Machine learning, optimization and event stream management

This is a bit different post – it is not about storage per se, but actually it is.

Let me start a bit from the other side – I’ve read Netflix article about how they created their excellent recommendation service ( ), and it raised a question in my head – is there a way for system to heal itself if it can learn?

Well, machine learning is not quite a learning – it is an explicitly defined mathematical problem, but I wonder whether it can be applied to optimization problem.
And to simplify things, lets move away from storage to robots.

Consider a simple scenario – robot moves over the road to its final destination point. It has a number of sensors which tell its control system about speed, edge, current coordination, wheel state and so on. And there are 2 rotated wheels.

At some point robot’s right wheel moves into the hole. Robot starts turning over and in a few moments it will fall.
How to fix this problem?

There are numerous robot control algorithms which very roughly say ‘if sensor X says Y do Z’ and those parameters may vary and be controlled by the management optimization.
But what if we step away to more generic solution – what if we do not have such algorithms. But we can change wheel rotations and perform some other tasks which we do not know in advance how they affect current situation.

Solution to this problem allows to solve a storage problem too – if storage control system can write into multiple disks, which one to select so that read and write performance would be maximized, space is efficiently used and so on.

A bad solution uses heuristics – if disk A is slow at the moment, use disk B. If robot falls to the right, rotate the right wheel. And so on. It doesn’t really work in practice.

Another solution – a naive one – is to ‘try every possible solution’ – rotate the right wheel, if things are worse, stop it. Rotate the left wheel – check sensors, if situation changed – react accordingly cialis rezeptfrei holland. Combine all possible controls (at random or at some order), adjust each control’s ‘weight’, hopefully this will fix the things. But when feature space is very large, i.e. we can use many controls – this solution doesn’t scale.

So, what is a generic way to solve such a problem?
Or speaking a little bit more mathematician way – if we have a ‘satisfaction’ function on several variables (we move to given direction, all sensors are green), and suddenly something out of our control started happening, which changed satisfaction function badly (sensors became red), how to use our variables (wheels and other controls) to return things back to normal?