Enhanced LIDAR maps out 45km rather than 215m

I’ve used small LIDAR sensors on toy (Arduino based) robots and they operate well within 1m or so. Ultrasonics sensors are another alternative but we found them very susceptible to noise and surface abrasion. With decent LIDAR sensors used in drones and vehicles, they work up to 215m or so.

But research in the lab (ScienceDaily article: Want to catch a photon, start by silencing the sun) has created LIDAR sensors that uses a novel form of analog/optical noise suppression that is capable of using these same LIDAR sensors and using them to map up to 45km of space.

The researchers were able to add a quantum marker to LIDAR beam photon(s) and then filter beam reflections to only honor those reflected photons with the quantum marker. The ScienceDaily article was based on a Nature Communications article, Noise-tolerant single photon sensitive three-dimensional imager.

What’s been changed?

They call the methodology implemented by their device, Quantum Parametric Mode Sorting or QPMS. It’s not intended to compete with software or computational approaches for noise filtering but rather complement those capabilities with a more accurate LIDAR, that can eliminate the vast majority of noise using non-linear optics (see Wikipedia article on Non-linear optics to learn more)..

It turns out the researchers are able to image space with their new augmented LIDAR using a single photon per pixel. They use an FPGA to control the system and programable ODL(optical delay line, delay’s optical signals), with up conversion single photon detector (USPD, that takes one or more photons at one frequency and converts them to another, higher frequency photon) and a silicon avalanche photo diode (SI-APD, which detects a single photon and creates an avalanche [of multiple electrons?] electrical signal from it.

How well does it work?

To measure the resolution capabilities of the circuit they constructed a 50x70mm (~2×2 3/4″) CNC machined aluminums depth resolution calibration device (sort of like an eye chart only for depth perception) see (2c and 2d below) and were able to accurately map the device column topologies.

They were also able to show enhanced perception and noise reduction when obscuring a landscape (Einstein’s head) with an aluminum screen which would be very hard for normal solutions to filter out. The device was able to clearly reconstruct the image even through the aluminum screen.

The result of all this is an all optical fibre noise reduction circuit. I’m sure the FPGA ,SI-APD, USPD, MLL, Transciever, ODL and MEM are electronics or electro-mechanical devices,, but the guts of the enhanced circuit seems all optical.

What does it mean?

What could QPMS mean for optical fibre communications. It’s possible that optical fibres could use significantly less electro-optical amplifiers, if a single photon could travel 45km without noise.

Also LiFi (light fidelity) or open air optical transmission of data could be significantly improved (both in transmission length and noise reduction) using QPMS. And rone could conceivably use LiFi outside of office communications, such as high bandwidth/low-noise, all optical cellular data services for devices. .

And of course boosting LIDAR length, noise reduction and resolution could be a godsend for all LIDAR mapping today. I readi another article (ScienceDaily: Modern technology reveals … secrets of great, white Maya road) about archeologist mapping the (old) Maya road through the jungles of central America using LIDAR equipped planes. I imagine a QPMS equiped LIDAR could map Mayan foot paths.


Google cloud offers SSD storage

Read an article the other day on Google Cloud tests out fast, high I/O SSD drives. I suppose it was only a matter of time before cloud services included SSDs in their I/O mix.

Yet, it doesn’t seem to me to be as simple as adding SSDs to the storage catalog. Enterprise storage vendors have had SSDs arguably since January of 2008 (see my EMC introduced SSDs to DMX dispatch). And although there are certainly a class of applications that can take advantage of SSD low latency/high IOPs, the vast majority of applications don’t seem to require these services.

Storage systems use of SSDs today

That’s why most enterprise storage system vendors support some form of automated storage tiering or flash caching of normal I/O for their high-end storage systems. Together with offering just plain old SSDs as data storage. In this more sophisticated solution customers have the option to assign application data to SSDs only, hybrid SSD-disks, or disk only storage. In this way the customer get’s to decide whether they want some sort of mix or just pure SSD or disk IO to satisfy their application IO requirements.

Storage startups have emerged that take on both the hybrid SSD-disk and all-flash model and add quality of service to the picture. An example of all-flash that supplies QoS version of all-flash storage is SolidFire (learn more about SolidFire in our GreyBeardsOnStorage podcast with Dave Wright).  An example that does the same sort of thing for hybrid storage is Fusion IOcontrol (formerly NexGen) storage.

Storage system QoS

In the case of SolidFire one can limit volume or volume groups with an IOPs max, throughput max, and a Burst max. The burst is sort of a credit that accrues on a time basis if the application doesn’t ask for the maximum IOPs/Througput which they then can consume above their maximums up to the burst max for a limited timeframe.

QoS capabilities are slowly making their way into enterprise storage systems as well but it will take some time for the instrumentation and capabilities to be put in place. But one can see limited QoS in IBM DS8000 priority IO, NetApp Storage QoS, EMC Unisphere QoS manager for VNX & SMC QoS for VMAX, and HDS SVOS QoS via partitioning. Most of these capabilities control access or partition cache, backend and frontend resources for host volumes. As such, they are not nearly as sophisticated or as easy to use as what SolidFire and other start ups are offering, but they are getting there.

Cloud SSD pricing

Back to the cloud offering. According to the GigaOm article, Google SSD volumes can sustain up to 15K IOPs and they are charging a premium price for this storage ($0.325/GB-month). Apparently Amazon AWS offers high IO EC2 storage as well with a maximum of 4K IOPs but charges a premium both for the storage ($0.125/GB month) and on an IOPs basis ($0.10/IOPS-month). GigaOM had a pricing comparison for 500GB and 2000 IOPs indicating that Google SSD storage would cost $163/month and the AWS provisioned SSD storage would cost $263 ($62.50 for storage and $200 for the 2000 IOPs).

The fact that you can drive the Google SSD to it’s limits without incurring any extra cost seems a serious advantage to me and would be very appealing to me to most enterprise customers.

But where’s latency

It seems to me after some IOPs level is attained, most mission critical applications are more interested in low latency IO (for more on why low latency matters seem my IO throughput vs. low latency post…). Many storage systems are capable of maximum of 100,000s of IOPS but most shops don’t run them that hard, ever. But with proper use of SSDs, most enterprise storage is now clocking IO at sub-msec. low latency IO.

However, I have yet to see any Cloud storage pricing or QoS for that matter that was based on latency guarantees.  I think this is a serious omission.

In any event, SSDs in the cloud is a good think now they just need to offer flash caching, automatic storage tiering and sophisticated QoS.  I realize this is partially re-inventing enterprise storage in the cloud but isn’t that what everyone actually wants, at cloud storage pricing of course.