Cachengo provides an Edge AI System designed for taking advantage of data outside the data center to deliver improved operations, deeper insights and a more responsive capability. No longer do you need to send data back to the data center for processing, with Cachengo’s Edge AI Systems you have the tools to process data at the source.
Now you have everything you need for Local Computer Vision processing.
Instead of buying commodity hardware and some software to try and create an edge analytics environment we suggest a different approach. Get an edge system that is designed from the ground up to deliver the function and tools necessary to be successful with analytics outside the data center. Our four-layer architecture creates the environment that is designed to be effective and efficient. Our experience in Demanding environments means you don’t need to try to figure it all out. You can focus on the applications while we take care of the infrastructure.
Let’s review the four layers of the Cachengo architecture products: Drives, Boxes, Networking and Management.
Bento Set-Top Box
Holds up to 8 Symbiotes
About the size of a book
Sled can be stand-alone solution for small form-factor deployments
Pizza 1U Rack Form Factor
Holds 32 Symbiotes
Ultra low power: Motherboards ≤10W vs. >65W for traditional solution
Can tolerate component failures without killing off a service
Symbiote was inspired by the run-of-the-mill, industry-standard 2.5″ form-factor hard drive (HDD). We thought the packaging and size was great, if only we could marry it with something new. This epiphany led us to invent Symbiote – an ultra smart drive ideally suited for the edge!
What could be more symbiotic than infusing incredible computing power directly onto a drive? Others have tried to do similar things but have simply missed the mark by combining such capabilities with the drive’s internally accessible micro-controller. The old way of doing things relied on the system’s main CPU to offload pre-programmed functions to these additional resources.
This created a bottleneck for the already over-tasked CPU that was then forced to carry the additional burden of acting as a centralized arbiter of these computing resources. These earlier forms of smarter drives were also highly proprietary and did not embrace any notion of choice. Fast forward to 2020 and the latest crop of SSD companies are still doing the same thing.
Symbiote is something completely new and different. Each drive becomes a stand-alone computational platform and server. There is no notion of a centralized CPU to become a bottleneck, or to act as an arbiter. Each drive is therefore individually addressable over the network, and can act as an object storage device (OSD).
This stands in stark contrast with conventional network storage architecture, which is more than two decades old and still treats the entire multi-drive system as the OSD. This era of new technology requires a substantially new architecture to take advantage of it.
Unlike its predecessors, symbiote allows you to control how much capacity, whether you want to mix and match flash with hard drive technology, and even what type of software you wish to run. Choices! What a novel concept!
Today’s complex analytics require data– and lots of it. If we are talking about machine learning (ML), artificial intelligence (AI), or just some type of augmented reality (AR), the need for fast storage is substantial. Latency and throughput become much more critical in order to deliver the data to where the analytics are processed.
The whole genre of ‘Computational Storage’ was created to address the needs of these new workloads. But analytics processing (also referred to as neural networks) is very different from offload. While other solutions incorporate offload capabilities, they cannot implement complex neural networks. This is because they do not possess graphics processing units (GPUs).
Our Symbiotes feature embedded GPUs and can perform sophisticated neural networking functions locally, right where the data is stored. What this means is you can ingest the data where it will permanently reside at the lowest CAPEX and OPEX possible, perform your analytics, and then merely send the results to wherever your central indexing resides.
This approach is not only cheaper, but is faster, less complex, and also simply smarter. As we continue to usher in 5G and Edge capabilities, we have a responsibility to be ecologically conscious. Processing data at the drive significantly reduces this network backhaul!
A new type of SD-WAN
Most SD-WAN technologies simply focus on connecting or extending networks. At Cachengo, we needed to do much more than that. We needed to provide a way to connect to potentially millions of Symbiotes, which are almost exclusively deployed behind secure firewalls and can be configured as object storage devices (OSDs), or even as application servers.
We wanted to handle all of our communications without a dependency upon Secure Shell (SSH). It is with this idea in mind that we created Cachengo Connect. Cachengo Connect is a messenger and a protocol at the same time, making it a unique product that allows us to seamlessly connect to our devices, no matter where they sit.
We can use Cachengo Connect in combination with Cachengo Portal to quickly deploy applications. Anything we can do via SSH can be done via Cachengo Connect, but in a fully-trackable, secure, and auditable fashion.
The shortest distance between two points is still a straight line
The worst thing you can do for your edge computing latency is to connect everything up to a proxy. We efficiently and securely connect all of our managed devices, regardless of whether they sit behind different firewalls. Proxies are inherently bad for scaling for numerous reasons— the biggest is that they ultimately create bottlenecks.
Imagine wanting to go from point A to point B, but having to go through point Z to do so, only to find that there is major construction going on at point Z. Why go through this experience if you don’t have to? Connecting devices up via VPN tunnels is equivalent to throwing a proxy in the middle of all of your traffic. You can do it, but you don’t have to.
In addition to the bottleneck potential, proxies are inherently bad because they also provide a risk for man in the middle attacks.
With a man in the middle attack on a proxy, any vulnerability can be exploited to create an opportunity for data to be hijacked, manipulated, or otherwise compromised. Furthermore, any connected devices or services can also be attacked through these types of exploits.
Besides the proxy concerns, there is also a concern for exposing endpoints to the public. Ever hear of DoS or DDoS? DoS stands for denial of service. When you expose a service on the internet it becomes extremely vulnerable to attacks.
This is because the service must broadcast itself to whatever is trying to find it and utilize it. People with malicious intentions can discover these exposed endpoints and disrupt such services by overloading them with requests until the underlying resources crash.
With our Secure Routes we can connect many endpoints without the use of VPN tunnels and without exposing resource endpoints. Imagine being in a building with many offices. Now, remove all doors, windows, and even hallways, but you want to go from one office to another. While such a scenario might wreak havoc to someone suffering from claustrophobia, it is actually ideal when it comes to network security. This is how we connect devices efficiently and securely. Of course, you are free to continue to utilize your VPNs if you like... but we wouldn’t recommend it.
Management outside the data center might seem difficult, but the environment demands an easy-to-deploy and manage solution. Cachengo Portal is that management solution. This cloud-based Tool enables easy provisioning of the Symbiotes and addition of new applications from the Cachengo Market which could include object storage from MinIO or containers with Kubernetes.
Our one-touch approach means that large scale deployments are simple and easy- even though the Symbiotes Operate as independent elements, perfect for highly parallel workloads like analytics and Computer Vision.