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Regarding the point that the software systems being more flexible. A nice thing about controllers such as PID is that they deal well with disturbances and chang

by anton_ 8y ago

Regarding the point that the software systems being more flexible. A nice thing about controllers such as PID is that they deal well with disturbances and changing environments. In this particular case the PID controller runs alongside the system with a fixed sampling rate. It means that it does not really care about dynamics of the system in control. Say we want to update the caching TTL and that affects how the system behaves. The PID algo only cares about the delta of the error over a fixed period of time (sampling rate) and will adjust accordingly. Also, as mirceal mentioned, there are autotuning algos if it gets really bad. Unfortunately it is a problem if the system changes too much the algo can stop working properly. But that could happen any time you make a major change in any complex system. To me it’s more of a problem of testing and sanity-checking, not of PID or other control algorithms. When you get to a certain point of complexity, testing all possible conditions becomes unrealistic and that might lead to bugs when a big part of the system gets rewritten. On the point that only a few engineers might know how it works. One of the purpose of the article is actually put an idea out there to make more engineers familiar with the concept and collect some feedback. :) I’ll update the comment with “On non-linearity of software systems” when I get a chance. This is actually a really good one and I have a few thoughts about it.

anton_ 8y ago
On non-linearity of software systems. Correct me if I’m wrong but I’m assuming you mean it in a sense of being probabilistic and not easily predictable. Which is an interesting point because a lot of modern algorithms are like that. Let’s take neural networks. Being able to explain what the model is doing and how to make sure it’s correct is a whole area of research. For example, the results of a convolutional net can change drastically by altering a single pixel in an image[1]. We also faced with a problem of explaining a machine learning model that was much simpler than a neural net. It is a global problem and unfortunately I don’t have any solution of the top of my head. [1] https://arxiv.org/abs/1710.08864 https://arxiv.org/abs/1710.08864

by anton_ 8y ago

anton_ 8y ago

On non-linearity of software systems. Correct me if I’m wrong but I’m assuming you mean it in a sense of being probabilistic and not easily predictable. Which is an interesting point because a lot of modern algorithms are like that. Let’s take neural networks. Being able to explain what the model is doing and how to make sure it’s correct is a whole area of research. For example, the results of a convolutional net can change drastically by altering a single pixel in an image[1]. We also faced with a problem of explaining a machine learning model that was much simpler than a neural net. It is a global problem and unfortunately I don’t have any solution of the top of my head. [1] https://arxiv.org/abs/1710.08864 https://arxiv.org/abs/1710.08864