24 ms·

So we started with centralized mainframes, because it was too expensive for everybody to have their own computer. As hardware improves, computers become cheaper

So we started with centralized mainframes, because it was too expensive for everybody to have their own computer. As hardware improves, computers become cheaper and more powerful and then everybody can have their own, and we move to a distributed model. Then as internet speeds improve, it becomes practical to bring all the compute back in to centralized data centres and communicate with them over the internet from small, less powerful devices. Then as the hardware improves again, the small devices become powerful enough to perform their own computation. In the talk, he says that the reason we need to do the compute "on the edge" is because the latency between the cloud is insufficient for real-time devices. So what happens when network speeds improve again (better fibre infrastructure, LiFi, etc.)? Will we bring the compute back in to centralized data centres? Will we continue to bounce back and forwards forever, as network and hardware technology leapfrog each other? Is one model better than the other?

jaredklewis 10y ago
> In the talk, he says that the reason we need to do the compute "on the edge" is because the latency between the cloud is insufficient for real-time devices. So what happens when network speeds improve again (better fibre infrastructure, LiFi, etc.)? Will we bring the compute back in to centralized data centres? Will we continue to bounce back and forwards forever, as network and hardware technology leapfrog each other? Is one model better than the other? Well, for one thing, a lot of the examples such as self-driving cars, drones, and any wearable clearly don't allow for use of fiber. Lifi may have some use cases, but again I don't see how Lifi could help something like a drone or self-driving car. But another point is availability. Wireless connections can drop in and out and our vulnerable to being slowed down by increased demand. Not to mention that the centralized service itself may fail, due to catastrophic power failure, DOS attacks, or any number of other reasons. If that centralized service or choppy wireless connection is providing you with your todo-list or family photo album, its probably not a big deal to have occasional outages. If the system is making decisions for self-driving vehicles, that will be an unmitigated disaster. Even if it is only enough logic to help an unconnected car pull over to the side of the road, a self-driving car needs to be able to operate offline, so one way or another, these cards will need powerful computers inside. And of course, distributed nodes can also fail. A single car's computer may fail, and that's not good. But the AI of every car in an entire area failing simultaneously because power to the local radio tower goes out is going to be way worse. The other thing, is that as we have with processing speeds, we will eventually hit limits in bandwidth. Using bandwidth efficiently will become a larger priority (as scarcity increases, so will the cost) and the centralized model clearly has a drawback in terms of bandwidth usage. So, all in all, I don't think it's just a pendulum that swings back in forth forever, but that the future will be a hybrid, but heavily distributed world out of necessity.
- clusmore 10y ago
  I agree that not all computation would move back to centralized data centres, but then not all computation is done in the cloud now (vs on your mobile phone). 20 years ago, people would have thought it was insane to send data packets over the internet to edit a document, or any number of other tasks now serviced by SaaS products. Of course there are still some tasks that are better done locally, notably real-time or life-critical tasks. And these SaaS services only became feasible when the connection reliability and speed allowed them to. All I'm suggesting is that future improvements to connection reliability and speed will give way to another round of SaaS products, perhaps then able to service real-time needs but still not preferred for life-critical tasks. As much as I hate to say it, the first example that comes to mind is surveillance/tracking. If you think very long-term, like say data-transfer-via-quantum-entanglement, then you could imagine data transfer being insignificant compared to compute time for real-time requirements, so you will naturally offload the compute to the biggest most powerful computer you can get your hands on.
- scardine 10y ago
  > Wireless connections can drop in and out and our vulnerable to being slowed down by increased demand. Not to mention that the centralized service itself may fail, due to catastrophic power failure, DOS attacks, or any number of other reasons. Not to mention how wireless connections themselves are vulnerable, jamming radio frequencies is easy and cheap.
jasoncchild 10y ago
I'm an advocate of edge computing for sensor data aggregation. I don't think round trip computation will be viable for soft real time applications for quite some time, unless that computation requires operating over large datasets (where it's prohibitive to store all it locally). Just my opinion (and some experience), hardly an authority.
rwmj 10y ago
Real Time cloud VMs is a thing -- soft RT using Linux, running on OpenStack. There was a talk about it at the KVM Forum (http://www.linux-kvm.org/images/0/0d/01x03-Jan_Kiszska-KVM_RT_for_masses.pdf http://www.linux-kvm.org/images/0/0d/01x03-Jan_Kiszska-KVM_R...) although I'm not sure if anyone is really using it at the moment.
TuringTest 10y ago
> Will we continue to bounce back and forwards forever, as network and hardware technology leapfrog each other? Is one model better than the other? It actually is task-dependent. There will always be tasks that work best at a centralized mainframe (e.g. weather forecast requires a supercomputer), while others will benefit from distributed local processing (moving the pointer on the screen following mouse input). For the tasks in between, the changing ratios between processing and transmission speed in the distributed points will influence where it's most practical to do the computing, which is the effect you have seen.
retube 10y ago
A key advantage if the cloud is that you don't need to install anything
rm_-rf_slash 10y ago
There is no better. Assuming dirt-cheap photonic connection speeds and handheld supercomputers, there will be tradeoffs regardless of the hardware limitations. Ex: you could watch anything on YouTube or Netflix but someone will always be knowing when you start, pause, stop, and possibly the volume you set. Or you could download all of YouTube onto your God-phone and watch to your heart's content without realtime surveillance. As hardware improves on both ends, the decision between centralized and decentralized will rely less and less on the constraints of the hardware, and more on things like consumer preferences.
krschultz 10y ago
Bandwidth & latency are different things. It doesn't matter how fat you make the pipe, the speed of light prevents certain types of computations from being run remotely. This doesn't matter so much for AI in apps, but certainly for self driving cars all critical computation needs to be on the edge. Once you have every car carrying multiple GPUs, the distributed computing power in cars probably is pretty comparable to a data center. You can run a lot of things in the idle time while the car is parked if you need to do training or things of that nature.

jaredklewis 10y ago

> In the talk, he says that the reason we need to do the compute "on the edge" is because the latency between the cloud is insufficient for real-time devices. So what happens when network speeds improve again (better fibre infrastructure, LiFi, etc.)? Will we bring the compute back in to centralized data centres? Will we continue to bounce back and forwards forever, as network and hardware technology leapfrog each other? Is one model better than the other? Well, for one thing, a lot of the examples such as self-driving cars, drones, and any wearable clearly don't allow for use of fiber. Lifi may have some use cases, but again I don't see how Lifi could help something like a drone or self-driving car. But another point is availability. Wireless connections can drop in and out and our vulnerable to being slowed down by increased demand. Not to mention that the centralized service itself may fail, due to catastrophic power failure, DOS attacks, or any number of other reasons. If that centralized service or choppy wireless connection is providing you with your todo-list or family photo album, its probably not a big deal to have occasional outages. If the system is making decisions for self-driving vehicles, that will be an unmitigated disaster. Even if it is only enough logic to help an unconnected car pull over to the side of the road, a self-driving car needs to be able to operate offline, so one way or another, these cards will need powerful computers inside. And of course, distributed nodes can also fail. A single car's computer may fail, and that's not good. But the AI of every car in an entire area failing simultaneously because power to the local radio tower goes out is going to be way worse. The other thing, is that as we have with processing speeds, we will eventually hit limits in bandwidth. Using bandwidth efficiently will become a larger priority (as scarcity increases, so will the cost) and the centralized model clearly has a drawback in terms of bandwidth usage. So, all in all, I don't think it's just a pendulum that swings back in forth forever, but that the future will be a hybrid, but heavily distributed world out of necessity.

clusmore 10y ago
I agree that not all computation would move back to centralized data centres, but then not all computation is done in the cloud now (vs on your mobile phone). 20 years ago, people would have thought it was insane to send data packets over the internet to edit a document, or any number of other tasks now serviced by SaaS products. Of course there are still some tasks that are better done locally, notably real-time or life-critical tasks. And these SaaS services only became feasible when the connection reliability and speed allowed them to. All I'm suggesting is that future improvements to connection reliability and speed will give way to another round of SaaS products, perhaps then able to service real-time needs but still not preferred for life-critical tasks. As much as I hate to say it, the first example that comes to mind is surveillance/tracking. If you think very long-term, like say data-transfer-via-quantum-entanglement, then you could imagine data transfer being insignificant compared to compute time for real-time requirements, so you will naturally offload the compute to the biggest most powerful computer you can get your hands on.
scardine 10y ago
> Wireless connections can drop in and out and our vulnerable to being slowed down by increased demand. Not to mention that the centralized service itself may fail, due to catastrophic power failure, DOS attacks, or any number of other reasons. Not to mention how wireless connections themselves are vulnerable, jamming radio frequencies is easy and cheap.

jasoncchild 10y ago

I'm an advocate of edge computing for sensor data aggregation. I don't think round trip computation will be viable for soft real time applications for quite some time, unless that computation requires operating over large datasets (where it's prohibitive to store all it locally). Just my opinion (and some experience), hardly an authority.

rwmj 10y ago

Real Time cloud VMs is a thing -- soft RT using Linux, running on OpenStack. There was a talk about it at the KVM Forum (http://www.linux-kvm.org/images/0/0d/01x03-Jan_Kiszska-KVM_RT_for_masses.pdf http://www.linux-kvm.org/images/0/0d/01x03-Jan_Kiszska-KVM_R...) although I'm not sure if anyone is really using it at the moment.

TuringTest 10y ago

> Will we continue to bounce back and forwards forever, as network and hardware technology leapfrog each other? Is one model better than the other? It actually is task-dependent. There will always be tasks that work best at a centralized mainframe (e.g. weather forecast requires a supercomputer), while others will benefit from distributed local processing (moving the pointer on the screen following mouse input). For the tasks in between, the changing ratios between processing and transmission speed in the distributed points will influence where it's most practical to do the computing, which is the effect you have seen.

retube 10y ago

A key advantage if the cloud is that you don't need to install anything

rm_-rf_slash 10y ago

There is no better. Assuming dirt-cheap photonic connection speeds and handheld supercomputers, there will be tradeoffs regardless of the hardware limitations. Ex: you could watch anything on YouTube or Netflix but someone will always be knowing when you start, pause, stop, and possibly the volume you set. Or you could download all of YouTube onto your God-phone and watch to your heart's content without realtime surveillance. As hardware improves on both ends, the decision between centralized and decentralized will rely less and less on the constraints of the hardware, and more on things like consumer preferences.

krschultz 10y ago

Bandwidth & latency are different things. It doesn't matter how fat you make the pipe, the speed of light prevents certain types of computations from being run remotely. This doesn't matter so much for AI in apps, but certainly for self driving cars all critical computation needs to be on the edge. Once you have every car carrying multiple GPUs, the distributed computing power in cars probably is pretty comparable to a data center. You can run a lot of things in the idle time while the car is parked if you need to do training or things of that nature.