Now we are talking about the inevitable nexus between computing and storage. We’re going to talk about a new capability of the snuggle age which is the ability to run PC to compute instances on this noble age. So maybe let’s just refresh our memories of what is the snuggle age and then we’ll go into the AC2 pots Ian what’s this knowledge.
Overview of Snowball
We provided an overview of Snowball edge as really a hybrid platform. First and foremost it’s a hardware appliance that is meant to transfer data back to a WSO it’s kind of a continuation of the service evolution that we started in 2015 with regular snowball snowball edge we saw a couple of upgrades. Couple of updates on terms of capacity from 80 terabytes to 100 terabytes. We saw a bump in networking capabilities to include not only 10 gig Ethernet but 25 and 40 gig Ethernet and then more interesting we added compute capability. So in addition to just providing raw storage capacity we added the ability to run during the initial release lambda functions by way of green grass on the device so that was kind of the foray into edge computer if you will. Again customers have the choice of whether they want to use the device just for data transfer or for compute or both. And so that’s from a speeds and feeds perspective and form factor point of view. Learning AWS training in Chennai at Greens Technologys
Many customers deploying Snowball edges as well as regular snowballs and conventional data canters. That just happens to be where they’re extracting a lot of the data for migration to AWS. There are just as many use cases or deployment scenarios where we see edge effectively being deployed in non conventional type of environments where that ruggedness and kind of overall durability is very much a nice to have if not a need to have.
That non data center environment extends to lots of sand and explosives. The part of the different the different computing location, that’s where the data is going to take. Got to take it.
What is edge computing and what are some of the characteristics of true edge computing environments?
Yet the data is actually being generated across all of those locations so that presents some interesting problems slash challenges to solve in terms of how do you acquire that data efficiently as well as cost effectively and then how can you process that data. And that’s kind of a good segue I guess into the snowball edge and easy to launch that we’re exactly so.
What is that easy to support and why.
That we’re seeing in terms of what used cases this solves with LAMDA and green grass on Snowball edge. A lot of customers were certainly interested in trying to figure out where does the whole server lists application footprint come into into the fold. As far as edge computing use cases and there’s been a bit of learning curve there in terms of taking either existing workloads or maybe it’s just a net new workload but kind of the existing skill set has not necessarily been at full parity with the whole survivalist movement which as we know LAMDA name is kind of predicated on this you know server list application Know and so really providing EC2 capability and specifically the S.P. E1 you see two instances that we watch support for on Snowball Edge was kind of an augmentation into what we’re else is providing and the Edge compute realm and so you have perhaps the tried that is perfectly comfortable developing and running applications and a true service environment and then for those that are running more classic workloads be they you know Linux based workloads or Wintel based workloads they now have that flexibility to simply encapsulate that workload in an AMI image just like they would with regular E.S. too. But instead of running it at some pre prescribed ABS region they’re basically running it on snowball edges as an end point.Obviously in AWS proper but also on Snowball edge so you can have a snowball edge that runs EC2 instances and at the same time it can still be providing that need of S3 endpoint for storage and it can also still be providing the ability to invoke lambda functions by having green grass enabled as well.
What its capacity is in terms of Sibiu memory.
Basically the SB E1 nomenclature is something that is very much specific to Snowball. So it’s just you know if you’re looking at the regular list long list of EC2 instance families you won’t find SB 1. It is very much pertinent to snow Snowball edge and basically if you look at the overall CPQ memory footprint for Snowball edge what we’re making available for customer workloads is 16 VCP you add up to 32 gigs of memory. Bearing in mind that outside of that there are some reservations that we hold within a device to run you know back plane processes as well as if you’re running for instance green grass or if you’re running the file interface for NFC access. Each one of those functions consumes a little bit of CPM and memory so just bear that in mind.
As far as the envelope for different SB E1 instances we have six different instances that are supported all the way from SB 1 small which is one VCP you and one gig of memory all the way up to SB E1 for X large which is the full max if you will of total CCU in memory 16 and 32 respectively.
So we have the ability to provision up to 1 terabyte of storage capacity for an actual S.P. One instance volume how you carve that up among st and number of SB 1 instances is entirely your choice but that one terabyte is the cap. So that’s going back to I would add to the fact that I would kind of cover one of the major or minor differences if you will in terms of the snowball edge that’s supporting two instances the unable to go up to one terabyte was a result of some upgrades and storage capacity that’s really useful now.
We start looking at perhaps you know maybe a single snowball edge is not sufficient. And so that’s where clustering multiple snowball edges from five to 10 nodes helps customers go in and solve some of those more robust workloads again that that would go beyond the single limitations of a single SB one instance.
You know I think it’s been hard for us to point our finger at one or two specific use cases and say aha this is the sweet spot. We’re really just seeing a broad array of use cases come to fruition across a number of verticals and industries and so maybe I’ll just kind of highlight a few of those that solidify I guess you know what the art of the possible is here. One is in transportation for automotive so I don’t think you know the notion of data slash computational intensive workloads is you know foreign when you’re looking at some of these advanced driver application systems that are being developed in the spirit of autonomous vehicles. And if you think about how the players in this space are scaling their R and D fleets across many metropolitan regions you kind of start to know the numbers start to add up in terms of okay if I’m collecting five to 10 terabytes per trip and I have this many vehicles you’re talking about terabytes of data very quickly on a monthly basis. In some cases on a weekly basis and so the question is how can you synthesize those data sets from an operational or indeed fleet to not only be able to collect that data in its raw format but be able to actually produce some actionable insights from that data close to where that fleet is right so that incurring that round trip time to send the data all the way back to the cloud New some meld the old processing machine learning deploying processing and then you know bringing the value the resulting value that data back is not something that operators have to incur.
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