr)

Was reading an article the other day from TechCrunch that said Servers need to die to save the Internet. This article talked about a startup called MaidSafe which is attempting to re-architect/re-implement/replace the Internet into a Peer-2-Peer, mesh network and storage service which they call the SAFE (Secure Access for Everyone) network. By doing so, they hope to eliminate the need for network servers and storage.

Sometime in the past I wrote a blog post about Peer-2-Peer cloud storage (see Free P2P Cloud Storage and Computing if  interested). But it seems MaidSafe has taken this to a more extreme level. By the way the acronym MAID used in their name stands for Massive Array of Internet Disks, sound familiar?

Crypto currency eco-system

The article talks about MaidSafe’s SAFE network ultimately replacing the Internet but at the start it seems more to be a way to deploy secure, P2P cloud storage.  One interesting aspect of the MaidSafe system is that you can dedicate a portion of your Internet connected computers’ storage, computing and bandwidth to the network and get paid for it. Assuming you dedicate more resources than you actually use to the network you will be paid safecoins for this service.

For example, users that wish to participate in the SAFE network’s data storage service run a Vault application and indicate how much internal storage to devote to the service. They will be compensated with safecoins when someone retrieves data from their vault.

Safecoins are a new BitCoin like internet currency. Currently one safecoin is worth about $0.02 but there was a time when BitCoins were worth a similar amount. MaidSafe organization states that there will be a limit to the number of safecoins that can ever be produced (4.3Billion) so there’s obviously a point when they will become more valuable if MaidSafe and their SAFE network becomes successful over time. Also, earned safecoins can be used to pay for other MaidSafe network services as they become available.

Application developers can code their safecoin wallet-ids directly into their apps and have the SAFE network automatically pay them for application/service use.  This should make it much easier for App developers to make money off their creations, as they will no longer have to use advertising support, or provide differenct levels of product such as free-simple user/paid-expert use types of support to make money from Apps.  I suppose in a similar fashion this could apply to information providers on the SAFE network. An information warehouse could charge safecoins for document downloads or online access.

All data objects are encrypted, split and randomly distributed across the SAFE network

The SAFE network encrypts and splits any data up and then randomly distributes these data splits uniformly across their network of nodes. The data is also encrypted in transit across the Internet using rUDPs (reliable UDPs) and SAFE doesn’t use standard DNS services. Makes me wonder how SAFE or Internet network nodes know where rUDP packets need to go next without DNS but I’m no networking expert. Apparently by encrypting rUDPs and not using DNS, SAFE network traffic should not be prone to deep packet inspection nor be easy to filter out (except of course if you block all rUDP traffic).  The fact that all SAFE network traffic is encrypted also makes it much harder for intelligence agencies to eavesdrop on any conversations that occur.

The SAFE network depends on a decentralized PKI to authenticate and supply encryption keys. All SAFE network data is either encrypted by clients or cryptographically signed by the clients and as such, can be cryptographically validated at network endpoints.

The each data chunk is replicated on, at a minimum, 4 different SAFE network nodes which provides resilience in case a network node goes down/offline. Each data object could potentially be split up into 100s to 1000s of data chunks. Also each data object has it’s own encryption key, dependent on the data itself which is never stored with the data chunks. Again this provides even better security but the question becomes where does all this metadata (data object encryption key, chunk locations, PKI keys, node IP locations, etc.) get stored, how is it secured, and how is it protected from loss. If they are playing the game right, all this is just another data object which is encrypted, split and randomly distributed but some entity needs to know how to get to the meta-data root element to find it all in case of a network outage.

Supposedly, MaidSafe can detect within 20msec. if a node is no longer available and reconfigure the whole network. This probably means that each SAFE network node and endpoint is responsible for some network transaction/activity every 10-20msec, such as a SAFE network heartbeat to say it is still alive.

It’s unclear to me whether the encryption key(s) used for rUDPs and the encryption key used for the data object are one and the same, functionally related, or completely independent? And how a “decentralized PKI”  and “self authentication” works is beyond me but they published a paper on it, if interested.

For-profit open source business model

MaidSafe code is completely Open Source (available at MaidSafe GitHub) and their APIs are freely available to anyone and require no API key. They also have multiple approved and pending patents which have been provided free to the world for use, which they use in a defensive capacity.

MaidSafe says it will take a 5% cut of all safecoin transactions over the SAFE network. And as the network grows their revenue should grow commensurately. The money will be used to maintain the core network software and  MaidSafe said that their 5% cut will be shared with developers that help develop/fix the core SAFE network code.

They are hoping to have multiple development groups maintaining the code. They currently have some across Europe and in California in the US. But this is just a start.

They are just now coming out of stealth, have recently received $6M USD investment (by auctioning off MaidSafeCoins a progenitor of safecoins) but have been in operation now, architecting/designing/developing the core code now for 8+ years now, which probably qualifies them for the longest running startup on the planet.

Replacing the Internet

MaidSafe believes that the Internet as currently designed is too dependent on server farms to hold pages and other data. By having a single place where network data is held, it’s inherently less secure than by having data spread out, uniformly/randomly across a multiple nodes. Also the fact that most network traffic is in plain text (un-encrypted) means anyone in the network data path can examine and potentially filter out data packets.

I am not sure how the SAFE network can be used to replace the Internet but then I’m no networking expert. For example, from my perspective, SAFE is dependent on current Internet infrastructure to store and forward rUDPs on along its trunk lines and network end-paths. I don’t see how SAFE can replace this current Internet infrastructure especially with nodes only present at the endpoints of the network.

I suppose as applications and other services start to make use of SAFE network core capabilities, maybe the SAFE network can become more like a mesh network and less dependent on the current hub and spoke current Internet we have today.  As a mesh network, node endpoints can store and forward packets themselves to locally accessed neighbors and only go out on Internet hubs/trunk lines when they have to go beyond the local network link.

Moreover, the SAFE can make any Internet infrastructure less vulnerable to filtering and spying. Also, it’s clear that SAFE applications are no longer executing in data center servers somewhere but rather are actually executing on end-point nodes of the SAFE network. This has a number of advantages, namely:

  • SAFE applications are less susceptible to denial of service attacks because they can execute on many nodes.
  • SAFE applications are inherently more resilient because the operate across multiple nodes all the time.
  • SAFE applications support faster execution because the applications could potentially be executing closer to the user and could potentially have many more instances running throughout the SAFE network.

Still all of this doesn’t replace the Internet hub and spoke architecture we have today but it does replace application server farms, CDNs, cloud storage data centers and probably another half dozen Internet infrastructure/services I don’t know anything about.

Yes, I can see how MaidSafe and its SAFE network can change the Internet as we know and love it today and make it much more secure and resilient.

Not sure how having all SAFE data being encrypted will work with search engines and other web-crawlers but maybe if you want the data searchable, you just cryptographically sign it. This could be both a good and a bad thing for the world.

Nonetheless, you have to give the MaidSafe group a lot of kudos/congrats for taking on securing the Internet and making it much more resilient. They have an active blog and forum that discusses the technology and what’s happening to it and I encourage anyone interested more in the technology to visit their website to learn more

~~~~

Comments?

Posted in Cloud services, Cloud storage, Crowdsourcing, Data availability, Data grid, Data security, Distributed computing, Information economy, Internet traffic, Networking, Object storage, Storage, storage economics, Strategic Inflection Points, Strategy, Systems, Visionary leadershp | Tagged , , , , , | Leave a comment

More women in tech

Read an interesting article today in the NY Times on how Some Universities Crack Code in Drawing Women to Computer Science. The article discusses how Carnegie Mellon University, Harvey Mudd University and the University of Washington have been successful at attracting women to enter their Computer Science (CompSci) programs.

When I was more active in IEEE there was a an affinity group called Women In Engineering (WIE) that worked towards encouraging female students to go into science, technology, engineering and math (STEM).  I also attended a conference for school age girls interested in science and helped to get the word out about IEEE and its activities.  WIE is still active encouraging girls to go into STEM fields.

However, as I visit startups around the Valley and elsewhere I see lots of coders which are male but very few that are female. On the other hand, the marketing and PR groups have almost a disproportionate representation of females although not nearly as skewed as the male to female ratio in engineering (5:6 in marketing/PR to 7:1 in engineering).

Some in the Valley are starting to report on diversity in their ranks and are saying that only 15 to 17% of their employees in technology are females.

On the other hand, bigger companies seem to do a little better than startups by encouraging more diversity in their technical ranks. But the problem is prevalent throughout the technical industry in the USA, at least.

Universities to the rescue

The article goes on to say that some universities have been more successful in recruiting females to CompSci than others and these have a number of attributes in common:

  • They train female teachers at the high school level in how to teach science better.
  • They host camps and activities where they invite girls to learn more about technology.
  • They provide direct mentors to supply additional help to girls in computer science
  • They directly market to females by changing brochures and other material to show women in science.

Some Universities eliminated programming experience as an entry criteria. They also broadened the appeal of the introductory courses in CompSci to show real world applications of doing technology figuring that this would appeal more to females.  Another university re-framed some of their course work to focus on creative problem solving rather than pure coding.

Other universities are not changing their programs at all and finding with better marketing, more mentorship support and early training they can still attract more females to computer science.

The article did mention one other thing that is attracting more females to CompSci and that is the plentiful, high paying jobs that are currently available in the field.

From my perspective, more females in tech is a good thing and we as an industry should do all we can to encourage this.

~~~~

Comments?

Photo credits: Circuit Bending Orchestra: Lara Grant at Diana Eng’s Fairytale Fashion Show, Eyebeam NYC / 20100224.7D.03621.P1.L1.SQ.BW / SML

Posted in Information economy, Strategic Inflection Points, Strategy, Visionary leadershp, Visionary organizations | Tagged , , , , , , | Leave a comment

Vacuum tubes on silicon

Read an interesting article the other day about researchers at NASA having invented a vacuum tube on a chip (see ExtremeTech, Vacuum tube strikes back). Their report was based on an IEEE Spectrum article called Introducing the Vacuum Transistor.

Computers started out early in the last century being mechanical devices (card sorters), moved up to electronic sorters/calculators/computers with vacuum tubes and eventually transitioned to solid state devices with the silicon transistor. Since then the MOS and CMOS transister have pretty much ruled the world of electronic devices.

Vacuum tube?

Vacuum tubes had a number of problems not the least of which was power consumption, size and reliability. It was nothing for a vacuum tube to burn out every couple of times it was powered on and the ENIAC (panel pictured here) had over 17,000 of them, took over 200 sq meters of space, used a lot (150KW) of power and weighed (27 metric) tons.

Of course each vacuum tube was the equivalent of just one transistor and the latest generation Intel Quad Core processors have over 2B transistors in them. So to implement an Intel Quad Core processor with vacuum tubes this might take over 3,000 football fields of space and over 17GW for power/cooling.

There were plenty of niceties with vacuum tubes not the least of which was their nice ruler flat frequency response, ability to support much higher frequencies, significantly less prone to noise and had less problems with radiation than transistors.  This last item meant that vacuum tubes were less susceptible to electromagnetic pulses. Many modern musical/instrument amplifiers are still made today using vacuum tube technology due to their perceived better sound.

But the main problems was their size and power consumption. If you could only shrink a vacuum tube to the size of a MOS field effect transistor (FET) and correspondingly reduce its power consumption, then you would have something.

NASA shrinks the vacuum tube

NASA researchers have shrunk the vacuum tube to nanometer dimensions in a vacuum- channel transistor. They believe it can be fabricated on standard CMOS technology lines and that it can operate at 460GHz. 

This new vacuum-channel transistor marries the benefits of vacuum tubes to the fabrication advantages of MOSFET technology. Making them as small as MOSFET transistors eliminates all of the problems with vacuum tube technology and handily solves a serious problem or two with MOSFETs.

07OLVacuumtransistors-1403115198821

One problem with MOSFET technology today is that we can no longer speed it up any faster than a 4-5GHz.  This limit was reached in 2004 when Intel and others determined that clock speed couldn’t be sped up much more without serious problems resulting and as a result, they started using additional transistors to offer multi-core processor chips.  A lot of time and money is continuing to be spent on seeing how best to offer even more cores but in the end there’s only so much parallelism that can be achieved in most applications and this limits the speed ups that can be attained with multi-core architectures.

But a shrunken vacuum tube doesn’t seem to have the same issues with higher clock speeds.  Also, there is a serious reduction in power consumption that accrues along with reduction in size.

The vacuum in a vacuum tube was there to inhibit electrons from being interfered with by gases. With the vacuum-channel transistor they don’t think they need a vacuum anymore due to the reduction of size and power being used but there’s a little problem on how to creating a helium filled enclosure which they feel will work instead of a vacuum. NASA feels that with todays chip packaging this shouldn’t be a problem.

Also, their current prototypes use 10V but other researchers have reduced other vacuum-channel transistors to use only 1-2v. As of yet the NASA researchers haven’t fabricated their vacuum-channel transistors on a real CMOS line but that’s the next major hurdle.

Imagine a much faster IT

A 400GHz processor in your desktop and maybe a 200GHz processor in your phone/tablet could all be possible with vacuum-channel transistors. They would be so much faster than today’s multi-core systems, that it would be almost impossible to compare the two. Yes there are some apps where multi-core could speed things up considerably but something that’s 10X faster than todays processors would operate much faster than a 10 core CPU. And it still doesn’t mean you couldn’t have multi-core vacuum-channel systems as well.

SSD or NAND flash storage is essentially based on CMOS transistors and the speed of flash is a somewhat of a function of the speed of its transistors.  A 400GHz vacuum-channel transistor could speed up flash storage by an order of magnitude or more. Flash access times are already at the 7µsec level (see my posts on MCS and UltraDIMM storage here and here).  How much of that 7µsec access time is due to the memory channel aand how much is a function of the SanDisk SSD storage is an open question. But whatever portion is on the SSD side could be potentially reduced by a factor of 10 or more with the use of vacuum-channel transistors.

From a disk perspective there are myriad issues that effect how much data can be stored linearly on a disk platter. But one of them is the speed of switching of electromagnetic  (GMR) head and the electronics. Vacuum-channel transistors should be able to eliminate that issue at least in the electronics and maybe with some work in the head as well so disk densities would no longer have to worry about switching speeds. Similar issues apply to magnetic tape densities as well.

Unclear to me how faster switching time would impact network transmission speeds. But it seems apparent that optical transmission times have already reached some sort of limit based on light frequencies used for transmission. However, electronic networking transfer speeds may be able to be enhanced significantly with faster speed switching.

Naturally, WIFI and other forms of radio transmission are seriously impeded by the current frequency and power of electronic switching. That’s one of the reasons why radio stations still depend somewhat on vacuum tubes. However, with vacuum-channel transistors problems with switching speed go away.  Indeed, NASA researchers believe that their vacuum-channel transistors should be able to reach terahertz (1000GHz) transmission switching. Which might make WIFI almost faster than any direct connect networking today.

~~~~
Comments?

Photo Credit(s): ENIAC panel (rear) by Erik Pittit, The Vacuum Tube Transistor from IEEE Spectrum

Posted in Energy efficiency, Mobile computing, Processing performance, Strategy, Systems, Visionary leadershp | Tagged , , , , , | Comments Off