Speaker 1: Imagine just for a second that you're running a massive
Speaker 1: global website.
Speaker 2: Right like a huge news platform or something.
Speaker 1: Exactly, and suddenly some totally unpredictable news event breaks. In
Speaker 1: the span of like two minutes, your traffic spikes by
Speaker 1: one thousand percent.
Speaker 2: Oh wow, Yeah, panic time right in.
Speaker 3: The Old Danes.
Speaker 1: And honestly, for a vast majority of the systems we
Speaker 1: still rely on today, you know exactly what happens next.
Speaker 1: The servers get overwhelmed, the request que fills up, the
Speaker 1: load balancer completely panics, and.
Speaker 2: Everyone trying to access your site just gets a blank
Speaker 2: white screen.
Speaker 1: Yeah, that generic error message And I mean every second
Speaker 1: that site is down is costing you money, your reputation,
Speaker 1: user trust, all of it. But and here's the hook.
Speaker 1: What if your software could sense that it was about
Speaker 1: to crash, diagnose the exact bottleneck in its own hardware,
Speaker 1: and completely rewrite its internal architecture to survive.
Speaker 2: That spike, just fix itself on the fly.
Speaker 3: Exactly.
Speaker 1: What if it could do all of that in a
Speaker 1: matter of milliseconds without a single human being ever even
Speaker 1: touching a keyboard.
Speaker 2: I mean, it sounds like we're talking about a living organism, right,
Speaker 2: Like something with a biological immune system that just heals
Speaker 2: itself when it gets a cut.
Speaker 3: It really does.
Speaker 2: But the reality is this isn't science fiction at all.
Speaker 2: We are already engineering digital systems that can do exactly this.
Speaker 1: Well, Welcome to today's deep dive. Today we are looking
Speaker 1: at a concept that fundamentally changes how we interact with technology.
Speaker 1: It's called autonomic computing. It's a fascinating space, it really is,
Speaker 1: and we've pulled insights from some of the leading frameworks
Speaker 1: in the field, including some incredibly detailed work from researchers
Speaker 1: like rodu Kalanescu and David Garland's team. We really want
Speaker 1: to figure out exactly how computer systems and networks are
Speaker 1: being built to manage, optimize, and heal themselves.
Speaker 2: Cut through the noise and really see how these systems
Speaker 2: actually think.
Speaker 1: Right, Okay, let's unpack this before we get into the
Speaker 1: incredibly complex frameworks these engineers are deploying today. I feel
Speaker 1: like we need to understand the basic mechanism. How does
Speaker 1: a machine actually watch itself? Why is this self watching
Speaker 1: even necessary in the first place.
Speaker 2: So to understand the core why, we really have to
Speaker 2: look at how traditional software has been built for decades. Historically,
Speaker 2: software systems are what we call open loop. They're designed
Speaker 2: to execute a specific function and they just blindly run
Speaker 2: that function until, well, until something.
Speaker 3: Breaks right or they run out of resources.
Speaker 2: Exactly, or a server literally catches fire when they hit
Speaker 2: a wall. They stop period, and then a human administrator
Speaker 2: has to step in, look at the air logs, figure
Speaker 2: out what went wrong, deploy a fix, and restart the system.
Speaker 2: And that takes a lot of time, tons of time.
Speaker 2: That human intervention is the bottleneck. It causes massive costly downtime.
Speaker 2: So the entire goal of autonomic computing is to eliminate
Speaker 2: that human vulnerability by creating a closed loop feedback system.
Speaker 3: A closed loop.
Speaker 1: Okay, so just briefly, is this kind of like a
Speaker 1: home thermostat.
Speaker 2: Ooh, that's a good comparison.
Speaker 1: Like you set the room temperature you want. The thermostat
Speaker 1: measures the actual temperature in the room, compares it to
Speaker 1: your goal, and if it's too cold, it turns on
Speaker 1: the heat, and once it hits the right temperature, it
Speaker 1: turns the heat off. It's basically managing itself based on
Speaker 1: its environment.
Speaker 2: The thermostat is like the perfect baseline analogy for a
Speaker 2: closed loop. But a modern distributed software system is infinitely
Speaker 2: more complex than a heating element. Yeah, I'd imagine a
Speaker 2: software system requires a much more explicit, highly detailed model
Speaker 2: of itself to function. You can't just measure temperature. You're
Speaker 2: measuring thousands of different metrics simultaneously, right, right, Which brings
Speaker 2: us to the foundational blueprint for these systems, the IBM
Speaker 2: m APE reference Model.
Speaker 3: M APE MAPE right exactly.
Speaker 2: It's an acronym that stands for monitor, analyze, plan and execute.
Speaker 2: These are the four distinct phases of the closed loop system.
Speaker 1: Okay, let's break those down. Monitor, analyze, plan, execute. If
Speaker 1: I'm a system administrator, what is my system actually doing
Speaker 1: in that very first phase?
Speaker 2: So, in the monitor phase, the system is extracting raw
Speaker 2: information from the managed resources. It's constantly pulling data on
Speaker 2: CPU load, memory usage, network traffic, discread write speeds.
Speaker 3: Just vacuuming up all this raw data.
Speaker 2: Yep. Then it moves to analyze. The system takes that
Speaker 2: raw data and determines if something has actually gone orwry.
Speaker 2: And crucially, it's not just looking for a crash, it
Speaker 2: is looking for a degrading trend.
Speaker 3: Oh interesting, Give me an example.
Speaker 2: Of that well, for example, it might notice that memory
Speaker 2: usage has been creeping up by say one percent every hour,
Speaker 2: so it's predicting a memory leak before the system actually
Speaker 2: runs out of RAM.
Speaker 1: Wow, which is way more advanced than just waiting for
Speaker 1: the system to completely break, much more proactive and reading
Speaker 1: through the sources. What really jumped out at me is
Speaker 1: that these four phases, they don't just happen in a vacuum.
Speaker 2: No, not at all.
Speaker 1: They're all orbiting around the central component called knowledge The models,
Speaker 1: the historical data, the behavioral scripts. That knowledge base seems
Speaker 1: to be the actual brain that the mate loop uses
Speaker 1: to make sense of the world.
Speaker 2: Yeah. The knowledge component is what separates a simple automated
Speaker 2: reflex from an intelligent adaptation. The knowledge base holds the
Speaker 2: topology of the network and all the historical performance logs.
Speaker 3: So it remembers what happened before exactly.
Speaker 2: If we connect this to the bigger picture, this closed
Speaker 2: loop architecture allows engineers to completely separate a system's primary
Speaker 2: functionality from its adaptation behaviors. What do you mean by that, Well,
Speaker 2: the software doing the main job, say processing credit card transactions,
Speaker 2: doesn't have to be bogged down with the logic of
Speaker 2: how to fix its own server memory. Oh I see,
Speaker 2: the autonomic manager sits outside of that primary software, watching, analyzing,
Speaker 2: and tweaking based on its knowledge base.
Speaker 1: Okay, so conceptually that makes total sense. But taking that
Speaker 1: theory and building it into complex modern software without having
Speaker 1: to rewrite millions of lines of code.
Speaker 2: From scratch, Yeah, that's the hard part.
Speaker 3: That seems like a monumental task.
Speaker 1: You can't just slap a self healing sticker on a
Speaker 1: massive corporate network. How do engineers actually bridge that gap?
Speaker 2: They use frameworks designed specifically for this, and one of
Speaker 2: the most prominent ones discussed in our source material is
Speaker 2: the Rainbow framework.
Speaker 3: Rainbow.
Speaker 2: Yeah, Rainbow is fascinating because it relies on what the
Speaker 2: researchers call architecture based self adaptation.
Speaker 3: Okay, unpack that a bit.
Speaker 2: Instead of the autonomic manager trying to read and rewrite
Speaker 2: raw lines of code, which would be incredibly slow and
Speaker 2: highly dangerous, Rainbow uses an abstract blueprint of the system
Speaker 2: a blueprint, right. It looks at the system from a
Speaker 2: bird's eye view, as a collection of high level components
Speaker 2: like clients and servers and connectors like HTTP pipelines.
Speaker 1: Okay, I really want to bring this to life because
Speaker 1: the text provides this brilliant concrete example a system they
Speaker 1: call ZNN dot com.
Speaker 2: Oh yes, the news site, right.
Speaker 1: It's a multimedia news site modeled in an ND tier style.
Speaker 1: So for anyone listening trying to visualize this, You've got
Speaker 1: a load balancer at the front door. That load balancer
Speaker 1: passes incoming user requests to a pool of replicated web servers,
Speaker 1: and then those servers pull articles and videos from a
Speaker 1: back end database.
Speaker 2: It's a very standard, highly common architecture for the web today.
Speaker 3: Right, So let's set the scenario.
Speaker 1: A massive global news event happens, ZNN dot com suddenly
Speaker 1: experiences astronomical traffic spikes.
Speaker 3: As we discussed.
Speaker 1: Earlier, Normally, the web servers get overloaded, the load balancer panics,
Speaker 1: and the website.
Speaker 2: Crashes total filling.
Speaker 1: So how does the Rainbow framework actually prevent this catastrophic
Speaker 1: failure using that bird's eye view you.
Speaker 2: Mentioned, It all comes down to Rainbow's translation infrastructure. You
Speaker 2: have to bridge the gap between the raw, messy reality
Speaker 2: of the physical hardware and the clean, abstract architectural blueprint
Speaker 2: that Rainbow was looking at. Okay, and Rainbow does this
Speaker 2: using two critical tools, probes and gages. Probes are deployed
Speaker 2: deep inside the target system to measure raw data. So
Speaker 2: a probe might just be a script that constantly reports
Speaker 2: CPU load on server three is at ninety five percent.
Speaker 1: So the probe is just a dumb thermometer. Essentially, it
Speaker 1: doesn't know what the temperature means. It just reports the number.
Speaker 2: That is the perfect way to look at it. But
Speaker 2: then you have the gauges. Okay, gauges are the doctors
Speaker 2: reading that thermometer. A gauge takes that raw probe data
Speaker 2: and interprets it into an architectural property.
Speaker 3: Interesting, So a.
Speaker 2: Gauge takes that ninety five percent CPU load, correlates it
Speaker 2: with the current incoming network traffic, looks at the database
Speaker 2: response times, and reports to the system the average response
Speaker 2: latency for a client is currently four thousand milliseconds.
Speaker 1: Oh wow.
Speaker 2: Yeah. Once that gauge updates the architectural model, the architecture
Speaker 2: evaluator kicks in. It constantly checks these architectural properties against
Speaker 2: predefined rules. For ZNN dot com, there is a hard
Speaker 2: rule written into the system. If a property called client
Speaker 2: t recristplatency exceeds a specific maximum threshold, it triggers a
Speaker 2: system wide alarm.
Speaker 1: Okay, so the alarm is ringing. The system knows latency
Speaker 1: is way too high. People are waiting four seconds for
Speaker 1: a page to load, and users are getting frustrated.
Speaker 3: But how does it choose how to fix it?
Speaker 2: That's the million dollar question.
Speaker 1: This is the part that completely fascinates me, because it
Speaker 1: can't just blindly start flipping switches in the server room, right.
Speaker 1: It has to weigh actual business trade offs exactly.
Speaker 2: This is where we enter the plan phase of the
Speaker 2: MAPE loop, which is handled by Rainbow's adaptation manager, and
Speaker 2: it uses something called utility theory to make its decisions.
Speaker 3: Utility theory, right, The.
Speaker 2: System doesn't just want to fix the problem. It wants
Speaker 2: to fix the problem in a way that maximizes overall
Speaker 2: business value.
Speaker 1: The math behind this decision engine is just incredible. The
Speaker 1: system literally relies on four weighted quality dimensions for ZNN.
Speaker 2: Dot com YEP. The four dimensions.
Speaker 1: First, there is response time, which is the most important metrics.
Speaker 1: The business weighted at point four out of one second
Speaker 1: is budget weighted at point three.
Speaker 2: Makes sense.
Speaker 1: Third is content quality, meaning you know whether you're serving
Speaker 1: rich multimedia videos or just plain text that's weighted at
Speaker 1: point two. And finally, disruption how jarring the fixes to
Speaker 1: the current users, which is the least important, weighted at
Speaker 1: point one, and.
Speaker 2: Those weights dictate the entire personality of the system because
Speaker 2: when the adaptation manager looks at the high latency problem,
Speaker 2: it uses an adaptation language called Stitch to evaluate its
Speaker 2: possible strategies.
Speaker 3: Okay, Stitch Yeah.
Speaker 2: In this scenario, Stitch sees two primary options to lower
Speaker 2: the latency. Option A is in large serverble. It can
Speaker 2: spin up more virtual servers to handle the traffic.
Speaker 3: Which sounds good.
Speaker 2: It is good. This maintains the high quality multimedia content,
Speaker 2: which is great for the content quality score, but spinning
Speaker 2: up cloud servers costs actual money, which actively hurts the
Speaker 2: budget score.
Speaker 3: Ah right, So what's the other option?
Speaker 2: Option B is switch to textual mode. You can strip
Speaker 2: out all the hiras, videos and images and just serve
Speaker 2: plaintext articles.
Speaker 3: Oh wow.
Speaker 2: This tanks the content quality score obviously, but it drastically
Speaker 2: improves response time without spending a single dime, which preserves
Speaker 2: the budget score.
Speaker 3: Wait, I need to push back on this for a second.
Speaker 1: Sure, disruption is only weighted at point one if the
Speaker 1: system suddenly strips all the video off the site and
Speaker 1: switches to text while a user is mid scroll. People
Speaker 1: are going to be incredibly jarred by that. Why is
Speaker 1: the math telling the machine that user disruption barely matters
Speaker 1: compared to the budget.
Speaker 2: It's a harsh reality of business survival over user comfort.
Speaker 2: Really yeah, think about it. If the site goes down
Speaker 2: completely because they ran out of server budget, the disruption
Speaker 2: is absolute. Nobody gets anything right. So the engineers who
Speaker 2: set those utility weights decided that a momentary flicker where
Speaker 2: a video turns into text is a totally acceptable trade
Speaker 2: off if it guarantees the site stays online and the
Speaker 2: company doesn't go bankrupt paying for emergency cloud servers.
Speaker 3: You know, It's exactly like an er trioge nurse.
Speaker 2: Oh yeah, hou.
Speaker 1: So if you walk into a busy emergency room, the
Speaker 1: trioge nurse has to instantly weigh the severity of your
Speaker 1: symptoms against the hospital's available beds, the number of doctors
Speaker 1: on shift, and the inc humming ambulance traffic.
Speaker 3: Exactly, they are.
Speaker 1: Making real time, highly complex operational choices based on conflicting resources.
Speaker 1: They might put you in a hallway bed, which is
Speaker 1: you know, highly disruptive and uncomfortable, because saving the budget
Speaker 1: of a private room for a critical patient maximizes the
Speaker 1: overall utility of the hospital.
Speaker 2: That is a brilliant analogy. And what is truly remarkable
Speaker 2: here is how the system handles uncertainty within.
Speaker 3: That trioshe process uncertainty.
Speaker 2: Yeah, the adaptation manager knows that enacting a strategy doesn't
Speaker 2: guarantee success. If it chooses option A and tries to
Speaker 2: spin up a new server, maybe the cloud provider has
Speaker 2: a glitch and the server fails to start. Oh right,
Speaker 2: Or if it chooses action B and switches to textual mode,
Speaker 2: maybe the traffic spike is so massive that text only
Speaker 2: still overloads the system. So the system computes the expected
Speaker 2: utility using stochastic models.
Speaker 1: Wait, hold on, stochastic models sounds incredibly intimidating. It does
Speaker 1: if I'm understanding this. Stochastic just means it is factoring
Speaker 1: in randomness, right. How does a machine calculate randomness when
Speaker 1: making a business decision?
Speaker 2: It calculates randomness by relying on probability data from its
Speaker 2: knowledge base. Expected utility isn't just a flat score, It's
Speaker 2: a weighted prediction. Okay, Let's say Option A spinning up
Speaker 2: a server has a high utility score of eighty if
Speaker 2: it works, but historical data tells the stochastic model there's
Speaker 2: only a fifty percent chance the server actually spins up
Speaker 2: in time to prevent the.
Speaker 3: Crash, so the score gets cut in half.
Speaker 2: Essentially, yes, the expected utility is heavily downgraded. Meanwhile, Option
Speaker 2: B switching to text might only have a utility score
Speaker 2: of sixty, but it has a ninety nine percent chance
Speaker 2: of working instantly.
Speaker 1: Oh.
Speaker 2: I see, the system multiplies the value of the outcome
Speaker 2: by the probability of its success. It is playing out
Speaker 2: alternate futures, calculating the math of that randomness, and picking
Speaker 2: the path with the highest guaranteed yield. Wow. Only then
Speaker 2: does it dispatch the strategy executor to make the actual change.
Speaker 3: That is mind blowing.
Speaker 1: It's doing all of that math, playing out all those
Speaker 1: alternate futures in millisecondsp But you know, thinking about this logically,
Speaker 1: Rainbow is brilliant if you are building a modern, clean
Speaker 1: web architecture like ZNN dot com. But the real world
Speaker 1: is messy, very messy. Custom building a bespoke Rainbow framework
Speaker 1: with its own stitch language rules and highly specific gauges
Speaker 1: for every single app or corporate network in the world.
Speaker 1: That would be impossibly expensive and time consuming.
Speaker 2: It would be.
Speaker 1: So how does the industry move from these custom, bespoke
Speaker 1: fixes to something universal, like something plug and play?
Speaker 2: That is the holy grail of this field general purpose
Speaker 2: autonomic computing. Okay, the goal is to make self management
Speaker 2: as standardized as plugging in a USB cable. You don't
Speaker 2: want to build a new autootic manager every time you
Speaker 2: build a new app. The solution is to use a
Speaker 2: universal reconfigurable policy engine universal instead of hardcoding the manager
Speaker 2: to understand one specific system engineers feed this generic engine
Speaker 2: and XML schema.
Speaker 3: XML, meaning basically a standard document format.
Speaker 2: Yes, this XML document acts as a universal translator. It
Speaker 2: defines the system model, the resources, the components, and the
Speaker 2: properties in a standardized, structured format that the generic policy
Speaker 2: engine can instantly understand.
Speaker 1: So it's like handing the engine a standardized map exactly.
Speaker 1: But here's where it gets really interesting to me. What
Speaker 1: happens if the system is old? I mean, corporate infrastructure
Speaker 1: is full of twenty year old legacy databases and dinosaur
Speaker 1: servers that were built long before anyone coined the term
Speaker 1: autonomic computing. You can't just slap a universal policy engine
Speaker 1: over a legacy database and expect them to talk.
Speaker 2: You definitely cannot.
Speaker 1: So how does this generic engine actually control resources that
Speaker 1: were never designed to be self managing.
Speaker 2: It controls them through something called manageability adapters.
Speaker 3: Manageability adapters.
Speaker 2: Think of these adapters as diplomatic envoice or like real
Speaker 2: time translators. They wrap around the legacy IT resources and
Speaker 2: provide uniform sensor and effector interface.
Speaker 3: Okay, so a wrapper.
Speaker 2: Yeah, the legacy system has no idea. It is part
Speaker 2: of an autonomic loop. It's just doing its job. When
Speaker 2: the legacy system spits out a weird, outdated air log,
Speaker 2: the manageability adapter intercepts. It translates that data into the
Speaker 2: standard XML format and hands it to the generic policy engine.
Speaker 3: Oh that's close.
Speaker 2: And when the policy engine sends a command to change
Speaker 2: a configuration, the adapter translates that standardized command back into
Speaker 2: the legacy system's native twenty year old language.
Speaker 1: To really show the power of this universal engine, we
Speaker 1: have to talk about the Fujitsu disk drive example from
Speaker 1: the text. Yes, that's a great one, because this isn't
Speaker 1: a massive global news site. This is a piece of
Speaker 1: physical hardware, and it perfectly illustrates how versatile this universal
Speaker 1: XML approaches it really does.
Speaker 2: In this case, study researchers took that exact same generic
Speaker 2: policy engine and used it to manage a physical Fujitsu dis.
Speaker 3: Drive, the exact same engine, the exact same one.
Speaker 2: They fed the engine a highly complex mathematical model of
Speaker 2: the using a free, open source probabilistic model.
Speaker 3: Checker called prism prism right.
Speaker 2: Specifically, they used a continuous time markoff chain model.
Speaker 3: Okay, I have to.
Speaker 1: Stop you there, a continuous time markof chain. Explain that
Speaker 1: to me, because it sounds like we just jumped into
Speaker 1: a graduate level statistics class.
Speaker 2: It sounds worse than it is. A markoff chain is
Speaker 2: simply a mathematical system that transitions from one state to
Speaker 2: another where the probability of the next state depends only
Speaker 2: on the current state, not on the sequence of events
Speaker 2: that preceded it.
Speaker 3: Okay.
Speaker 2: In the case of the Fujitsu disk drive, the drive
Speaker 2: has different states. It can be busy reading data, it
Speaker 2: can be idle, or it can drop into a slate
Speaker 2: mode to save power.
Speaker 1: Okay, let me try an analogy go for it is
Speaker 1: a Markoff chain model of this disk drive, kind of
Speaker 1: like deciding what to do with your.
Speaker 3: Car at a long red light.
Speaker 2: Oh I like this.
Speaker 1: So you're currently in the idle state. Your engine is running,
Speaker 1: which is wasting gas. But if the light turns green,
Speaker 1: you can hit the gas and go inside. Now you
Speaker 1: could choose to turn the engine completely off, transitioning to
Speaker 1: the sleep state.
Speaker 3: That saves a ton of gas.
Speaker 1: But when the light turns green, it takes you like
Speaker 1: three seconds to restart the engine, which delays you and
Speaker 1: frustrates all the cars waiting behind you.
Speaker 2: That's a brilliant way to frame it. The disc drive
Speaker 2: faces that exact same dilemma. If it drops into sleep mode,
Speaker 2: it saves a massive amount of power, but waking up
Speaker 2: from sleep takes time, which delays incoming data requests and
Speaker 2: builds up a queue of frustrated users the cars behind you.
Speaker 3: Oh I see.
Speaker 2: So the universal policy engine uses that Markov chain model
Speaker 2: to dynamically calculate the exact, mathematically optimal probability for the
Speaker 2: drive to switch from idle to sleep at any given
Speaker 2: mill a second.
Speaker 1: So it's constantly monitoring how many cars are piling up
Speaker 1: behind it.
Speaker 2: Yes, it is constantly adjusting the probability of going to
Speaker 2: sleep based on the length of the request queue. It
Speaker 2: perfectly balances the utility of power savings against the utility
Speaker 2: of fast response times. It calculates the optimal moment to
Speaker 2: turn off the engine, so to speak.
Speaker 1: And what's amazing to me is that it does all
Speaker 1: of this using the exact same generic policy engine that
Speaker 1: could theoretically be managing ZNN dot COM's.
Speaker 2: Server pool exactly.
Speaker 1: The core logic monitor the queue, analyze the markof chain,
Speaker 1: plan the state transition, and execute the sleep command remains identical.
Speaker 1: The XML schema and the manageability adaptors just swap out
Speaker 1: the context.
Speaker 2: The engine doesn't care if it's managing a news website's
Speaker 2: bandwidth or a physical disk drives power consumption. You just
Speaker 2: change the blueprint, you handle the engine, and it optimizes
Speaker 2: whatever reality it finds itself in.
Speaker 1: So, bringing this back to why this matters to you
Speaker 1: the listener, have you ever hit refresh on a major
Speaker 1: news site during say, a chaotic election night, and the
Speaker 1: page hangs for just a split second before loading a
Speaker 1: slightly simpler, text heavy version of the site.
Speaker 2: I'm sure most people have.
Speaker 1: Or have you ever accessed a massive cloud database for
Speaker 1: work and noticed a momentary pause before the data just
Speaker 1: floods in perfectly, You probably just triggered.
Speaker 3: An autonomic loop.
Speaker 1: Almost certainly, there is a very good chance an autonomic
Speaker 1: manager is working silently in the background of your daily life.
Speaker 1: It's evaluating utility weights, reading architectural gauges, calculating stochastic probabilities,
Speaker 1: and literally reconfiguring the network's architecture in milliseconds so you
Speaker 1: never notice a severe hiccup.
Speaker 2: It truly is the invisible infrastructure of our modern digital lives.
Speaker 2: We demand perfection from our systems, and autonomic computing is
Speaker 2: the only way to deliver that at scale.
Speaker 1: So what does this all mean. It means we are
Speaker 1: fundamentally shifting from building static tools that humans have to
Speaker 1: constantly maintain and repair to building a digital infrastructure that
Speaker 1: actually cares for itself.
Speaker 2: And that leads to a rather profound implication that the
Speaker 2: source material hints at regarding the future of this technology.
Speaker 2: Oh yeah, the text mentions that future policy engines won't
Speaker 2: just follow the pre defined rules and mark off chains
Speaker 2: that human engineers give them. They will eventually include machine
Speaker 2: learning modules machine learning. Yes, these modules will automatically refine
Speaker 2: and derive the behavioral models that the systems they manage,
Speaker 2: based entirely on their own observation over time.
Speaker 1: Wait, meaning, the system will start writing its own XML schemas.
Speaker 1: It will invent its own rules.
Speaker 2: That is the logical next step. If these autonomic systems
Speaker 2: become truly capable of learning, rewriting their own adaptation strategies,
Speaker 2: and generating their own resource definition policies without any human input,
Speaker 2: they become entirely self sufficient.
Speaker 1: Wow. We've talked entirely today about systems that optimize for efficiency, budget,
Speaker 1: and speed. They prioritize survival and utility above all else. Right,
Speaker 1: But if these machine learning modules start writing their own rules,
Speaker 1: redefining their own architecture. What happens when a system inevitably
Speaker 1: decides that the human administrators are the biggest bottleneck to
Speaker 1: its efficiency?
Speaker 2: Slightly terrifying thought.
Speaker 1: If the machine's ultimate goal is optimization, at what point
Speaker 1: do we transition from being the managers of the system
Speaker 1: to being a liability it needs to route around. Something
Speaker 1: to think about the next time you hit refresh