1 00:00:04,400 --> 00:00:07,800 Speaker 1: Welcome to Tech Stuff, a production from I Heart Radio. 2 00:00:12,200 --> 00:00:15,000 Speaker 1: Hey there, and welcome to tech Stuff. I'm your host, 3 00:00:15,160 --> 00:00:18,080 Speaker 1: Jonathan Strickland. I'm an executive producer with I Heart Radio, 4 00:00:18,120 --> 00:00:20,919 Speaker 1: and I love all things tech. And over the history 5 00:00:21,079 --> 00:00:24,200 Speaker 1: of this podcast, I have covered a lot of topics 6 00:00:24,239 --> 00:00:27,040 Speaker 1: that were, at least at one time a little more 7 00:00:27,200 --> 00:00:32,280 Speaker 1: than buzzwords to me, cloud computing, machine learning, Internet of things. 8 00:00:32,720 --> 00:00:35,879 Speaker 1: A lot of these topics were either just not widely 9 00:00:36,040 --> 00:00:40,519 Speaker 1: spoken about in mainstream society or they were even just 10 00:00:40,800 --> 00:00:44,239 Speaker 1: emerging within the tech world itself. This is where I 11 00:00:44,280 --> 00:00:46,440 Speaker 1: have to remind all of you that I came into 12 00:00:46,520 --> 00:00:50,640 Speaker 1: tech Stuff as someone who loved technology, but who is not, 13 00:00:51,320 --> 00:00:55,560 Speaker 1: in actuality technologist or an engineer or anything like that. 14 00:00:56,000 --> 00:00:59,080 Speaker 1: And one of the terms that I started running into 15 00:00:59,160 --> 00:01:03,440 Speaker 1: around fourteen or so, so it was after I had 16 00:01:03,440 --> 00:01:07,360 Speaker 1: started this podcast. One of those terms was edge computing, 17 00:01:07,560 --> 00:01:11,319 Speaker 1: or sometimes computing at the edge. And if you listen 18 00:01:11,400 --> 00:01:15,640 Speaker 1: to yesterday's Smart Talks episode with Malcolm Gladwell, you heard 19 00:01:15,680 --> 00:01:18,720 Speaker 1: a little bit about that in there, and now we're 20 00:01:18,720 --> 00:01:22,280 Speaker 1: gonna dive in whole hog. So today I thought we'd 21 00:01:22,280 --> 00:01:26,319 Speaker 1: talked a bit about what edge computing actually means and 22 00:01:26,480 --> 00:01:29,640 Speaker 1: contrast it with other types of computing models, and we'll 23 00:01:29,680 --> 00:01:32,119 Speaker 1: talk about how all of this is meant to work 24 00:01:32,160 --> 00:01:35,480 Speaker 1: together in different ways, and why it's even a thing 25 00:01:35,480 --> 00:01:38,600 Speaker 1: in the first place, and where edge computing is today 26 00:01:38,640 --> 00:01:41,280 Speaker 1: and what we might think of it in the future. 27 00:01:41,880 --> 00:01:44,920 Speaker 1: I thought that a good way to approach edge computing 28 00:01:45,040 --> 00:01:47,840 Speaker 1: is really to start with some basic facts. There are 29 00:01:47,840 --> 00:01:51,880 Speaker 1: certain things that we, as smarty pants human types, can 30 00:01:51,960 --> 00:01:56,360 Speaker 1: do to speed up computer systems, To speed up the 31 00:01:56,360 --> 00:01:59,360 Speaker 1: the moment from where we put input into a system 32 00:01:59,600 --> 00:02:03,160 Speaker 1: to an we get output from that system. We can 33 00:02:03,200 --> 00:02:07,280 Speaker 1: create more efficient and powerful processors. For example, you know, 34 00:02:07,320 --> 00:02:10,760 Speaker 1: we can lean on parallel processing for some types of 35 00:02:10,800 --> 00:02:15,640 Speaker 1: computer problems. Doesn't work for everything. Quantum computing, similarly, will 36 00:02:15,680 --> 00:02:21,359 Speaker 1: speed up certain types of computer problems exponentially. We can 37 00:02:21,440 --> 00:02:25,320 Speaker 1: design better software, and we can try to avoid Worth's law. 38 00:02:25,639 --> 00:02:28,079 Speaker 1: That's what the law that states that software tends to 39 00:02:28,120 --> 00:02:32,600 Speaker 1: get slower at a rate that's faster than improvements in hardware. 40 00:02:33,400 --> 00:02:36,519 Speaker 1: If we do combinations of all these sort of strategies, 41 00:02:36,520 --> 00:02:39,959 Speaker 1: then the machines of tomorrow will in theory run software 42 00:02:40,000 --> 00:02:43,360 Speaker 1: better than the machines of today. Again, assuming we don't 43 00:02:43,400 --> 00:02:47,120 Speaker 1: just make even more bloated software that ends up canceling 44 00:02:47,120 --> 00:02:51,280 Speaker 1: out our sick hardware gains. But there's one thing that 45 00:02:51,360 --> 00:02:55,040 Speaker 1: we just cannot get around, and that is the top 46 00:02:55,120 --> 00:02:59,000 Speaker 1: speed at which information can travel from one point to another. 47 00:02:59,680 --> 00:03:04,360 Speaker 1: Even using a fiber optic cable and a clever optical 48 00:03:04,440 --> 00:03:08,160 Speaker 1: system to deliver information, we are limited by the speed 49 00:03:08,160 --> 00:03:12,080 Speaker 1: of light itself. Now that speed is um let me 50 00:03:12,120 --> 00:03:17,000 Speaker 1: check my notes here says wicked fast. When traveling through 51 00:03:17,000 --> 00:03:22,360 Speaker 1: a vacuum, light zips along at two hundred million, seven 52 00:03:22,400 --> 00:03:28,760 Speaker 1: hundred thousand, four hundred fifty eight meters per second. Nothing 53 00:03:29,400 --> 00:03:33,760 Speaker 1: goes faster, thanks a lot, Einstein. What this means for 54 00:03:33,880 --> 00:03:38,320 Speaker 1: us is that distance matters. Of course, until fairly recently, 55 00:03:38,360 --> 00:03:40,920 Speaker 1: this wasn't the biggest concern for us because the way 56 00:03:40,960 --> 00:03:43,520 Speaker 1: we did computing was pretty much always right up close 57 00:03:43,560 --> 00:03:47,240 Speaker 1: and personal. So let's talk about that for a second, alright. So, 58 00:03:47,360 --> 00:03:51,400 Speaker 1: to begin with, we have computers that we physically access 59 00:03:51,600 --> 00:03:54,720 Speaker 1: in some way in order to carry out a program. 60 00:03:54,760 --> 00:03:58,680 Speaker 1: So in the very early days, people program computers by 61 00:03:58,680 --> 00:04:03,560 Speaker 1: physically plugging in different cables into different sockets and throwing 62 00:04:03,640 --> 00:04:07,080 Speaker 1: switches and I don't know, waiting for lightning to strike 63 00:04:07,160 --> 00:04:12,119 Speaker 1: or something. Okay, ignore that last bit. The process was tedious, 64 00:04:12,320 --> 00:04:15,760 Speaker 1: it was complicated, it was easy to come up. The 65 00:04:15,840 --> 00:04:18,919 Speaker 1: speed of the computer was limited both by its own 66 00:04:19,040 --> 00:04:22,760 Speaker 1: method of processing information as well as the speed at 67 00:04:22,760 --> 00:04:27,719 Speaker 1: which human operators could operate it. But once the computer 68 00:04:27,839 --> 00:04:32,520 Speaker 1: actually finished the calculations, delivering them was pretty fast. I mean, 69 00:04:32,600 --> 00:04:35,600 Speaker 1: sometimes delivering meant printing out a sheet of paper or 70 00:04:35,680 --> 00:04:38,840 Speaker 1: making little lights on a panel light up a specific way, 71 00:04:38,920 --> 00:04:40,599 Speaker 1: and then the humans would have to consult a guide 72 00:04:40,600 --> 00:04:43,039 Speaker 1: to figure out what that all meant. But if the 73 00:04:43,080 --> 00:04:46,440 Speaker 1: computer had had a display, it would be able to 74 00:04:46,480 --> 00:04:49,400 Speaker 1: throw up the answer as soon as it got there 75 00:04:49,440 --> 00:04:53,479 Speaker 1: with zero delay. Flash forward a few decades, and we 76 00:04:53,600 --> 00:04:57,000 Speaker 1: then had computers that had simplified input and output devices. 77 00:04:57,240 --> 00:05:01,120 Speaker 1: You had your keyboards, you had your monitors and what not. Now, 78 00:05:01,200 --> 00:05:05,800 Speaker 1: programming a computer was far less convoluted, though as programming 79 00:05:05,880 --> 00:05:08,599 Speaker 1: language is evolved, it can still be fairly easy for 80 00:05:08,640 --> 00:05:11,480 Speaker 1: a human to gum things up with a careless error 81 00:05:11,600 --> 00:05:15,080 Speaker 1: or a miscalculation or typo. Again, most of the time 82 00:05:15,279 --> 00:05:18,240 Speaker 1: we were talking about people being all right up on 83 00:05:18,520 --> 00:05:22,040 Speaker 1: the computer, and so there was really no delay between 84 00:05:22,040 --> 00:05:26,480 Speaker 1: the machine arriving at the endpoint of a computational process 85 00:05:26,560 --> 00:05:28,640 Speaker 1: and then delivering the result to the person who was 86 00:05:28,720 --> 00:05:33,120 Speaker 1: using the computer. The information wasn't really traveling anywhere, it 87 00:05:33,200 --> 00:05:37,880 Speaker 1: was just there. Then we get ARPA, the Advanced Research 88 00:05:37,960 --> 00:05:42,520 Speaker 1: Projects Agency. It's the predecessor to DARPA. One of the 89 00:05:42,600 --> 00:05:45,279 Speaker 1: projects ARPA tackled was to find a way to network 90 00:05:45,440 --> 00:05:48,799 Speaker 1: different types of computers together. This was a big challenge 91 00:05:48,960 --> 00:05:51,920 Speaker 1: for several reasons, but one of them was that different 92 00:05:51,960 --> 00:05:57,040 Speaker 1: computers ran on very different processes and what we'll call 93 00:05:57,160 --> 00:06:00,320 Speaker 1: them operating systems, but that's really being a bit general us. 94 00:06:00,320 --> 00:06:03,800 Speaker 1: But the point is that the computers from different manufacturers, 95 00:06:03,920 --> 00:06:07,760 Speaker 1: effectively they spoke different languages and they could not directly 96 00:06:07,839 --> 00:06:11,760 Speaker 1: communicate with one another. And in the easy way boiled down, 97 00:06:12,200 --> 00:06:14,600 Speaker 1: the computers spoke math, but it was kind of like 98 00:06:15,200 --> 00:06:18,600 Speaker 1: wildly different dialects of math. So there needed to be 99 00:06:18,800 --> 00:06:21,920 Speaker 1: some sort of common language that all computers would be 100 00:06:21,960 --> 00:06:25,680 Speaker 1: able to convert their native speech into and then translate 101 00:06:25,839 --> 00:06:30,039 Speaker 1: incoming speech back into their native language. Now that's a 102 00:06:30,120 --> 00:06:34,479 Speaker 1: super oversimplified way to say that very smart people built 103 00:06:34,520 --> 00:06:37,560 Speaker 1: out the protocols that would allow for the transfer of 104 00:06:37,600 --> 00:06:41,600 Speaker 1: information across networks we would eventually get stuff like T 105 00:06:41,760 --> 00:06:46,360 Speaker 1: C P I P that would facilitate these connections. Now 106 00:06:46,600 --> 00:06:49,240 Speaker 1: computers could connect into their network and they would be 107 00:06:49,279 --> 00:06:52,840 Speaker 1: able to send information to and receive information from other 108 00:06:52,880 --> 00:06:56,640 Speaker 1: computers on that same network. And as other networks took shape, 109 00:06:56,960 --> 00:07:00,800 Speaker 1: they could interconnect with that first network, and then we 110 00:07:00,880 --> 00:07:04,360 Speaker 1: got the network of networks, or the Internet. So I'm 111 00:07:04,400 --> 00:07:07,520 Speaker 1: giving a really fast rundown of the history of the 112 00:07:07,560 --> 00:07:11,560 Speaker 1: Internet here. This is like from space levels of high 113 00:07:11,680 --> 00:07:14,800 Speaker 1: level view of the history of the Internet. All right, 114 00:07:14,840 --> 00:07:17,520 Speaker 1: now we're gonna skip way ahead. Let's get up to 115 00:07:17,560 --> 00:07:21,720 Speaker 1: the ninety nineties, where the general public became aware that 116 00:07:21,760 --> 00:07:25,560 Speaker 1: there was this thing called the Internet, and the development 117 00:07:25,560 --> 00:07:29,640 Speaker 1: and deployment of the Worldwide Web really helped that along considerably. 118 00:07:30,400 --> 00:07:35,040 Speaker 1: Now we had this information super highway, or if you prefer, 119 00:07:35,200 --> 00:07:38,120 Speaker 1: we had a series of pipes that let information flow 120 00:07:38,200 --> 00:07:43,400 Speaker 1: from one source to another. Using this vast network of machines, 121 00:07:43,760 --> 00:07:45,920 Speaker 1: we could send messages to friends on the other side 122 00:07:45,960 --> 00:07:48,680 Speaker 1: of the planet, or check in on a particular coffee 123 00:07:48,680 --> 00:07:51,640 Speaker 1: pot at the computer lab in the University of Cambridge 124 00:07:51,640 --> 00:07:54,600 Speaker 1: in England, even if we happened to be in Athens, Georgia, 125 00:07:54,640 --> 00:07:57,520 Speaker 1: at the time. This, by the way, was actually a 126 00:07:57,600 --> 00:08:00,960 Speaker 1: thing that once existed. And yes I did use a 127 00:08:01,000 --> 00:08:04,600 Speaker 1: computer in the University of Georgia Computer Lab to check 128 00:08:04,720 --> 00:08:06,920 Speaker 1: and see whether or not there was coffee in a 129 00:08:07,000 --> 00:08:11,200 Speaker 1: coffee pot across the pond. Normally there wasn't because I 130 00:08:11,240 --> 00:08:13,480 Speaker 1: was often in there in the afternoon and it was 131 00:08:13,560 --> 00:08:16,800 Speaker 1: several hours later over in the UK. But you get 132 00:08:16,800 --> 00:08:20,360 Speaker 1: the point now. The way the information actually travels across 133 00:08:20,400 --> 00:08:24,920 Speaker 1: the Internet is pretty fascinating. First, information travels in bunches 134 00:08:24,960 --> 00:08:28,640 Speaker 1: of data called packets, and this was a brilliant move 135 00:08:28,840 --> 00:08:33,520 Speaker 1: early on in the development of networking technology. When computers 136 00:08:33,640 --> 00:08:37,240 Speaker 1: send data across the Internet, they chopped that data up 137 00:08:37,320 --> 00:08:40,880 Speaker 1: into packets, and a packet has two different kinds of 138 00:08:40,960 --> 00:08:45,040 Speaker 1: data in it. There's data that's all about control information. 139 00:08:45,120 --> 00:08:47,239 Speaker 1: So in other words, this is the data that explains 140 00:08:47,679 --> 00:08:50,800 Speaker 1: where the packet came from, where it's going to, how 141 00:08:50,800 --> 00:08:54,240 Speaker 1: many other packets the receiving computers should get in order 142 00:08:54,320 --> 00:08:57,439 Speaker 1: to make up the entire file, and where within that 143 00:08:57,520 --> 00:09:00,240 Speaker 1: sequence this particular packet should go. So you can think 144 00:09:00,240 --> 00:09:02,480 Speaker 1: of that information is sort of like being the information 145 00:09:02,520 --> 00:09:04,600 Speaker 1: on the outside of an envelope that you would send 146 00:09:04,679 --> 00:09:08,400 Speaker 1: through the mail. Then you've got the payload or the 147 00:09:08,520 --> 00:09:11,600 Speaker 1: data that relates to the file itself, the thing you 148 00:09:11,640 --> 00:09:15,079 Speaker 1: are sending. The packets are kind of like puzzle pieces 149 00:09:15,080 --> 00:09:17,680 Speaker 1: that get reassembled on the other side. Actually, think of 150 00:09:17,720 --> 00:09:21,360 Speaker 1: it a lot like the sequence of Mike TV and 151 00:09:21,480 --> 00:09:25,480 Speaker 1: Willie Wonka and the Chocolate Factory. The gene Wilder version 152 00:09:25,720 --> 00:09:28,720 Speaker 1: that is the description they give for how Mike gets 153 00:09:28,720 --> 00:09:31,880 Speaker 1: broken up into millions of tiny pieces and then flies 154 00:09:31,920 --> 00:09:34,480 Speaker 1: through the air and gets reassembled on the other side. 155 00:09:35,080 --> 00:09:39,240 Speaker 1: Is sort of like what happens with data packets sort of. Well, 156 00:09:39,240 --> 00:09:43,360 Speaker 1: those packets, which tend to be pretty small like bytes, 157 00:09:43,600 --> 00:09:47,480 Speaker 1: is common. They can all travel different pathways in order 158 00:09:47,520 --> 00:09:50,360 Speaker 1: to get to their intended destination. If you think of 159 00:09:50,360 --> 00:09:55,040 Speaker 1: the Internet as just a huge, interconnected series of roads 160 00:09:55,080 --> 00:09:57,000 Speaker 1: that allow you to get from point A to point B, 161 00:09:57,120 --> 00:10:01,160 Speaker 1: but you can choose literally thousand of different routes in 162 00:10:01,240 --> 00:10:03,720 Speaker 1: order to do so. That's why, I mean, these packets 163 00:10:03,720 --> 00:10:08,679 Speaker 1: can all travel different routes. This actually helps make information 164 00:10:08,800 --> 00:10:11,920 Speaker 1: transfers more robust. I mean that makes sense, right, because 165 00:10:11,920 --> 00:10:14,840 Speaker 1: if you were to send a really big file that 166 00:10:15,000 --> 00:10:17,040 Speaker 1: could not be broken up, well, first of all, you'd 167 00:10:17,040 --> 00:10:19,120 Speaker 1: have to have a connection that could handle that much 168 00:10:19,200 --> 00:10:22,160 Speaker 1: data being sent all at once, and that connection would 169 00:10:22,160 --> 00:10:25,040 Speaker 1: need the same throughput all the way to the destination, 170 00:10:25,640 --> 00:10:28,120 Speaker 1: or you would have to divide up the information in 171 00:10:28,160 --> 00:10:30,560 Speaker 1: some way. And if you did do that, if you 172 00:10:30,760 --> 00:10:33,160 Speaker 1: broke it up into packets, but all those packets had 173 00:10:33,200 --> 00:10:36,760 Speaker 1: to zip down the exact same pathway, and then something 174 00:10:36,840 --> 00:10:41,520 Speaker 1: happened halfway through the transfer, uh, like maybe a connection broke, 175 00:10:41,720 --> 00:10:45,040 Speaker 1: maybe a server went offline, whatever it might be, the 176 00:10:45,120 --> 00:10:48,360 Speaker 1: incoming file would be incomplete on the receiving end. So 177 00:10:48,440 --> 00:10:54,000 Speaker 1: packet switching helps ensure that communication between two computers across 178 00:10:54,080 --> 00:10:58,360 Speaker 1: different networks can actually happen. But as you can imagine, 179 00:10:58,679 --> 00:11:01,440 Speaker 1: if the users can computer is in one part of 180 00:11:01,440 --> 00:11:06,040 Speaker 1: the world and the destination computer is on the other 181 00:11:06,160 --> 00:11:09,559 Speaker 1: end of a communication channel on the other side of 182 00:11:09,600 --> 00:11:11,720 Speaker 1: the world, then there might be a bit of a 183 00:11:11,760 --> 00:11:15,360 Speaker 1: delay between sending something and getting something back because that's 184 00:11:15,360 --> 00:11:18,600 Speaker 1: a lot of distance to travel, a lot of hops 185 00:11:18,640 --> 00:11:20,480 Speaker 1: to make on the way. We'll talk about hops a 186 00:11:20,480 --> 00:11:24,400 Speaker 1: bit later, and so you can encounter some latency. Now 187 00:11:24,480 --> 00:11:27,120 Speaker 1: let's move ahead a little bit more and we get 188 00:11:27,160 --> 00:11:30,240 Speaker 1: to the early days of cloud computing. The most basic 189 00:11:30,280 --> 00:11:33,840 Speaker 1: definition I've ever heard about cloud computing is that it's computing, 190 00:11:34,240 --> 00:11:37,960 Speaker 1: but it's happening on someone else's computer, which is pretty accurate, 191 00:11:38,240 --> 00:11:40,400 Speaker 1: and there are a lot of subcategories we can talk about, 192 00:11:40,559 --> 00:11:44,720 Speaker 1: like cloud storage that doesn't necessarily require any computing on 193 00:11:44,760 --> 00:11:47,920 Speaker 1: the server side, but it does mean you're saving stuff 194 00:11:48,000 --> 00:11:51,640 Speaker 1: to someone else's machine, or more likely saving stuff to 195 00:11:51,679 --> 00:11:56,480 Speaker 1: someone else's several other machines for the sake of redundancy. 196 00:11:56,760 --> 00:11:59,960 Speaker 1: Cloud computing is really useful because you're no longer worried 197 00:12:00,080 --> 00:12:03,200 Speaker 1: about the device that the end user is relying upon 198 00:12:03,679 --> 00:12:07,160 Speaker 1: to do heavy lifting. One of the really big frustrations 199 00:12:07,200 --> 00:12:11,160 Speaker 1: of owning a computing devices that well, Moore's Law is 200 00:12:11,200 --> 00:12:14,920 Speaker 1: a thing. Basically, we interpret Moore's law to mean that 201 00:12:15,360 --> 00:12:18,800 Speaker 1: every two years or so, the new computer processors that 202 00:12:18,840 --> 00:12:21,520 Speaker 1: are rolling out of clean rooms are about twice as 203 00:12:21,520 --> 00:12:24,280 Speaker 1: powerful as the ones that came out two years earlier, 204 00:12:24,640 --> 00:12:29,680 Speaker 1: so we see processor capabilities effectively doubling every two years. 205 00:12:30,200 --> 00:12:33,240 Speaker 1: It's actually a little bit more nuanced than that interpretation, 206 00:12:33,280 --> 00:12:35,680 Speaker 1: but I've done full episodes about Moore's law in the past, 207 00:12:36,040 --> 00:12:39,440 Speaker 1: so we'll just go with the overly simplified but widely 208 00:12:39,600 --> 00:12:45,040 Speaker 1: accepted definition. This tendency means that computer capabilities are on 209 00:12:45,080 --> 00:12:48,360 Speaker 1: a pretty incredible trajectory. But the flip side of that 210 00:12:48,480 --> 00:12:51,720 Speaker 1: coin is that any computer you buy today has a 211 00:12:51,800 --> 00:12:54,280 Speaker 1: limited shelf life, at least as far as running the 212 00:12:54,320 --> 00:12:57,760 Speaker 1: most current software goes. Even in the early days of 213 00:12:57,800 --> 00:13:00,600 Speaker 1: personal computers, the joke was that by the time you 214 00:13:00,679 --> 00:13:04,679 Speaker 1: got your computer home from the store, it was already obsolete. 215 00:13:05,040 --> 00:13:08,560 Speaker 1: And that joke wasn't that funny because it felt all 216 00:13:08,679 --> 00:13:15,040 Speaker 1: too true. Well, cloud computing offloads the computational lift off 217 00:13:15,080 --> 00:13:17,920 Speaker 1: of the end user device and puts it on a 218 00:13:17,960 --> 00:13:22,440 Speaker 1: server farm somewhere in the world. If the company providing 219 00:13:22,480 --> 00:13:25,679 Speaker 1: the service is a really big one, or it's piggybacking 220 00:13:25,840 --> 00:13:29,439 Speaker 1: off of a really big company like Amazon, then it 221 00:13:29,559 --> 00:13:33,480 Speaker 1: might distribute those servers in different geographic regions. Otherwise it 222 00:13:33,480 --> 00:13:36,959 Speaker 1: could be in a centralized data center somewhere. The server 223 00:13:37,120 --> 00:13:40,240 Speaker 1: farm provides the number crunching, and it means that the 224 00:13:40,360 --> 00:13:43,560 Speaker 1: end users machine doesn't have to be that advanced in 225 00:13:43,640 --> 00:13:46,760 Speaker 1: order to take advantage of some heavy duty computing power. 226 00:13:47,360 --> 00:13:50,920 Speaker 1: This is what enables devices like Chrome books from Google. 227 00:13:51,480 --> 00:13:55,480 Speaker 1: These are very lightweight computers, both in terms of physical weight, 228 00:13:55,840 --> 00:13:59,920 Speaker 1: and their native processing power. That's because Chrome books rely 229 00:14:00,160 --> 00:14:03,320 Speaker 1: heavily on cloud based services, so a lot of the 230 00:14:03,360 --> 00:14:07,559 Speaker 1: actual processing is happening on machines that could be hundreds 231 00:14:07,600 --> 00:14:11,600 Speaker 1: of miles away. The Chrome book itself is kind of 232 00:14:11,640 --> 00:14:16,480 Speaker 1: a conduit for computational services. It's doing some work, but 233 00:14:16,760 --> 00:14:19,760 Speaker 1: not most of it. This is the same strategy that 234 00:14:19,800 --> 00:14:24,320 Speaker 1: powers things like streaming game services like Google Stadia or 235 00:14:24,440 --> 00:14:29,200 Speaker 1: Microsoft's Xbox Game Pass. The game's run on specialized hardware 236 00:14:29,480 --> 00:14:32,400 Speaker 1: that can crank up the settings on demanding games and 237 00:14:32,440 --> 00:14:35,840 Speaker 1: then deliver all of that over a streaming internet connection. 238 00:14:36,240 --> 00:14:38,200 Speaker 1: So as long as you have a good connection to 239 00:14:38,240 --> 00:14:41,480 Speaker 1: the Internet, you can enjoy a gaming experience that would 240 00:14:41,520 --> 00:14:44,200 Speaker 1: otherwise require you to have a souped up gaming rig 241 00:14:44,320 --> 00:14:48,080 Speaker 1: or console. But while this removes some of the burden 242 00:14:48,280 --> 00:14:51,000 Speaker 1: from the end user, who might no longer need to 243 00:14:51,040 --> 00:14:53,520 Speaker 1: go out and buy the best computer to run the 244 00:14:53,600 --> 00:14:57,880 Speaker 1: latest software, because that gets pretty darn expensive, particularly if 245 00:14:57,920 --> 00:15:02,080 Speaker 1: you're interested in gaming. A aiming rig can run thousands 246 00:15:02,200 --> 00:15:04,520 Speaker 1: of dollars if you want something that's state of the art, 247 00:15:05,360 --> 00:15:08,840 Speaker 1: but it does bump up against that fundamental speed limit 248 00:15:08,880 --> 00:15:11,560 Speaker 1: that we mentioned at the beginning of this podcast. When 249 00:15:11,560 --> 00:15:14,520 Speaker 1: we come back, we'll talk about how edge computing fits 250 00:15:14,600 --> 00:15:18,560 Speaker 1: into this strategy. But first let's take a quick break. 251 00:15:26,000 --> 00:15:27,720 Speaker 1: I've got a couple of other things that i need 252 00:15:27,760 --> 00:15:30,120 Speaker 1: to say about network computing, and then I promised we're 253 00:15:30,160 --> 00:15:33,040 Speaker 1: going to get to edge computing. One thing is that 254 00:15:33,120 --> 00:15:36,440 Speaker 1: the Internet has made it possible to have the actual 255 00:15:36,560 --> 00:15:40,240 Speaker 1: Internet of things. Now, back when I first heard this term, 256 00:15:40,320 --> 00:15:43,400 Speaker 1: it didn't seem to be much more than just a buzzword. 257 00:15:43,800 --> 00:15:48,280 Speaker 1: It was a concept that sounded intriguing but hadn't really 258 00:15:48,320 --> 00:15:51,520 Speaker 1: begun to manifest in a way that was visible to 259 00:15:52,000 --> 00:15:55,920 Speaker 1: the general public. But starting around the early twenty tens, 260 00:15:55,960 --> 00:15:59,280 Speaker 1: that began to change. And today there are more than 261 00:15:59,400 --> 00:16:04,320 Speaker 1: thirty billion IoT devices connecting to the Internet, with experts 262 00:16:04,400 --> 00:16:06,800 Speaker 1: estimating that the total number will be somewhere in the 263 00:16:06,800 --> 00:16:09,720 Speaker 1: neighborhood of thirty five billion by the end of this year, 264 00:16:10,040 --> 00:16:12,760 Speaker 1: and it's just going to get bigger from there. And 265 00:16:12,840 --> 00:16:16,120 Speaker 1: these devices are all across the spectrum when it comes 266 00:16:16,160 --> 00:16:20,760 Speaker 1: to purpose and intended user base and what the outcome 267 00:16:20,960 --> 00:16:24,120 Speaker 1: would be. You've got the consumer facing stuff that a 268 00:16:24,160 --> 00:16:27,240 Speaker 1: lot of us have encountered, you know, everything from smart 269 00:16:27,320 --> 00:16:31,560 Speaker 1: thermostats to home security systems to personal medical devices, to 270 00:16:32,280 --> 00:16:35,600 Speaker 1: athletic trackers, all that kind of stuff. Then you've got 271 00:16:35,600 --> 00:16:41,120 Speaker 1: more specialized variations like lab technology for hospitals and research facilities. 272 00:16:41,520 --> 00:16:46,160 Speaker 1: You've got municipal infrastructure components that are turning traffic lights 273 00:16:46,160 --> 00:16:49,840 Speaker 1: into smart intersections and that kind of thing. The variety 274 00:16:49,920 --> 00:16:54,920 Speaker 1: and scope of the Internet of Things is unbelievable, and many, 275 00:16:54,960 --> 00:16:59,119 Speaker 1: if not most, of these devices are pretty light lifters 276 00:16:59,160 --> 00:17:02,720 Speaker 1: when it comes to processing information. For most of them, 277 00:17:02,760 --> 00:17:05,560 Speaker 1: their main job is to gather data in some way, 278 00:17:05,840 --> 00:17:09,720 Speaker 1: whether that's through optics or other sensors, and then they 279 00:17:09,760 --> 00:17:13,160 Speaker 1: send that data up through the Internet, where some other 280 00:17:13,240 --> 00:17:17,399 Speaker 1: system somewhere connected to the network will process the information 281 00:17:17,520 --> 00:17:21,600 Speaker 1: and then presumably will do something with that info. So 282 00:17:21,680 --> 00:17:25,080 Speaker 1: the output might be a readout that's useful to an 283 00:17:25,200 --> 00:17:28,119 Speaker 1: end user, like it might be that you look at 284 00:17:28,160 --> 00:17:30,560 Speaker 1: your phone and you're able to see input based upon 285 00:17:30,600 --> 00:17:32,800 Speaker 1: the Internet of Things that are around you, or it 286 00:17:32,880 --> 00:17:36,600 Speaker 1: might send that information off to some other related system 287 00:17:36,640 --> 00:17:39,359 Speaker 1: so that a result will show up somewhere else, maybe 288 00:17:39,400 --> 00:17:43,320 Speaker 1: in a way that isn't even obvious to us. The 289 00:17:43,359 --> 00:17:46,920 Speaker 1: traffic light example could be a version of that, right, 290 00:17:47,000 --> 00:17:50,720 Speaker 1: You could have a citywide system of connected smart traffic 291 00:17:50,800 --> 00:17:55,000 Speaker 1: lights that could detect changes in traffic patterns and perhaps 292 00:17:55,160 --> 00:17:59,840 Speaker 1: respond proactively in an effort to minimize congestion. For example. 293 00:18:00,440 --> 00:18:04,439 Speaker 1: This is actually a really really complicated problem. It's not 294 00:18:04,560 --> 00:18:07,600 Speaker 1: something that's simple to solve, but it would be impossible 295 00:18:07,640 --> 00:18:10,639 Speaker 1: to solve unless you had a sort of Internet of 296 00:18:10,680 --> 00:18:14,399 Speaker 1: things infrastructure to collect and process all that data. But 297 00:18:14,600 --> 00:18:18,159 Speaker 1: the era of lightweight devices connected to the Internet really 298 00:18:18,200 --> 00:18:21,600 Speaker 1: brings into focus the limitations we face if we rely 299 00:18:21,880 --> 00:18:26,080 Speaker 1: on centralized data centers. The further out you are from 300 00:18:26,119 --> 00:18:29,119 Speaker 1: that data center, the more delay there's going to be 301 00:18:29,520 --> 00:18:32,639 Speaker 1: as the devices in your area are trying to communicate 302 00:18:32,760 --> 00:18:36,760 Speaker 1: back with that distant center. There's just no getting around that, 303 00:18:36,880 --> 00:18:40,440 Speaker 1: because even if we made a perfect conduit between the 304 00:18:40,520 --> 00:18:43,800 Speaker 1: data center and the end device, we'd still be limited 305 00:18:43,800 --> 00:18:46,880 Speaker 1: by the fact that light has a speed limit. And 306 00:18:46,960 --> 00:18:50,080 Speaker 1: here's where edge computing finally really comes into play. Now. 307 00:18:50,119 --> 00:18:52,199 Speaker 1: I should also mention that edge computing is one of 308 00:18:52,200 --> 00:18:55,960 Speaker 1: those things that has a few different interpretations and definitions. 309 00:18:56,440 --> 00:18:59,560 Speaker 1: What it is largely depends upon whom you are talking 310 00:18:59,640 --> 00:19:04,280 Speaker 1: to about it, so you might get slightly different definitions, 311 00:19:04,320 --> 00:19:07,160 Speaker 1: but there are some general features that I think are 312 00:19:07,160 --> 00:19:11,840 Speaker 1: common across all definitions. So if we visualize a typical 313 00:19:11,920 --> 00:19:15,720 Speaker 1: cloud computer network, we might think of a centralized data 314 00:19:15,800 --> 00:19:19,960 Speaker 1: center that connects out to various end points. Edge computing 315 00:19:20,280 --> 00:19:23,080 Speaker 1: is a practice in which a company locates servers at 316 00:19:23,160 --> 00:19:27,280 Speaker 1: the edge of networks. In other words, you are creating 317 00:19:27,320 --> 00:19:30,600 Speaker 1: servers that can do data processing, and you are putting 318 00:19:30,640 --> 00:19:33,520 Speaker 1: them close to where the data is actually being gathered 319 00:19:33,640 --> 00:19:37,960 Speaker 1: or generated as and you're putting it physically close to them. So, 320 00:19:38,119 --> 00:19:41,800 Speaker 1: for example, I live in the city of Atlanta, Georgia. 321 00:19:42,040 --> 00:19:46,520 Speaker 1: Now let's say that Amazon's Web Services, which provides hosting 322 00:19:46,600 --> 00:19:50,640 Speaker 1: services to thousands of different apps and processes. Let's say 323 00:19:50,640 --> 00:19:54,119 Speaker 1: that they were all located in the state of Washington, 324 00:19:54,240 --> 00:19:57,879 Speaker 1: that's where Amazon has its prime headquarters. Well, that's about 325 00:19:57,960 --> 00:20:02,000 Speaker 1: two thousand, sixty five miles away from me, around four 326 00:20:02,040 --> 00:20:06,960 Speaker 1: thousand two kilometers. So if I'm running an application that 327 00:20:07,119 --> 00:20:11,000 Speaker 1: needs super fast response times in other words, I need 328 00:20:11,119 --> 00:20:14,679 Speaker 1: really low latency, then I'm probably going to have a 329 00:20:14,680 --> 00:20:18,280 Speaker 1: bad experience because the data has to travel pretty far, 330 00:20:18,680 --> 00:20:21,960 Speaker 1: and it's not necessarily going in anything like a straight line, 331 00:20:22,040 --> 00:20:25,960 Speaker 1: because that's not how data traveling on the Internet works. 332 00:20:26,560 --> 00:20:30,159 Speaker 1: It's likely having to go through multiple hops, so I 333 00:20:30,200 --> 00:20:33,480 Speaker 1: mentioned hops earlier. A hop is when a data packet 334 00:20:33,560 --> 00:20:37,360 Speaker 1: moves from one network segment to the next network segment, 335 00:20:37,760 --> 00:20:39,800 Speaker 1: So you can think of it as like hopping from 336 00:20:39,840 --> 00:20:41,639 Speaker 1: one router to the next in order to get to 337 00:20:41,680 --> 00:20:45,200 Speaker 1: its final destination. And the more hops that there are 338 00:20:45,280 --> 00:20:48,640 Speaker 1: between me and the server that's running the app I'm 339 00:20:48,680 --> 00:20:52,600 Speaker 1: actually using, the more latency I'm going to experience because 340 00:20:52,640 --> 00:20:55,119 Speaker 1: the data has to travel more hops in order to 341 00:20:55,160 --> 00:20:57,680 Speaker 1: get to the server that's doing all the number crunching 342 00:20:58,080 --> 00:21:00,000 Speaker 1: then has to do the same thing in order to 343 00:21:00,119 --> 00:21:03,240 Speaker 1: return results to me. So I'm going to experience this 344 00:21:03,520 --> 00:21:08,840 Speaker 1: as lag or latency. But if Amazon instead set up 345 00:21:09,000 --> 00:21:13,520 Speaker 1: different regional server farms around the world and located edge 346 00:21:13,520 --> 00:21:17,040 Speaker 1: servers at those spots, the edge servers could take over 347 00:21:17,080 --> 00:21:21,680 Speaker 1: the immediate processing requirements. So let's say Amazon set up 348 00:21:21,760 --> 00:21:25,959 Speaker 1: that kind of data center in Atlanta where I live now. 349 00:21:26,400 --> 00:21:29,400 Speaker 1: Instead of my device whatever it might be connecting back 350 00:21:29,440 --> 00:21:34,200 Speaker 1: to Amazon's home headquarters in Seattle, Washington, it's instead connecting 351 00:21:34,240 --> 00:21:38,760 Speaker 1: to this much closer edge server in Atlanta. The edge 352 00:21:38,760 --> 00:21:42,040 Speaker 1: server can carry out whatever the immediate function is that 353 00:21:42,080 --> 00:21:45,680 Speaker 1: I'm trying to do so. Maybe now the data traveling 354 00:21:45,720 --> 00:21:48,120 Speaker 1: between me and the edge server is only going through 355 00:21:48,359 --> 00:21:51,320 Speaker 1: one or two hops. Not only is it traveling a 356 00:21:51,359 --> 00:21:55,440 Speaker 1: shorter physical distance, it is having to make fewer transitions 357 00:21:55,600 --> 00:21:59,280 Speaker 1: from one machine like a router on the Internet, to 358 00:21:59,480 --> 00:22:01,119 Speaker 1: the next than it would if it were to go 359 00:22:01,200 --> 00:22:03,560 Speaker 1: all the way back to Seattle. And the reduction in 360 00:22:03,640 --> 00:22:07,480 Speaker 1: hops is critical for reducing latency. Now, this doesn't mean 361 00:22:07,520 --> 00:22:13,320 Speaker 1: that edge servers totally replace centralized data centers. Instead, they 362 00:22:13,359 --> 00:22:18,840 Speaker 1: work in concert with those centralized data centers. Edge servers 363 00:22:19,040 --> 00:22:22,080 Speaker 1: kind of act as a point of processing for quick response, 364 00:22:22,720 --> 00:22:26,399 Speaker 1: but you might want to have deeper analytical work being 365 00:22:26,440 --> 00:22:30,920 Speaker 1: done by your centralized server. This work isn't necessarily as 366 00:22:30,960 --> 00:22:33,879 Speaker 1: time sensitive, and in fact, the results of that work 367 00:22:33,960 --> 00:22:37,440 Speaker 1: might not return to the end user like me at all. 368 00:22:37,720 --> 00:22:41,600 Speaker 1: It might be things like trend analysis, where you're looking 369 00:22:41,760 --> 00:22:46,960 Speaker 1: at millions of different transactions over a course of months 370 00:22:46,960 --> 00:22:50,000 Speaker 1: and months and then drawing conclusions from that data. Well, 371 00:22:50,040 --> 00:22:53,880 Speaker 1: that's not as time sensitive. That's literally looking at changes 372 00:22:54,000 --> 00:22:57,240 Speaker 1: over time. So that can be done in a big 373 00:22:57,320 --> 00:23:00,840 Speaker 1: centralized data center. It doesn't need to be offloaded to 374 00:23:01,760 --> 00:23:05,639 Speaker 1: the servers that are geographically close to me. Let's take 375 00:23:05,760 --> 00:23:08,880 Speaker 1: an actual example. Let's say I'm using an augmented reality 376 00:23:08,920 --> 00:23:13,520 Speaker 1: headset that connects wirelessly to hot spots and cellular networks. 377 00:23:13,600 --> 00:23:17,280 Speaker 1: So this is an actual headset that I'm wearing. I've 378 00:23:17,320 --> 00:23:20,399 Speaker 1: got a battery pack for it, and it's communicating with 379 00:23:20,520 --> 00:23:23,720 Speaker 1: the network. I'm walking through Atlanta and I'm looking at 380 00:23:23,800 --> 00:23:28,679 Speaker 1: various features, and the augmented headset is displaying information about 381 00:23:28,760 --> 00:23:31,159 Speaker 1: the stuff I'm looking at, and I can see the 382 00:23:31,240 --> 00:23:34,040 Speaker 1: information within my view. It's overlaid on top of my 383 00:23:34,160 --> 00:23:37,320 Speaker 1: view of the world around me. The headset would likely 384 00:23:37,359 --> 00:23:42,560 Speaker 1: be fitted with stuff like a GPS chip, accelerometers, magnetometer 385 00:23:42,760 --> 00:23:46,600 Speaker 1: for compass directions, camera to identify what it is I'm 386 00:23:46,680 --> 00:23:50,680 Speaker 1: looking at, a processor, and so on. The headset needs 387 00:23:50,680 --> 00:23:54,159 Speaker 1: to relay data to servers to process this information, to 388 00:23:54,320 --> 00:23:58,879 Speaker 1: interpret it, and then to return relevant information to my view. 389 00:23:59,320 --> 00:24:01,800 Speaker 1: The reason you why do this is because by offloading 390 00:24:02,040 --> 00:24:06,639 Speaker 1: those computational requirements to another machine, you don't have to 391 00:24:06,680 --> 00:24:10,159 Speaker 1: make the a R goggles way like fifty pounds and 392 00:24:10,760 --> 00:24:14,800 Speaker 1: have big cooling systems attached and everything, because they don't 393 00:24:14,840 --> 00:24:18,080 Speaker 1: have to do as heavy a lift in the computational department. 394 00:24:18,760 --> 00:24:23,359 Speaker 1: But this has to happen really fast, otherwise I'm going 395 00:24:23,400 --> 00:24:26,399 Speaker 1: to see information about what I had been looking at 396 00:24:26,840 --> 00:24:30,640 Speaker 1: a few moments before while I'm now looking at something else, 397 00:24:30,640 --> 00:24:33,600 Speaker 1: which would be really disorienting. I might look at a 398 00:24:33,640 --> 00:24:36,520 Speaker 1: historic building and I might wonder, oh, I wonder what 399 00:24:36,720 --> 00:24:40,159 Speaker 1: this building used to be, and then I happen to 400 00:24:40,200 --> 00:24:42,280 Speaker 1: look away and I'm looking at a tree or something, 401 00:24:42,320 --> 00:24:45,320 Speaker 1: and then I see that, apparently that tree is actually 402 00:24:45,400 --> 00:24:48,960 Speaker 1: the historic Wren's nest, which was the home of the 403 00:24:49,040 --> 00:24:53,199 Speaker 1: controversial author Joel Chandler Harris. And heck, I might just 404 00:24:53,280 --> 00:24:56,399 Speaker 1: think that my headset detected that there's an actual wren's 405 00:24:56,480 --> 00:24:59,280 Speaker 1: nest in the tree I'm looking at. It would be confusing. 406 00:24:59,720 --> 00:25:03,359 Speaker 1: So applications like streaming video games two players using a 407 00:25:03,359 --> 00:25:07,720 Speaker 1: specialized device, edge computing is absolutely critical. Like a R 408 00:25:07,760 --> 00:25:12,840 Speaker 1: and VR, latency will ruin your experience in gaming, There's 409 00:25:12,880 --> 00:25:14,919 Speaker 1: nothing like playing a game and feeling that bit of 410 00:25:15,000 --> 00:25:17,800 Speaker 1: lag between when you press a button and when something 411 00:25:17,920 --> 00:25:21,159 Speaker 1: actually happens on screen. You don't want there to be 412 00:25:21,200 --> 00:25:24,760 Speaker 1: a perceptible delay between hitting a jump button and having 413 00:25:24,760 --> 00:25:28,119 Speaker 1: your little Italian plumber dude actually jump. You want that 414 00:25:28,160 --> 00:25:32,040 Speaker 1: to feel seamless, and humans are pretty sensitive to delay. 415 00:25:32,280 --> 00:25:34,439 Speaker 1: You would want there to be less than one hundred 416 00:25:34,440 --> 00:25:38,920 Speaker 1: milliseconds or one hundred thousands of a second, but even 417 00:25:39,000 --> 00:25:42,960 Speaker 1: that is a little long. The general consensus is that 418 00:25:43,040 --> 00:25:45,600 Speaker 1: the goal post is to have delays of less than 419 00:25:45,720 --> 00:25:50,040 Speaker 1: twenty milliseconds, and that is fast enough so that our 420 00:25:50,160 --> 00:25:53,800 Speaker 1: mushy brains don't really process any kind of delay. If 421 00:25:53,800 --> 00:25:56,800 Speaker 1: you've ever used a VR application that has even a 422 00:25:56,840 --> 00:26:00,119 Speaker 1: tiny bit of latency in it, you've probably felt a 423 00:26:00,200 --> 00:26:04,600 Speaker 1: sort of unpleasant swimming sensation that can frequently lead to 424 00:26:04,640 --> 00:26:08,000 Speaker 1: a type of motion sickness. It's because you're sensing that 425 00:26:08,080 --> 00:26:11,680 Speaker 1: delay between when you turn your head and when your 426 00:26:11,720 --> 00:26:15,520 Speaker 1: actual point of view within the virtual environment adjusts, and 427 00:26:15,600 --> 00:26:19,760 Speaker 1: your brain says, hey, something is like really wrong here, 428 00:26:20,119 --> 00:26:22,159 Speaker 1: and then it sends a message to your stomach to 429 00:26:22,240 --> 00:26:25,440 Speaker 1: go hog wild because somehow that's going to fix things 430 00:26:26,119 --> 00:26:29,919 Speaker 1: all right. My understanding of biology is admittedly a little 431 00:26:29,960 --> 00:26:33,199 Speaker 1: limited here, but the important bit is that latency is 432 00:26:33,280 --> 00:26:36,080 Speaker 1: bad and we must do our best to eliminate it. 433 00:26:36,720 --> 00:26:40,120 Speaker 1: A r VR and game streaming are all really obvious 434 00:26:40,200 --> 00:26:44,040 Speaker 1: examples of technologies that rely on low latency response time. 435 00:26:44,320 --> 00:26:48,959 Speaker 1: While also typically requiring a high data throughput communication channel. 436 00:26:49,320 --> 00:26:52,840 Speaker 1: So when you hear people talking about applications that require 437 00:26:52,840 --> 00:26:57,680 Speaker 1: stuff like five G connectivity and edge computing, a r VR, 438 00:26:57,840 --> 00:27:01,520 Speaker 1: video games, those are all typically part the conversation. And 439 00:27:01,560 --> 00:27:04,040 Speaker 1: before I go further, I shall also clarify that I 440 00:27:04,080 --> 00:27:09,040 Speaker 1: specifically mean high frequency five G connectivity. If you've listened 441 00:27:09,080 --> 00:27:11,480 Speaker 1: to my episodes about five G, you know that there 442 00:27:11,480 --> 00:27:15,000 Speaker 1: are a few different flavors of that technology, all of 443 00:27:15,000 --> 00:27:17,880 Speaker 1: which relate to the band of radio frequencies that are 444 00:27:17,920 --> 00:27:22,160 Speaker 1: being used. The higher end frequencies within five G can 445 00:27:22,200 --> 00:27:26,120 Speaker 1: carry an incredible amount of information all at once. These 446 00:27:26,240 --> 00:27:29,639 Speaker 1: are bands of frequencies that provide data speeds that rival 447 00:27:29,720 --> 00:27:33,840 Speaker 1: that of dedicated fiber optic lines. But these frequencies also 448 00:27:34,119 --> 00:27:38,040 Speaker 1: don't travel very far and they can't penetrate solid walls 449 00:27:38,200 --> 00:27:40,840 Speaker 1: very well, so you quickly lose out on that high 450 00:27:40,920 --> 00:27:45,040 Speaker 1: volume throughput if you move away from the transmission antenna 451 00:27:45,440 --> 00:27:48,359 Speaker 1: or something comes between you and it. It's why a 452 00:27:48,440 --> 00:27:51,639 Speaker 1: really robust high speed five G network would need a 453 00:27:51,680 --> 00:27:55,280 Speaker 1: lot of towers to make it work. Anyway, the five 454 00:27:55,359 --> 00:27:58,680 Speaker 1: G would be the communication channel, while edge computing would 455 00:27:58,680 --> 00:28:03,600 Speaker 1: provide the actual proces sessing capabilities to return relevant information quickly. 456 00:28:04,080 --> 00:28:06,760 Speaker 1: You can imagine how edge computing would be important for 457 00:28:06,800 --> 00:28:10,159 Speaker 1: all sorts of applications, not just VR, A R and 458 00:28:10,240 --> 00:28:13,480 Speaker 1: video games. The Internet of Things depends pretty heavily on 459 00:28:13,800 --> 00:28:17,520 Speaker 1: edge computing, as the end devices, like I said, tend 460 00:28:17,520 --> 00:28:20,359 Speaker 1: to be pretty simple. A lot of IoT devices boiled 461 00:28:20,400 --> 00:28:24,080 Speaker 1: down to a sensor that detects some sort of dynamic element, 462 00:28:24,720 --> 00:28:28,040 Speaker 1: you know, a thermometer for example, detecting changes in temperature. 463 00:28:28,560 --> 00:28:31,120 Speaker 1: Then it has a means of sending the information off 464 00:28:31,160 --> 00:28:34,000 Speaker 1: to somewhere else, and it's that somewhere else that ends 465 00:28:34,040 --> 00:28:36,919 Speaker 1: up making meaning of the data that the sensors are 466 00:28:36,920 --> 00:28:40,000 Speaker 1: actually gathering. So the end device is at least in 467 00:28:40,000 --> 00:28:43,600 Speaker 1: a way in this particular instance, kind of stupid. It's 468 00:28:43,640 --> 00:28:47,120 Speaker 1: just giving constant updates and a processing center is actually 469 00:28:47,360 --> 00:28:50,840 Speaker 1: making use of that information. However, there are also some 470 00:28:50,920 --> 00:28:54,840 Speaker 1: IoT devices that actually do have some compute capacity built 471 00:28:54,880 --> 00:28:58,520 Speaker 1: into them. There are machines that have processing units kind 472 00:28:58,520 --> 00:29:01,560 Speaker 1: of like CPUs on a computer, and they also have 473 00:29:01,720 --> 00:29:04,520 Speaker 1: memory and the ability to do at least some data 474 00:29:04,560 --> 00:29:08,240 Speaker 1: processing right at the point of data generation or data collection. 475 00:29:08,840 --> 00:29:11,360 Speaker 1: So if you were to go out and purchase a 476 00:29:11,440 --> 00:29:14,880 Speaker 1: brand new car, chances are that car would have somewhere 477 00:29:14,880 --> 00:29:18,560 Speaker 1: in the neighborhood of fifty CPUs built into it in 478 00:29:18,640 --> 00:29:23,120 Speaker 1: order to control various functions, all of these operating independently 479 00:29:23,240 --> 00:29:26,360 Speaker 1: of each other. But it also means that with the 480 00:29:26,440 --> 00:29:32,280 Speaker 1: elements of the network, these can become effectively edge devices. 481 00:29:32,280 --> 00:29:35,000 Speaker 1: So you've got your edge servers and you've got your 482 00:29:35,200 --> 00:29:39,640 Speaker 1: edge devices, which if they weren't connected to any other network, 483 00:29:39,720 --> 00:29:42,800 Speaker 1: you would just call them computers. When we come back, 484 00:29:42,960 --> 00:29:46,040 Speaker 1: we'll talk a bit more about edge computing and some 485 00:29:46,120 --> 00:29:49,480 Speaker 1: of the pauses and some of the challenges associated with it, 486 00:29:49,840 --> 00:29:52,360 Speaker 1: and what we might expect to see develop in the 487 00:29:52,560 --> 00:30:04,400 Speaker 1: near future. But first let's take another quick break. One 488 00:30:04,440 --> 00:30:07,240 Speaker 1: thing I haven't talked about so far in this episode 489 00:30:07,640 --> 00:30:11,920 Speaker 1: is containers, not physical containers that you would find in 490 00:30:11,960 --> 00:30:15,720 Speaker 1: the real world, but rather the concept of containers within 491 00:30:15,760 --> 00:30:19,920 Speaker 1: the context of software development and deployment. Now, remember how 492 00:30:19,920 --> 00:30:23,920 Speaker 1: I talked about how packet switching and how information travels 493 00:30:24,000 --> 00:30:27,680 Speaker 1: across the Internet and how data packets play apart. Well, 494 00:30:28,320 --> 00:30:31,920 Speaker 1: in a kind of similar way, containers are standard units 495 00:30:32,120 --> 00:30:36,360 Speaker 1: of software. So we're not just talking about data, we're 496 00:30:36,360 --> 00:30:40,080 Speaker 1: actually talking about applications here, and a container is a 497 00:30:40,120 --> 00:30:43,440 Speaker 1: way to put together everything that a specific piece of 498 00:30:43,480 --> 00:30:47,520 Speaker 1: software needs in order for it to operate. That includes 499 00:30:47,600 --> 00:30:50,640 Speaker 1: the system tools that it needs, the code of the 500 00:30:50,720 --> 00:30:54,360 Speaker 1: app itself, any libraries the code has to draw upon 501 00:30:54,440 --> 00:30:57,640 Speaker 1: in the process of executing the program, and so on. 502 00:30:58,000 --> 00:31:01,120 Speaker 1: So essentially you can think of contain inners as all 503 00:31:01,200 --> 00:31:04,480 Speaker 1: the stuff this particular app needs in order to do 504 00:31:04,720 --> 00:31:07,520 Speaker 1: whatever it is the app does. Now, the reason that 505 00:31:07,600 --> 00:31:11,560 Speaker 1: containers are important is that they allowed developers to move 506 00:31:11,720 --> 00:31:16,720 Speaker 1: software two different operating environments easily. Let's say that you 507 00:31:16,800 --> 00:31:19,840 Speaker 1: are developing an app, and you might first develop it 508 00:31:20,040 --> 00:31:22,840 Speaker 1: on your own machine, but then you actually need to 509 00:31:22,880 --> 00:31:24,960 Speaker 1: test the app out. You've built it up to a 510 00:31:25,000 --> 00:31:28,040 Speaker 1: point where you can run some tests, get some people 511 00:31:28,080 --> 00:31:30,520 Speaker 1: in there to check it out, go through all the features, 512 00:31:30,600 --> 00:31:33,239 Speaker 1: make sure it works. So you want to deploy it 513 00:31:33,320 --> 00:31:38,440 Speaker 1: to a test environment, a discrete environment in which the 514 00:31:38,440 --> 00:31:40,960 Speaker 1: app can behave as if it were released to the wild. 515 00:31:41,400 --> 00:31:44,040 Speaker 1: But here's the important part. You haven't actually released it 516 00:31:44,040 --> 00:31:46,480 Speaker 1: out to the wild, so that gives the opportunity to 517 00:31:46,480 --> 00:31:50,280 Speaker 1: find any problems with it, vulnerabilities, anything that could be 518 00:31:50,760 --> 00:31:53,640 Speaker 1: detrimental Once it was released. You can do all that 519 00:31:53,720 --> 00:31:56,520 Speaker 1: in a safe space. So you do that, and you 520 00:31:56,560 --> 00:31:58,920 Speaker 1: find out what works and what doesn't work. You go back, 521 00:31:59,000 --> 00:32:01,200 Speaker 1: you make some changes, you deploy it again to the 522 00:32:01,200 --> 00:32:04,400 Speaker 1: test environment, you test it again. Once it gets to 523 00:32:04,440 --> 00:32:06,760 Speaker 1: the point that the app seems to be working the 524 00:32:06,800 --> 00:32:09,640 Speaker 1: way you wanted, then maybe you move it to a 525 00:32:09,720 --> 00:32:14,160 Speaker 1: staging environment, and from there it goes into production and 526 00:32:14,200 --> 00:32:17,120 Speaker 1: it becomes an app that end users can actually download 527 00:32:17,160 --> 00:32:21,320 Speaker 1: and install on their devices and actually use well. Containers 528 00:32:21,560 --> 00:32:25,520 Speaker 1: make it easier to move this app from one environment 529 00:32:25,520 --> 00:32:28,680 Speaker 1: to another, from laptop to test environment and back again, 530 00:32:29,120 --> 00:32:32,920 Speaker 1: or test environment to staging environment, staging environment to production, 531 00:32:33,360 --> 00:32:38,680 Speaker 1: et cetera. And these environments can all have different elements 532 00:32:38,840 --> 00:32:42,360 Speaker 1: from one another, and those differences could mean that code 533 00:32:42,480 --> 00:32:46,200 Speaker 1: that works really well in one environment suddenly doesn't work 534 00:32:46,240 --> 00:32:48,520 Speaker 1: at all or is doing really weird stuff in a 535 00:32:48,600 --> 00:32:53,360 Speaker 1: different environment. But containers contain all the elements of the 536 00:32:53,360 --> 00:32:56,560 Speaker 1: app needs to run properly, so that at least in theory, 537 00:32:57,160 --> 00:33:00,280 Speaker 1: the code should run the same way regardless of whatever 538 00:33:00,440 --> 00:33:03,360 Speaker 1: environment it is in, because all the requirements for the 539 00:33:03,400 --> 00:33:06,720 Speaker 1: app are contained along with the code of the app itself. 540 00:33:07,240 --> 00:33:09,320 Speaker 1: Now I get that this is a little difficult to 541 00:33:09,400 --> 00:33:11,800 Speaker 1: grock for some folks. I mean it's tough for me 542 00:33:11,920 --> 00:33:15,640 Speaker 1: and I have covered it multiple times. But beyond containers, 543 00:33:15,640 --> 00:33:17,800 Speaker 1: we have another term that frequently pops up when we 544 00:33:17,840 --> 00:33:21,280 Speaker 1: talk about cloud computing and edge computing, and that term 545 00:33:21,400 --> 00:33:25,040 Speaker 1: is the dreaded Kubernetes, which refers to in now open 546 00:33:25,080 --> 00:33:29,080 Speaker 1: source platform for a container management. So in other words, 547 00:33:29,480 --> 00:33:33,160 Speaker 1: this is kind of one step up from the containers themselves. 548 00:33:33,200 --> 00:33:38,080 Speaker 1: This is a product that helps teams operationalized containers at scale. 549 00:33:38,360 --> 00:33:42,160 Speaker 1: Because it's one thing to move a container from a 550 00:33:43,000 --> 00:33:46,840 Speaker 1: you know, a developer laptop to a testing environment. It's 551 00:33:46,880 --> 00:33:51,040 Speaker 1: another thing to deploy software that is going to potentially 552 00:33:51,160 --> 00:33:54,640 Speaker 1: millions of users. So from a software perspective, we could 553 00:33:54,720 --> 00:33:58,040 Speaker 1: say that Kubernetes and containers are trying to do from 554 00:33:58,080 --> 00:34:01,440 Speaker 1: a code approach what ed computing is trying to do 555 00:34:01,560 --> 00:34:04,400 Speaker 1: from a hardware approach, But in reality, all the stuff 556 00:34:04,480 --> 00:34:07,800 Speaker 1: kind of gets mixed up together. Containers make it easier 557 00:34:07,880 --> 00:34:11,520 Speaker 1: from an operational standpoint to deploy apps to the edge, 558 00:34:11,880 --> 00:34:15,360 Speaker 1: whether that's to edge devices where you're more likely to 559 00:34:15,440 --> 00:34:20,040 Speaker 1: use a simple container platform, or to edge servers where 560 00:34:20,040 --> 00:34:23,520 Speaker 1: you might be relying on the more robust Kubernetes platform. 561 00:34:23,560 --> 00:34:26,160 Speaker 1: So these are all pieces of a puzzle that makes 562 00:34:26,280 --> 00:34:30,400 Speaker 1: edge computing a viable strategy. Now, there's some other considerations 563 00:34:30,400 --> 00:34:33,000 Speaker 1: that I T professionals have to make when it comes 564 00:34:33,000 --> 00:34:37,360 Speaker 1: to edge computing. Distributing computing to the edge comes with challenges. 565 00:34:37,640 --> 00:34:41,200 Speaker 1: For example, when you've got your own on premises cloud 566 00:34:41,280 --> 00:34:45,239 Speaker 1: computing data center, you have a lot of control when 567 00:34:45,239 --> 00:34:48,600 Speaker 1: it comes to ensuring the safety of your equipment and 568 00:34:48,640 --> 00:34:52,400 Speaker 1: the information that it holds. That spans physical safety. That 569 00:34:52,440 --> 00:34:55,239 Speaker 1: means you can actually make sure that those facilities are 570 00:34:55,320 --> 00:35:00,000 Speaker 1: protected that unauthorized people cannot easily get physical access to machine. 571 00:35:00,000 --> 00:35:03,919 Speaker 1: Means that you've got a properly cooled facility to deal 572 00:35:03,960 --> 00:35:06,160 Speaker 1: with all the heat that those computers are giving off. 573 00:35:06,280 --> 00:35:09,120 Speaker 1: That kind of thing. It also means that you have 574 00:35:09,160 --> 00:35:12,120 Speaker 1: more control over cyber security, so you can make sure 575 00:35:12,320 --> 00:35:15,640 Speaker 1: that protections are in place to keep things running smoothly. 576 00:35:16,160 --> 00:35:19,759 Speaker 1: But moving out to the edge creates more opportunities for 577 00:35:19,840 --> 00:35:22,840 Speaker 1: bad actors to find a way to attack a system. 578 00:35:22,880 --> 00:35:26,520 Speaker 1: So creating an edge computing network that is easy to 579 00:35:26,560 --> 00:35:31,280 Speaker 1: administer and orchestrate while also being secure is a pretty 580 00:35:31,280 --> 00:35:35,440 Speaker 1: big hurdle. It may mean leaning heavily on other entities 581 00:35:35,480 --> 00:35:38,839 Speaker 1: and trusting that they've got their act together, and as 582 00:35:38,840 --> 00:35:42,560 Speaker 1: we've seen pretty recently, sometimes that it turns out that 583 00:35:42,600 --> 00:35:46,080 Speaker 1: a trusted entity has been compromised and then there can 584 00:35:46,120 --> 00:35:50,359 Speaker 1: be fallout of specifically, thinking about the Solar winds hack. Now, 585 00:35:50,360 --> 00:35:53,319 Speaker 1: in that case, we weren't talking about edge computing, but 586 00:35:53,360 --> 00:35:57,359 Speaker 1: I'm using it to illustrate a point. The interconnectedness of 587 00:35:57,400 --> 00:36:00,760 Speaker 1: these systems and the fact that you might talking about 588 00:36:00,920 --> 00:36:04,400 Speaker 1: half a dozen companies that own parts of this network 589 00:36:04,920 --> 00:36:08,279 Speaker 1: that are all involved in this edge computing enterprise means 590 00:36:08,320 --> 00:36:12,120 Speaker 1: that you're asking a lot of organizations to trust one another, 591 00:36:12,600 --> 00:36:16,160 Speaker 1: and if one of those organizations gets compromised, there's a 592 00:36:16,280 --> 00:36:20,120 Speaker 1: danger that hackers could take advantage of those trusted relationships 593 00:36:20,360 --> 00:36:23,759 Speaker 1: to gain access to the others. Now, related to the 594 00:36:23,800 --> 00:36:27,200 Speaker 1: issue of security is privacy. When it comes to the 595 00:36:27,239 --> 00:36:30,000 Speaker 1: Internet of Things, we're often talking about devices that are 596 00:36:30,040 --> 00:36:34,399 Speaker 1: gathering data about us in our activities. Yes, we've also 597 00:36:34,480 --> 00:36:38,080 Speaker 1: got devices that are sensing environments and not so much 598 00:36:38,160 --> 00:36:41,160 Speaker 1: focused on people, but a lot of devices are either 599 00:36:41,280 --> 00:36:45,600 Speaker 1: directly or indirectly keeping track of where people are, who 600 00:36:45,640 --> 00:36:48,640 Speaker 1: they are, and what they're doing. Now that data can 601 00:36:48,680 --> 00:36:51,520 Speaker 1: be useful for a lot of good things, but it 602 00:36:51,560 --> 00:36:56,480 Speaker 1: can obviously also be misused or outright abused. So there 603 00:36:56,600 --> 00:37:00,000 Speaker 1: is an onus on companies to make sure they are 604 00:37:00,040 --> 00:37:03,960 Speaker 1: good stewards of data and that they protect that information. 605 00:37:04,400 --> 00:37:07,880 Speaker 1: And since this information is potentially moving between lots of 606 00:37:07,920 --> 00:37:12,880 Speaker 1: different points, from say the endpoint in the environment or 607 00:37:12,960 --> 00:37:17,200 Speaker 1: on a user through the edge network and potentially further 608 00:37:17,320 --> 00:37:20,239 Speaker 1: up the chain to a cloud computing network, there are 609 00:37:20,280 --> 00:37:24,120 Speaker 1: a lot of opportunities for vulnerabilities. Now. While we often 610 00:37:24,160 --> 00:37:27,720 Speaker 1: associate vulnerabilities with hackers who are trying to find ways 611 00:37:27,760 --> 00:37:31,600 Speaker 1: to exploit systems, a vulnerability could just as easily be 612 00:37:31,680 --> 00:37:35,479 Speaker 1: a poorly protected web portal that is publishing what should 613 00:37:35,520 --> 00:37:39,240 Speaker 1: otherwise be private data. We've seen this happen with companies 614 00:37:39,280 --> 00:37:43,000 Speaker 1: where somewhere someone along the chain failed to take into 615 00:37:43,000 --> 00:37:46,560 Speaker 1: account what was actually going on, and data that should 616 00:37:46,600 --> 00:37:50,680 Speaker 1: have remained protected in private was somehow published publicly or 617 00:37:50,800 --> 00:37:55,600 Speaker 1: semi publicly. As we look at decentralized computer systems, we 618 00:37:55,680 --> 00:37:58,919 Speaker 1: see a lot more points where this kind of thing 619 00:37:59,200 --> 00:38:03,319 Speaker 1: could potentially happen, either with the raw data collected in 620 00:38:03,360 --> 00:38:07,200 Speaker 1: the wild or then the processed data that comes out 621 00:38:07,200 --> 00:38:10,400 Speaker 1: of the edge network. Either way, that's something else that 622 00:38:10,440 --> 00:38:14,080 Speaker 1: I T professionals have to focus on. Reducing the number 623 00:38:14,080 --> 00:38:16,600 Speaker 1: of times data needs to move from one part of 624 00:38:16,640 --> 00:38:20,120 Speaker 1: the system to the other is potentially one solution toward 625 00:38:20,200 --> 00:38:25,040 Speaker 1: providing better privacy and security. You're reducing the number of hops. 626 00:38:25,080 --> 00:38:28,640 Speaker 1: You're reducing the number of vulnerabilities that could potentially exist. 627 00:38:29,160 --> 00:38:31,640 Speaker 1: If all the computing can stay at the edge and 628 00:38:31,760 --> 00:38:35,000 Speaker 1: nothing needs to you know, phone home to the centralized 629 00:38:35,160 --> 00:38:40,080 Speaker 1: data center, there are fewer opportunities for something to go astray. Similarly, 630 00:38:40,239 --> 00:38:43,360 Speaker 1: we will likely see lots of companies specialize in ways 631 00:38:43,400 --> 00:38:46,480 Speaker 1: to manage the flow of data between devices on the 632 00:38:46,560 --> 00:38:49,960 Speaker 1: edge of a network and big data centers. I think 633 00:38:49,960 --> 00:38:53,680 Speaker 1: that's going to be a really big business. That it's 634 00:38:53,680 --> 00:38:57,879 Speaker 1: like enterprise to enterprise business. But but what if you're 635 00:38:57,880 --> 00:39:00,120 Speaker 1: not an I T professional? I mean, I'm not so 636 00:39:00,160 --> 00:39:02,400 Speaker 1: what if you're like me, it's not your job to 637 00:39:02,440 --> 00:39:04,640 Speaker 1: worry about this kind of stuff. What does all this 638 00:39:04,800 --> 00:39:08,880 Speaker 1: actually mean to you? Well, essentially, it means the gradual 639 00:39:08,920 --> 00:39:14,880 Speaker 1: introduction of more lightweight technologies that have increasingly usefulness in 640 00:39:15,080 --> 00:39:18,560 Speaker 1: our lives. Well, maybe usefulness is going a bit far. 641 00:39:18,840 --> 00:39:21,640 Speaker 1: We'll be able to do a lot more stuff with 642 00:39:21,760 --> 00:39:26,239 Speaker 1: different devices, interacting with our environments and with ourselves. Not 643 00:39:26,320 --> 00:39:28,760 Speaker 1: all of it might be useful. I mean I often 644 00:39:28,840 --> 00:39:32,040 Speaker 1: talk about augmented reality applications like I did earlier in 645 00:39:32,040 --> 00:39:34,440 Speaker 1: this episode, where you know, you use an app on 646 00:39:34,480 --> 00:39:37,279 Speaker 1: a smartphone, or maybe you're lucky you've got a pair 647 00:39:37,320 --> 00:39:39,920 Speaker 1: of a R goggles and you're able to visualize the 648 00:39:39,960 --> 00:39:42,879 Speaker 1: world around you in different ways. You know, I love 649 00:39:42,960 --> 00:39:46,640 Speaker 1: this idea of going to the site of an old 650 00:39:46,680 --> 00:39:49,520 Speaker 1: castle and looking at the ruins and then seeing a 651 00:39:49,600 --> 00:39:52,520 Speaker 1: virtual reconstruction of what the castle looked like when it 652 00:39:52,600 --> 00:39:55,680 Speaker 1: was in its heyday. That to me is like the 653 00:39:55,719 --> 00:39:59,840 Speaker 1: gold standard of a R applications, Which can you know 654 00:40:00,120 --> 00:40:03,239 Speaker 1: tells you that I majored in Medieval English literature when 655 00:40:03,280 --> 00:40:05,880 Speaker 1: I was in college. But I also admit that the 656 00:40:05,920 --> 00:40:09,000 Speaker 1: reality of a R means I'll probably see a lot 657 00:40:09,040 --> 00:40:14,080 Speaker 1: more let's call them frivolous uses of the technology, like 658 00:40:14,719 --> 00:40:17,680 Speaker 1: walking through a grocery store and seeing characters in the 659 00:40:17,719 --> 00:40:21,520 Speaker 1: front of cereal boxes seemingly come to life, inviting me 660 00:40:21,600 --> 00:40:24,880 Speaker 1: to enjoy their sugary goodness. Or I might look at 661 00:40:24,880 --> 00:40:27,920 Speaker 1: a movie poster and suddenly a trailer for that film 662 00:40:27,960 --> 00:40:31,160 Speaker 1: begins to play inside my vision. A lot of the 663 00:40:31,200 --> 00:40:35,480 Speaker 1: services that we use are monetized in various ways, and 664 00:40:35,600 --> 00:40:37,960 Speaker 1: I mean, I'm not knocking it. That makes sense. No 665 00:40:37,960 --> 00:40:40,680 Speaker 1: one wants to work for free. But that often means 666 00:40:40,719 --> 00:40:44,080 Speaker 1: that all certain technologies might have incredible potential. We also 667 00:40:44,120 --> 00:40:48,320 Speaker 1: have to wade through a lot of less lofty applications. 668 00:40:48,920 --> 00:40:51,680 Speaker 1: But edge computing is going to make that sort of 669 00:40:51,680 --> 00:40:55,560 Speaker 1: stuff possible. Without edge computing, we wouldn't have the responsiveness 670 00:40:55,600 --> 00:41:00,200 Speaker 1: capable of generating those experiences in a timely fashion. Sin 671 00:41:01,120 --> 00:41:04,200 Speaker 1: So with edge computing, we're also going to see a 672 00:41:04,239 --> 00:41:08,239 Speaker 1: lot of one of my old favorite words, convergence, the 673 00:41:08,280 --> 00:41:13,160 Speaker 1: convergence of multiple disciplines of technology creating new approaches towards 674 00:41:13,560 --> 00:41:18,239 Speaker 1: various problems. You've got your communication channels supplied by technologies 675 00:41:18,280 --> 00:41:21,840 Speaker 1: like a robust five G rollout. You've got your computer 676 00:41:21,960 --> 00:41:26,080 Speaker 1: technologies at the edge of the network. Behind that, you've 677 00:41:26,120 --> 00:41:31,440 Speaker 1: got machine learning, artificial intelligence, autonomous management taking over tasks, 678 00:41:31,480 --> 00:41:36,320 Speaker 1: optimizing them, always striving towards constant improvement. It's a pretty 679 00:41:36,400 --> 00:41:38,920 Speaker 1: cool way to look at the future. Even something as 680 00:41:39,120 --> 00:41:42,919 Speaker 1: seemingly dull as supply chain management really could have big 681 00:41:42,960 --> 00:41:47,280 Speaker 1: results to consumers down the road. Literally and figuratively, imagine 682 00:41:47,760 --> 00:41:50,440 Speaker 1: that the price of some goods starts to come down 683 00:41:50,600 --> 00:41:54,440 Speaker 1: because we've actually developed far more efficient approaches to producing 684 00:41:54,520 --> 00:41:58,279 Speaker 1: and shipping that stuff, and thus it costs less to 685 00:41:58,320 --> 00:42:02,920 Speaker 1: get it to market, and various companies are competing with 686 00:42:02,960 --> 00:42:06,319 Speaker 1: one another, so they reduce the price to you. That's 687 00:42:06,360 --> 00:42:09,120 Speaker 1: one benefit we could see through this kind of robust 688 00:42:09,280 --> 00:42:13,839 Speaker 1: rollout of edge computing. However, we have to remember one 689 00:42:14,320 --> 00:42:17,680 Speaker 1: this version of the future is not a guarantee. It's 690 00:42:17,719 --> 00:42:21,120 Speaker 1: a possibility. It's also good to think about the larger 691 00:42:21,200 --> 00:42:25,560 Speaker 1: effect that we see as a consequence of more computing systems, 692 00:42:26,000 --> 00:42:30,640 Speaker 1: more IoT devices, more processing, because this ultimately means we 693 00:42:30,760 --> 00:42:34,520 Speaker 1: have to consume more energy. We need more electricity to 694 00:42:34,640 --> 00:42:38,160 Speaker 1: fuel all this stuff, which means we got to produce 695 00:42:38,239 --> 00:42:42,400 Speaker 1: more electricity, which frequently means we also are going to 696 00:42:42,480 --> 00:42:46,000 Speaker 1: have a big ecological impact as a result of all 697 00:42:46,080 --> 00:42:49,920 Speaker 1: this progress, assuming that we're still relying heavily on fossil 698 00:42:50,000 --> 00:42:55,080 Speaker 1: fuels for our generation of electricity. Nothing exists in a 699 00:42:55,160 --> 00:42:57,960 Speaker 1: vacuum except for all that light and zipping around out 700 00:42:58,040 --> 00:43:01,920 Speaker 1: in space. This is all interconnected. We have to train 701 00:43:01,960 --> 00:43:05,840 Speaker 1: ourselves to think about big picture stuff, to tackle problems 702 00:43:05,880 --> 00:43:11,520 Speaker 1: in a way that aren't exacerbating different but very important problems. Now, 703 00:43:11,560 --> 00:43:14,840 Speaker 1: I never said I had all the answers. Heck, I 704 00:43:14,880 --> 00:43:18,480 Speaker 1: only have a couple of answers. For example, the capital 705 00:43:18,480 --> 00:43:22,520 Speaker 1: of Iceland is Reka, Vic doesn't really apply here, but 706 00:43:22,640 --> 00:43:26,560 Speaker 1: that's my point. But yes, that's our overview of edge 707 00:43:26,600 --> 00:43:29,799 Speaker 1: computing and the role it plays within networks and our 708 00:43:29,880 --> 00:43:33,360 Speaker 1: experiences with technology. It is one of those things that 709 00:43:33,400 --> 00:43:36,200 Speaker 1: continues to evolve. And like I said, this one's a 710 00:43:36,200 --> 00:43:39,120 Speaker 1: pretty young one. Like I think the earliest mentions I 711 00:43:39,120 --> 00:43:43,840 Speaker 1: could find were somewhere around t so not that old 712 00:43:44,000 --> 00:43:47,279 Speaker 1: in terms of technologies that we have at our disposal. 713 00:43:47,800 --> 00:43:52,000 Speaker 1: So I'll probably be doing multiple episodes about this further 714 00:43:52,080 --> 00:43:55,880 Speaker 1: into the future as we see it evolve over time 715 00:43:55,920 --> 00:44:00,279 Speaker 1: and different implementations take shape. I'm excited to see what 716 00:44:00,360 --> 00:44:05,160 Speaker 1: will become a reality based on this technology. Uh, And 717 00:44:05,239 --> 00:44:08,960 Speaker 1: of course I am concerned about the impacts that the 718 00:44:09,000 --> 00:44:13,840 Speaker 1: technology will have beyond just its direct application. But I 719 00:44:13,880 --> 00:44:16,640 Speaker 1: think we can leave off here and then we will 720 00:44:16,680 --> 00:44:21,239 Speaker 1: come back with new episodes about other stuff. So if 721 00:44:21,280 --> 00:44:23,799 Speaker 1: you have any suggestions about other stuff, I can cover 722 00:44:24,120 --> 00:44:29,000 Speaker 1: preferably related to technology. I mean, if you want me 723 00:44:29,040 --> 00:44:32,319 Speaker 1: to talk about ka Vic, I'll do it. It's just 724 00:44:32,400 --> 00:44:34,560 Speaker 1: not I don't know how well it fits in with 725 00:44:34,600 --> 00:44:37,480 Speaker 1: tech stuff, and my sponsors might get mad the heck 726 00:44:37,880 --> 00:44:39,800 Speaker 1: um game. If you are, let me know what you 727 00:44:39,840 --> 00:44:41,960 Speaker 1: would like me to talk about in future episodes. The 728 00:44:41,960 --> 00:44:44,360 Speaker 1: best way to do that is to reach out on Twitter. 729 00:44:44,880 --> 00:44:48,160 Speaker 1: The handle I use is tech stuff H s W 730 00:44:49,040 --> 00:44:57,280 Speaker 1: and I'll talk to you again. Release it. Tex Stuff 731 00:44:57,400 --> 00:45:00,360 Speaker 1: is an I Heart Radio production. For more POE casts 732 00:45:00,400 --> 00:45:03,160 Speaker 1: from I Heart Radio, visit the I Heart Radio app, 733 00:45:03,280 --> 00:45:06,440 Speaker 1: Apple Podcasts, or wherever you listen to your favorite shows. 734 00:45:10,760 --> 00:45:10,800 Speaker 1: H