A steampunk Venusian rover

Read an article last week in theEngineer on “Designing a mechanical rover to explore … Venus“, on a group at JPL, led by Jonathon Sauder who are working on a mechanical rover to study Venus.

Venus has a temperature of ~470c, hot enough to melt lead, which will fry most electronics in seconds. Moreover, the Venusian surface is under a lot of pressure, roughly equivalent to a mile under water or ~160X the air pressure at Earth’s surface (from NASA Venus in depth). Extreme conditions for any rover.

Going mobile

Sauder and his team were brainstorming mechanical rovers, that operated similar to Theo Jansen’s StrandBeest which walks using wind energy alone. (Checkout the video of the BEEST walking).

Jansen had told Sauder’s team that his devices work much better on smooth surfaces and that uneven, beach like surfaces presented problems.

So, Sauder’s team started looking at using something with tracks instead of legs/feet, sort of like a World War 1 tank. That could operate upside down as well as rightside up.

Rather than sails (as the StrandBeest), they plan to use multiple vertical axis wind turbines, called Sarvonius rotors, located inside the tank to create energy and store that energy in springs for future use.

Getting data

They’re not planning to ditch electronics all together but need to minimize the rovers reliance on electronics.

There are some electronics that can operate at 450C based on silicon carbide and gallium carbide which have a very low level of integration at this time, just a 100 transistors per chip.  And they could use this to add electronic processing and control to their mechanical rover.

Solar panels can supply electricity to the high temperature electronics and can operate at 450C.

But to get information off the rover and back to the Earth, they plan to use a highly radio reflective spot on the rover and a mechanical shutter mechanism. The mechanism can be closed and opened and together with an orbiting satellite generating radio pulses and recording the rover’s reflectivity or not, send Morse code from rover to satellite. The orbiting satellite could record this information and then transmit it to Earth.

The rover will make use of simple chemical reactions to measure soil, rock and atmospheric chemistry. Soil and rocks suitable for analysis can be scooped up, drilled out and moved to the analysis chamber(s) via mechanical devices. Wind speed and direction can be sensed with simple mechanical devices.

In order to avoid obstacles wihile roving around the planet, they  plan to use a mechanical probe out othe front (and back?) of the rover with control systems attached to this to avoid obstacles. This way the rover can move around more of the planets surface.

Such a mechanical rover with high temperature electronics might also be suitable for other worlds in the solar system, Mercury for sure but moons of the Jovian planets, also have extreme pressure environments.

And such a electrical-mechanical rover also might work great to probe volcano’s on earth, although the temperatures are 700 to 1200C, ~2 to 3X Venus. Maybe such a rover could be used in highly radioactive environments to record information and send this back to personnel outside the environment or even effect some preprogrammed repairs. Ocean vents could also be another potential place where such a rover might work well.

Possible improvements

Mechanical probes would need to be moved vertically and swing horizontally to be effective and would necessarily have to poke outside the tanks envelope to read obstacles ahead.

Sonar could work better. Sounds or clicks could be produced mechanically and their reflections could be also received mechanically (a mic is just a mechanical transducer). At the pressures on Venus, sound should travel far.

Morse code was designed to efficiently send alpha-numerics and not much else. It would seem that another codec could be designed to send scientific information faster. And if one mechanical spot is good, multiple spots would be better assuming the satellite could detect multiple radio reflective spots located in close proximity to one another on the rover.

Radio works but why not use infrared. If there were some way to read an infrared signal from the probe, it could present more information per pass.

For instance, an infrared photo of the rover’s bottom or top, using with a flat surface, could encode information in cold and hot spots located across that surface.

This could work at whatever infrared resolution available from the satellite orbiting overhead and would send much more information per orbital pass.

In fact, such an infrared surface readout might allow the rover to send B&W pictures up to the satellite. Sonar could provide a mechanism to record a (sound) picture of the environment being scanned. The infrared information could be encoded across the surface via pipes of cool and hot liquids, sort of like core memory of old.

What about steam power. With 450C there ought to be more than enough heat to boil some liquid and have it cool via expansion. Having cool liquid could be used to cool electronics, chemical and solar devices.  And as the high temperatures on Venus seem constant, steam power and liquid cooling would be available all the time and eliminating any need for springs to hold energy.

And the cooling liquid from steam engines could be used to support an infrared signaling mechanism.

Still not sure why we need any electronics. A suitably configured, shrunken, analytical engine could provide the rudimentary information processing necessary to work the shutter or other transmitter mechanisms, initiate, readout and store mechanical/chemical/sonar sensors and control the other items on the rover.

And with a suitably complex analytical engine there might be some way to mechanically program it with various modes using something like punched tape or cards. Such a device could be used to hold and load information for separate programs in minimal space and could also be used to store information for later transmission, supplying a 100% mechanical storage device.

Going 100% mechanical could also lead to a potentially longer lived rover than something using some electronics and mostly mechanical devices on a planet like Venus. Mechanical devices can fail, but their failure modes are normally less catastrophic, well understood. Perhaps with sufficient mechanical redundancy and concern for tribology, such a 100% mechanical rover could last an awful long time, without any maintenance, e.g., like swiss watches.


Photo Credit(s): World War One tank – mark 1 by Photos of the Past

Vintage Philmor morse code practice … by Joe Haupt

Accompanied by an instructor… by vy pham;

Core memory more detail by Kenneth Moore;

Model of the Analytical Engine By Bruno Barral (ByB), CC BY-SA 2.5;

Punched tape by Rositslav Lisovy

Steam locomotives by Jim Phillips

Data hypervisor

(c) 2012 Silverton Consulting, Inc. All rights reserved

With all this talk of software defined networking and server virtualization where does storage virtualization stand.  I blogged about some problems with storage virtualization a week or so ago in my post on Storage Utilization is broke and this post takes it to the next level.  Also I was at a financial analyst conference this week in Vail where I heard Mark Lewis of Tekrocket but formerly of EMC discuss the need for a data hypervisor to provide software defined storage.

I now believe what we really need for true storage virtualization is a renewed focus on data hypervisor functionality.  The data hypervisor would need both a control plane and a data plane in order to function properly.   Ideally the control plane would set up the interface and routing for the data plane hardware and the server and/or backend storage would be none the wiser.

DMs everywhere

I envision a scenario where a customer’s application data is packaged with a data hypervisor which runs on a commodity data switch hardware with data plane and control plane software running on it.  Sort of creating (virtual) data machines or DMs.

All enterprise and nowadays most midrange storage provide most of the functionality of a storage control plane such as defining units of storage, setting up physical to logical storage mapping, incorporating monitoring, and management of the physical storage layer, etc.  So control planes are pervasive in today’s storage but proprietary.

In addition most storage systems have data plane functionality which operates to connect a host IO request to the actual data which resides in backend storage or internal cache.  But again although data planes are everywhere in storage today they are all proprietary to a specific vendor’s storage system.

Data switch needed

But in order to utilize a data hypervisor and create a more general purpose control plane layer, we need a more generic data plane layer that operates on commodity hardware. This is different from today’s SAN storage switches or DCB switches but similar in a some ways.

The functions of the data switch/data plane layer would be to take routing instructions from the control plane layer and direct the server IO request to the proper storage unit using the data plane layer.  Somewhere in this world view, probably at the data plane level it would introduce data protection services like RAID or other erasure coding schemes, point in time copy/clone services and replication services and other advanced storage features needed by enterprise storage today.

Also it would need to provide some automated storage movement across and within tiers of physical storage and it would connect server storage interfaces at the front end to storage interfaces at the backend.  Not unlike SAN or DCB switches but with much more advanced functionality.

Ideally data switch storage interfaces could attach to dedicated JBOD, Flash arrays as well as systems using DAS  storage.  In addition, it would be nice if the data switch could talk to real storage arrays on SAN, IP/SANs or NFS&CIFS/SMB storage systems.

The other thing one would like out of a data switch is support for a universal translator that would map one protocol to another, such as iSCSI to SAS, NFS to FC, or FC to NFS and any other combination, depending on the needs of the server and the storage in the configuration.

Now if the data switch were built on top of commodity x86 hardware and software with the data switch as just a specialized application that would create the underpinnings for a true data hypervisor with a control and data plane that could be independent and use anybody’s storage.

Data hypervisor

Assuming all this were available then we would have true storage virtualization.  With these capabilities, storage could be repurposed on the fly, added to, subtracted from, and in general be a fungible commodity not unlike server processing MIPs under VMware or Hyper-V.

Application data would then needed to be packaged into a data machine which would offer all the host services required to support host data access.  The data hypervisor would handle the linkages required to interface with the control and data layers.

Applications could be configured to utilize available storage at ease and storage could grow,  shrink or move to accommodate the required workload just as easily as VMs can be deployed today.

How we get there

Aside from the VMware, Citrix, Microsoft thrusts towards virtual storage there are plenty of storage virtualization solutions that can control most backend enterprise SAN storage. However, the problem with these solutions is that in general the execute only on a specific vendors hardware and don’t necessarily talk to DAS or JBOD storage.

In addition, not all of the current generation storage virtualization solutions are unified. That is most of these today only talk FC, FCoE or iSCSI and don’t support NFS or CIFS/SMB.

These don’t appear to be insurmountable obstacles and with proper allocation of R&D funding, could all be solved.

However the more problematic is that none of these solutions operate on commodity hardware or commodity software.

The hardware is probably the easiest to deal with. Today many enterprise storage systems are built ontop of x86 processor storage controllers. Albeit sometimes they incorporate specialized packaging for redundancy and high availability.

The harder problem may be commodity software. Although the genesis for a few storage virtualization systems might come from BSD or other “commodity” software operating systems. They have been modified over the years to no longer represent anything that can run on standard off the shelf operating systems.

Then again some storage virtualization systems started out with special home grown hardware and software. As such, converting these over to something more commodity oriented would be a major transition.

But the challenge is how to get there from here and would anyone want to take this on.  The other problem is that the value add that storage vendors supply currently would be somewhat eroded.  Not unlike what happened to proprietary Unix systems with the advent of VMware.

But this will not take place overnight and the company that takes this on and makes a go at it can have a significant software monopoly that would be hard to crack.

Perhaps it will take a startup to do this but I believe the main enterprise storage vendors are best positioned to take this on.


No-power sensors surface due to computational energy efficiency trends

Koomeys_law_graph,_made_by_Koomey (cc) (from wikipedia.org)
Koomeys_law_graph,_made_by_Koomey (cc) (from wikipedia.org)

Read an article The computing trend that will change everything in MIT’s TechReview today  about the trend in energy consumption per unit of computation.

Along with Moore’s law dictating that  transister density doubles every 18 to 24 months, there is Koomey’s law that states that computational power efficiency or computations per watt, will double every 1.57 yrs.

Koomey’s law has made today’s smart phones and tablets possible.  If your current laptop were computing at the power efficiency of 1991 computers their batteries would last ~2.5 seconds.

No-power sensors?!

But this computing efficiency trend is giving rise to no-power sensors/devices, or computational sensors without batteries.  These new sensors gather electrical energy from “ambient radio waves” in the air, and by doing so harvest enough electricity to power computations and as such, don’t need batteries.

Such devices can gather ~50μwatts of power from a TV transmitter just 2.5 miles away.  Most calculators only use ~5μwatts and digital thermoters around 1μwatt, so 50 is enough to do some reasonable amounts of sensing work.

But the exciting part is that as Koomley’s law continues, the amount of work that 50μwatts or even 5μwatts supports doubles again every 1.6 years.  For example, the computational power of today’s laptops will only consume infinitesimal amounts of power in ~two decades time.  Thus, no-power-sensors of 2034 will be very smart indeed.

“Any sufficiently advanced technology is indistinguishable from magic”, Arthur C. Clarke

Data transmission efficiency not keeping up

Nonetheless, the fact that computational efficiency is doubling every 1.6 years doesn’t mean the data transmission efficiency is doing the same.  Which means that for the foreseeable future, data transmission may remain a crucial bottleneck for no-power sensors.

However, computational increases can somewhat compensate for data transmission limitations by more efficient encoding, compression, etc. But there are limits as to what can be accomplished within any data transmission technology.


Thus, for the foreseeable future, although sensors will be able to do lots more computations, what they transmit to the outside world may remain limited.  Giving rise to smart, no-power sensors providing very miniscule data packages.

One term coined to describe such limited external data transmission from no-power computationally intense sensors is nanodata.   Because of their ability to exist outside the power grid, it is very likely that the future sensor cloud or internet-of-things will be primarily comprised of such nanodata devices.

I was at SNW last week and there was some discussion of “little data” or data in corporate databases, in contrast with big data.  But nanodata is something I had never heard of before today.

So now we have big data, little data, and nanodata.  Seems like are missing a few steps here…