What happens to a complex system when it gets pushed just past its limits?
In some cases, the system in question is able to actually change its limits, so it can handle the new stressors that get thrown at it. When a system is pushed beyond its design limit, it has to change the way that it works. The system needs to adapt its own processes to work in a different way.
We use the term resilience to describe the ability of a system to adapt how it does its work, and this is what resilience engineering researchers study. These researchers have identified multiple factors that foster resilience. For example, people on the front lines of the system need autonomy to be able to change the way they work, and they also need to coordinate effectively with others in the system. A system under stress inevitably need access to additional resources, which means that there needs to be extra capacity that was held in reserve. People need to be able to anticipate trouble ahead, so that they can prepare to change how they work and deploy the extra capacity.
However, there are cases when systems fail to adapt effectively when pushed just beyond their limits. These systems face what Woods and Branlat call decompensation: they exhaust their ability to keep up with the demands placed on them, and their performance falls sharply off of a cliff. This behavior is the opposite of resilience, and researchers call it brittleness.
The ongoing problems facing Southwest Airlines provides us with a clear example of brittleness. External factors such as the large winter storm pushed the system past its limit, and it was not able to compensate effectively in the face of these stressors.
There are many reports coming out of the media now about different factors that contributed to Southwest’s brittleness. I think it’s too early to treat these as definitive. A proper investigation will likely take weeks if not months. When the investigation finally gets completed, I’m sure there will be additional factors identified that haven’t been reported on yet.
But one thing we can be sure of at this point is that Southwest Airlines fell over when pushed beyond its limits. It was brittle.
If you’re categorizing your incidents by cause, here are some options for causes that I’d love to see used. These are all taken directly from the field of cognitive systems engineering research.
Production pressure
All of us are so often working near saturation: we have more work to do than time to do it. As a consequence, we experience pressure to get that work done, and the pressure affects how we do our work and the decisions we make. Multi-tasking is a good example of a symptom of production pressure.
Ask yourself “for the people whose actions contributed to the incident, what was their personal workload like? How did it shape their actions?”
Goal conflicts
Often we’re trying to achieve multiple goals while doing our work. For example, you may have a goal to get some new feature out quickly (production pressure!), but you also have a goal to keep your system up and running as you make changes. This creates a goal conflict around how much time you should put into validation: the goal of delivering features quickly pushes you towards reducing validation time, and the goal of keeping the system up and running pushes you towards increasing validation time.
If someone asks “Why did you take action X when it clearly contravenes goal G?”, you should ask yourself “was there another important goal, G1, that this action was in support of?”
Workarounds
How do you feel about the quality of the software tools that you use in order to get your work done? (As an example: how are the deployment tools in your org?)
Often the tools that we use are inadequate in one way or another, and so we resort to workarounds: getting our work done in a way that works but is not the “right” way to do it (e.g., not how the tool was designed to be used, against the official process of how to do things). Using workarounds is often dangerous because the system wasn’t designed with that type of work in mind. But if the dangerous way of doing work is the only way that the work can get done, then you’re going to end up with people taking dangerous actions.
If an incident involves someone doing something they weren’t “supposed to”, you should ask yourself, “did they do it this way because they are working around some deficiency in the tools that have to use?”
Automation surprises
Software automation often behaves in ways that people don’t expect: we have incorrect mental models of why the system is doing what it is, often because the system isn’t designed in a way to make it easy for us to form good mental models of behavior. (As someone who works on a declarative deployment system, I acutely feel the pain we can inflict on our users in this area).
If someone took the “wrong” action when interacting with a software system in some way, ask yourself “what was their understanding of the state of the world at the time, and what was their understanding of what the result of that action would be? How did they form their understanding of the system behavior?”
Do you find this topic interesting? If so, I bet you’ll enjoy attending the upcoming Learning from Incidents Conference taking place on Feb 15-16, 2023 in Denver, CO.
My colleagues recently wrote a great post on the Netflix tech blog about a tough performance issue they wrestled with. They ultimately diagnosed the problem as false sharing, which is a performance problem that involves caching.
I’m going to take that post and write a simplified version of part of it here, as an exercise to help me understand what happened. After all, the best way to understand something is to try to explain it to someone else.
But note that the topic I’m writing about here is outside of my personal area of expertise, so caveat lector!
The problem: two bands of CPU performance
Here’s a graph from that post that illustrates the problem. It shows CPU utilization for different virtual machines instances (nodes) inside of a cluster. Note that all of the nodes are configured identically, including running the same application logic and taking the same traffic.
Note that there are two “bands”, a low band at around 15-20% CPU utilization, and a high band that varies a lot, from about 25%-90%.
Caching and multiple cores
Computer programs keep the data that they need in main memory. The problem with main memory is that accessing it is slow in computer time. According to this site, a CPU instruction cycle is about 400ps, and accessing main memory (DRAM access) is 50-100ns, which means it takes ~ 125 – 250 cycles. To improve performance, CPUs keep some of the memory in a faster, local cache.
There’s a tradeoff between the size of the cache and its speed, and so computer architects use a hierarchical cache design where they have multiple caches of different sizes and speeds. It was an interaction pattern with the fastest on-core cache (the L1 cache) that led to the problem described here, so that’s the cache we’ll focus on in this post.
If you’re a computer engineer designing a multi-core system where each core has on-core cache, your system has to implement a solution for the problem known as cache coherency.
Cache coherency
Imagine a multi-threaded program where each thread is running on a different core:
thread T1 runs on CPU 1
thread T2 runs on CPU 2
There’s a variable used by the program, which we’ll call x.
Let’s also assume that both threads have previously read x, so the memory associated with x is loaded in the caches of both. So the caches look like this:
Now imagine thread T1 modifies x, and then T2 reads x.
T1 T2
-- --
x = x + 1
if(x==0) {
// shouldn't execute this!
}
The problem is that T2’s local cache has become stale, and so it reads a value that is no longer valid.
The term cache coherency refers to the problem of ensuring that local caches in a multi-core (or, more generally, distributed) system stay in sync.
This problem is solved by a hardware device called a cache controller. The cache controller can detect when values in a cache have been modified on one core, and whether another core has cached the same data. In this case, the cache controller invalidates the stale cache. In the example above, the cache controller would invalidate the cache in T2. When T2 went to read the variable x, it would have to read the data from main memory into the core.
Cache coherency ensures that the behavior is correct, but every time a cache is invalidated and the same memory has to be retrieved from main memory again, it pays the performance penalty of reading from main memory.
The diagram above shows that the cache contains both the data as well as the addresses in main memory where the data comes from: we only need to invalidate caches that correspond to the same range of memory
Data gets brought into cache in chunks
Let’s say a program needs to read data from main memory. For example, let’s say it needs to read the variable named x. Let’s assume x is implemented as a 32-bit (4 byte) integer. When the CPU reads from main memory, the memory that holds the variable x will be brought into the cache.
But the CPU won’t just read the variable x into cache. It will read a contiguous chunk of memory that includes the variable x into cache. On x86 systems, the size of this chunk is 64 bytes. This means that accessing the 4 bytes that encodes the variable x actually ends up bringing 64 bytes along for the ride.
These chunks of memory stored in the cache are referred to as cache lines.
False sharing
We now almost have enough context to explain the failure mode. Here’s a C++ code snippet from the OpenJDK repository (from src/hotspot/share/oops/klass.hpp)
class Klass : public Metadata {
...
// Cache of last observed secondary supertype
Klass* _secondary_super_cache;
// Array of all secondary supertypes
Array<Klass*>* _secondary_supers;
This declares two pointer variables inside of the Klass class: _secondary_super_cache, and _secondary_supers. Because these two variables are declared one after the other, they will get laid out next to each other in memory.
The two variables are adjacent in main memory.
The _secondary_super_cache is, itself, a cache. It’s a very small cache, one that holds a single value. It’s used in a code path for dynamically checking if a particular Java class is a subtype of another class. This code path isn’t commonly used, but it does happen for programs that dynamically create classes at runtime.
Now imagine the following scenario:
There are two threads: T1 on CPU 1, T2 on CPU 2
T1 wants to write the _secondary_super_cache variable and already has the memory associated with the _secondary_super_cache variable loaded in its L1 cache
T2 wants to read from the _secondary_supers variable and already has the memory associated with the _secondary_supers variable loaded in its L1 cache.
When T1 (CPU 1) writes to _secondary_super_cache, if CPU 2 has the same block of memory loaded in its cache, then the cache controller will invalidate that cache line in CPU 2.
But if that cache line contained the _secondary_supers variable, then CPU 2 will have to reload that data from memory to do its read, which is slow.
ssc refers to _secondary_super_cache, ss refers to _secondary_supers
This phenomenon, where the cache controller invalidates cached non-stale data that a core needed to access, which just so happens to be on the same cache line as stale data, is called false sharing.
What’s the probability of false sharing in this scenario?
In this case, the two variables are both pointers. On this particular CPU architecture, pointers are 64-bits, or 8 bytes. The L1 cache line size is 64 bytes. That means a cache line can store 8 pointers. Or, put another away, a pointer can occupy one of 8 positions in the cache line.
There’s only one scenario where the two variables don’t end up on the same cache line: when _secondary_super_cache occupies position 8, and _secondary_supers occupies position 1. In all of the other scenarios, the two variables will occupy the same cache line, and hence will be vulnerable to false sharing.
1 / 8 = 12.5%, and that’s roughly the number of nodes that were observed in the low band in this scenario.
And now I recommend you take another look at the original blog post, which has a lot more details, including how they solved this problem, as well as a new problem that emerged once they fixed this one.
Critical digital services has yet to experience its “Three-Mile Island” event. Is such an accident necessary for the domain to take human performance seriously? Or can it translate what other domains have learned and make productive use of those lessons to inform how work is done and risk is anticipated for the future?
I don’t think the software world will ever experience such an event.
The effect of TMI
The Three Mile Island accident (TMI) is notable, not because of the immediate impact on human lives, but because of the profound effect it had on the field of safety science.
Before TMI, the prevailing theories of accidents was that they were because of issues like mechanical failures (e.g., bridge collapse, boiler explosion), unsafe operator practices, and mixing up physical controls (e.g., switch that lowers the landing gear looks similar to switch that lowers the flaps).
But TMI was different. It’s not that the operators were doing the wrong things, but rather that they did the right things based on their understanding of what was happening, but their understanding of what was happening, which was based on the information that they were getting from their instruments, didn’t match reality. As a result, the actions that they took contributed to the incident, even though they did what they were supposed to do. (For more on this, I recommend watching Richard Cook’s excellent lecture: It all started at TMI, 1979).
TMI led to a kind of Cambrian explosion of research into human error and its role in accidents. This is the beginning of the era where you see work from researchers such as Charles Perrow, Jens Rasmussen, James Reason, Don Norman, David Woods, and Erik Hollnagel.
Why there won’t be a software TMI
TMI was significant because it was an event that could not be explained using existing theories. I don’t think any such event will happen in a software system, because I think that every complex software system failure can be “explained”, even if the resulting explanation is lousy. No matter what the software failure looks like, someone will always be able to identify a “root cause”, and propose a solution (more automation, better procedures). I don’t think a complex software failure is capable of creating TMI style cognitive dissonance in our industry: we’re, unfortunately, too good at explaining away failures without making any changes to our priors.
As operators, when the system we operate is working properly, we use a functional description of the system to reason about its behavior.
Here’s an example, taken from my work on a delivery system. if somebody asks me, “Hey, Lorin, how do I configure my deployment so that a canary runs before it deploys to production?”, then I would tell them, “In your deliver config, add a canary constraint to the list of constraints associated with your production environment, and the delivery system will launch a canary and ensure it passes before promoting new versions to production.”
This type of description is functional; It’s the sort of verbiage you’d see in a functional spec. On the other hand, if an alert fires because the environment check rate has dropped precipitously, the first question I’m going to ask is, “did something deploy a code change?” I’m not thinking about function anymore, but I’m thinking of the lowest level of abstraction.
Rasmussen calls this model a means-ends hierarchy, where the “ends” are at the top (the function: what you want the system to do), and the “means” are at the bottom (how the system is physically realized). We describe the proper function of the system top-down, and when we successfully diagnose a problem with the system, we explain the problem bottom-up.
The abstraction hierarchy, explained with an example
The five levels of the abstraction hierarchy
To explain these, I’ll use the example of a car.
The functional purpose of the car is to get you from one place to another. But to make things simpler, let’s zoom in on the accelerator. The functional purpose of the accelerator is to make the car go faster.
The abstract functions include transferring power from the car’s power source to the wheels, as well as transferring information from the accelerator to that system about how much power should be delivered. You can think of abstract functions as being functions required to achieve the functional purpose.
The generalized functions are the generic functional building blocks you use to implement the abstract functions. In the case of the car, you need a power source, you need a mechanism for transforming the stored energy to mechanical energy, a mechanism for transferring the mechanical energy to the wheels.
The physical functions capture how the generalized function is physically implemented. In an electric vehicle, your mechanism for transforming stored energy to mechanical energy is an electric motor; in a traditional car, it’s an internal combustion engine.
The physical form captures the construction detail of how the physical function. For example, if it’s an electric vehicle that uses an electric motor, the physical form includes details such as where the motor is located in the car, what its dimensions are, and what materials it is made out of.
Applying the abstraction hierarchy to software
Although Rasmussen had physical systems in mind when he designed the hierarchy (his focus was on process control, and he worked at a lab that focused on nuclear power plants), I think the model can map onto software systems as well.
I’ll use the deployment system that I work on, Managed Delivery, as an example.
The functional purpose is to promote software releases through deployment environments, as specified by the service owner (e.g., first deploy to test environment, then run smoke tests, then deploy to staging, wait for manual judgment, then run a canary, etc.)
Here are some examples of abstract functions in our system.
There is an “environment check” control loop that evaluates whether each pending version of code is eligible for promotion to the next environment by checking its constraints.
There is a subsystem that listens for “new build” events and stores them in our database.
There is a “resource check” control loop that evaluates whether the currently deployed version matches the most recent eligible version.
For generalized functions, here are some larger scale building blocks we use:
a queue to consume build events generated by the CI system
a relational database to track the state of known versions
a workflow management system for executing the control loops
For the physical functions that realize the generalized functions:
SQS as our queue
MySQL Aurora as our relational database
Temporal as our workflow management system
For physical form, I would map these to:
source code representation (files and directory structure)
deployment representation (e.g., organization into clusters, geographical regions)
Consider: you don’t care about how your database is implemented, until you’re getting some sort of operational problem that involves the database, and then you really have to care about how it’s implemented to diagnose the problem.
Why is this useful?
If Rasmussen’s model is correct, then we should build operator interfaces that take the abstraction hierarchy into account. Rasmussen called this approach ecological interface design (EID), where the abstraction hierarchy is explicitly represented in the user interface, to enable operators to more easily navigate the hierarchy as they do their troubleshooting work.
I have yet to see an operator interface that does this well in my domain. One of the challenges is that you can’t rely solely on off-the-shelf observability tooling, because you need to have a model of the functional purpose and the abstract functions to build those models explicitly into your interface. This means that what we really need are toolkits so that organizations can build custom interfaces that can capture those top levels well. In addition, we’re generally lousy at building interfaces that traverse different levels: at best we have links from one system to another. I think the “single pane of glass” marketing suggests that people have some basic understanding of the problem (moving between different systems is jarring), but they haven’t actually figured out how to effectively move between levels in the same system.
The individual contributor feels production pressure from their manager.
The manager feels production pressure from their director.
The director feels production pressure from their vice president.
The vice president feels production pressure from their C-level executive.
The C-level executive feels production pressure from the CEO.
The CEO feels production pressure from the board of directors.
The board of directors feels production pressure from investment funds, who are the major shareholders.
And what about the managers of these investment funds? They feel production pressure to provide good returns so that customers will continue to invest. Many of their customers happen to hold shares of the fund in their retirement accounts. Customers such as … the individual contributor.
And the circle of production pressure is complete.
In the ALERRT report on the Uvalde shooting, the term reasonable appears four times (emphasis mine):
A reasonable officer would conclude in this case, based upon the totality of the circumstances, that use of deadly force was warranted.
ALERRT report, p13
The suspect was actively firing his weapon when the officers entered the building, and a reasonable officer would assume that there were injured people in the classrooms.
ALERRT report, p16
A reasonable officer would have considered this an active situation and devised a plan to address the suspect.
ALERRT report, p17
During each of these instances, the situation had gone active, and the immediate action plan should have been triggered because it was reasonable to believe that people were being killed.
ALERRT report, p18
The implication here is that the responses of the officers who responded were unreasonable, because they did not conform to what the ALERRT staff considered to be reasonable.
Labeling the responders actions as unreasonable enables us to explain away the failures in the law enforcement response as deficiencies with the individual responders. I suspect a law enforcement officer in another city reading the ALERRT report would conclude “this type of thing would never happen to my department, because we know what we’re doing. It’s these bozos in Udvale that were the problem here.”
Once we identify the problem as being the individual responders, we don’t have to dig any deeper to understand what happened. There’s nothing to learn, because we’ve explained the failure away. It was due to incompetence!
The problem with this type of assessment on the behavior of the responders is that it makes it more difficult to learn from the incident, an effect that Cook and Woods call distancing through differencing. They describe a case study of a chemical processing company where there was a chemical fire that happened in a foreign processing plant (emphasis mine).
Interestingly, the relevant people at the plant knew all about the previous incident as soon as it had occurred through more informal communication channels. They had reviewed the incident, noted many features that were different from their plant (non-US location, slightly different model of the same machine, different safety systems to contain fires). The safety people consciously classified the incident as irrelevant to the local setting, and they did not initiate any broader review of hazards in the local plant. Overall they decided the incident “couldn’t happen here.” … But these local workers regarded the overseas fire not as evidence of a type of hazard that existed in the local workplace but rather as evidence that workers at the other plant were not as skilled, as motivated and as careful as they were, after all, they were not Americans (the other plant was in a first world country). The consequence of this view was that no broader implications of the fire overseas were extracted locally after that event.
Cook & Woods, Distancing Through Differencing
Later on, there was a chemical fire at an American facility. There were similar systemic failures in both fires, but the Americans had not learned the lessons of the systemic failures from the foreign fire. Ironically, the same pattern of distancing through differencing was observed after the second fire (emphasis mine):
Interestingly (and ominously) this distancing through differencing that occurred in response to the external, overseas fire, was repeated internally after the local fire. Workers in the same plant, working in the same area in which the fire occurred but on a different shift, attributed the fire to lower skills of the workers on the other shift. They regarded the workers to whom the accident happened as inattentive and unskilled. Not surprisingly, this meant that they saw the fire as largely irrelevant to their own work. After all, their reasoning went, the fire occurred because the workers to whom it happened were less careful than we are. Despite their beliefs, there was no evidence whatsoever that there were significant differences between workers on different shifts or in different countries (in fact, there was evidence that one of the workers involved was among the better skilled at this plant).
Cook & Woods, Distancing Through Differencing
If we want to learn as much as we can from an incident, we have to fight the urge to diagnose an incident as due to the incompetence of individuals involved. We need to assume that the incident happened even though everyone involved was acting reasonably. Only then will we be able to see the systemic problems with clarity.
Here’s an excerpt from the Uvalde shooting interim report about one of the first officers on the scene at the Uvalde shooting. Based on the timeline, I’d guess that the event described below occurred around 11:32 AM, just after the suspect fired shots outside of the school, but before he entered the school.
One of those officers testified to the Committee that, based on the sound of echoes, he believed the shooter had fired in their direction. That officer saw children dressed in bright colors in the playground, all running away. Then, at a distance exceeding 100 yards, he saw a person dressed in black, also running away. Thinking that the person dressed in black was the attacker, he raised his rifle and asked Sgt. Coronado for permission to shoot. Sgt. Coronado testified he heard the request, and he hesitated. He knew there were children present. He considered the risk of shooting a child, and he quickly recalled his training that officers are responsible for every round that goes downrange.
Interim report, p42
Should the officer have fired? Here’s the ALERRT report’s assessment (emphasis mine)
Third, a Uvalde PD officer reported that he was at the crash site and observed the suspect carrying a rifle prior to the suspect entering the west hall exterior door. The UPD officer was armed with a rifle and sighted in to shoot the attacker; however, he asked his supervisor for permission to shoot. The UPD officer did not hear a response and turned to get confirmation from his supervisor. When he turned back to address the suspect, the suspect had already entered the west hall exterior door at 11:33:00. The officer was justified in using deadly force to stop the attacker. Texas Penal Code § 9.32, DEADLY FORCE IN DEFENSE OF PERSON states, an individual is justified in using deadly force when the individual reasonably believes the deadly force is immediately necessary to prevent the commission of murder (amongst other crimes). In this instance, the UPD officer would have heard gunshots and/or reports of gunshots and observed an individual approaching the school building armed with a rifle. A reasonable officer would conclude in this case, based upon the totality of the circumstances, that use of deadly force was warranted. Furthermore, the UPD officer was approximately 148 yards from the west hall exterior door. One-hundred and forty-eight yards is well within the effective range of an AR-15 platform. The officer did comment that he was concerned that if he missed his shot, the rounds could have penetrated the school and injured students. We also note that current State of Texas standards for patrol rifle qualifications do not require officers to fire their rifles from more than 100 yards away from the target. It is, therefore, possible that the officer had never fired his rifle at a target that was that far away. Ultimately, the decision to use deadly force always lies with the officer who will use the force. If the officer was not confident that he could both hit his target and of his backdrop if he missed, he should not have fired.
… had the UPD officer engaged the suspect with his rifle, he may have been able to neutralize, or at least distract, the suspect preventing him from entering the building.
ALERRT report, pp13–14
In hindsight, it sounds like that officer made the wrong call. If he had acted, perhaps he could have stopped the attacker from entering the school and slaughtering children.
If you’re thinking “if it was me in place of that officer, I would have taken the shot”, then, congratulations, you would have killed an innocent man (emphasis mine):
The officers testified to the Committee that it turned out that the person they had seen dressed in black was not the attacker, but instead it was Robb Elementary Coach Abraham Gonzales. … In a subsequent DPS interview, the officer in question described the person he saw not as “the shooter” but as “a person in black toward the back of the school, but kids were behind that individual.” DPS interview (June 13, 2022). These DPS interview reports do not include or support the detail suggested in the ALERRT report that a Uvalde police officer “observed the suspect carrying a rifle outside the west hall entry.” Based on its review of evidence to date, this Committee concludes that it is more likely that the officer saw Coach Gonzales dressed in black near a group of schoolchildren than that there was an actual opportunity to shoot the attacker from over 100 yards away, as assumed by ALERRT’s partial report.
Interim report, p43
This is yet another reminder that incident responders are faced with making time-pressured risk trade-offs under uncertainty. There are risks associated with both action and inaction, you don’t have enough information to make a fully informed decision, and you can’t take an arbitrary amount of time to make a decision, because the situation can change rapidly.
If you want to make sense of how responders behave in a situation like Uvalde, you need to understand what it an incident looks like from the inside.
According to the interim report on the elementary school shooting in Uvalde, there were 376(!) law enforcement officers that responded. How do we make sense of the fact it took such a long to neutralize the shooter? One way is to see the problem as what the researchers Klein, Feltovich, and Woods refer to as a fundamental common ground breakdown.
The term “common ground” refers to a kind of shared understanding between people who are coordinating in some way, so that the participants can predict each other’s future actions. To take a simple example, if you and I are playing a board game, “whose turn is next” would be part of the common ground.
For a group of people to coordinate, they need to maintain common ground, shared understanding of the situation and what actions they expect others to take.
Because incidents are dynamic and the respondents have access to different information, a common risk during incidents is the erosion of common ground. One particular risk is what Klein et al refer to as confusion over who knows what.
During the shooting at Uvalde, many of the officers believed that Chief Arrodondo was the incident commander:
The general consensus of witnesses interviewed by the Committee was that officers on the scene either assumed that Chief Arredondo was in charge, or that they could not tell that anybody was in charge of a scene described by several witnesses as “chaos” or a “cluster.”
Interim report, p62
But Arredondo saw himself in the role of a responder trying to resolve the situation directly, not as an incident commander working on coordination.
[W]hile you’re in there, you don’t title yourself … .I know our policy states you’re the incident commander. My approach and thought was responding as a police officer. And so I didn’t title myself. But once I got in there and we took that fire, back then, I realized, we need some things. We’ve got to get in that door. We need an extraction tool. We need those keys. As far as … I’m talking about the command part … the people that went in, there was a big group of them outside that door. I have no idea who they were and how they walked in or anything. I kind of – I wasn’t given that direction. *** you can always hope and pray that there’s an incident command post outside. I just didn’t have access to that. I didn’t know anything about that.
Interim report, p63
Here’s another example of how common ground got eroded: different understandings of the situation based on whether you were inside or outside.
Also, the misinformation reported to officers on the outside likely prevented some of them from taking a more assertive role. For example, many officers were told to stay out of the building because Chief Arredondo was inside a room with the attacker actively negotiating.
Interim report, p63
Another challenge that Klein et al discuss is communication problems, which we also see in Uvalde. In a previous post, I wrote about how Chief Arredondo believed that the shooter was barricaded alone in the room. There was evidence to the contrary, but that information didn’t reach him during the incident.
There was a series of phone calls with a student inside Room 112, initiated by the student calling 911 at 12:03 p.m. Radio traffic communicated to those officers who could hear it the fact that a student had called from within the classroom. Several witnesses indicated that they were aware of this, but not Chief Arredondo.
Interim report, p62
In particular, the police radios didn’t work properly inside of the school, a fact that is mentioned multiple times in the report (emphasis mine)
An effective incident commander located away from the drama unfolding inside the building would have realized that radios were mostly ineffective, and that responders needed other lines of communication to communicate important information like the victims’ phone calls from inside the classrooms.
Interim report, p8
Uvalde CISD police officers commonly carried two radios: one for the school district, and another “police radio” which transmitted communications from various local law enforcement agencies. While the school district radios tended to work reliably, the police radios worked more intermittently depending on where they were used.
Interim report, p14
Upon entering the building, the officers tried but were unable to communicate on their radios.
Interim report, p51
As mentioned in the narratives above, there were important events happening outside the north and south ends of the west building. In part due to the difficulty of maintaining radio communications within the building, not everybody inside the building received all of this information.
Interim report, p62
Radio communication was ineffective, so something else was needed for decisionmakers to receive critical information, such as the fact that victims had called from inside the rooms with the attacker.
Interim report, p64
Fundamental common ground breakdown is an ever-present danger whenever we coordinate, and it’s especially dangerous during incidents. The shooting in Uvalde is a painful reminder of this failure mode.
Surfing Complexity 
