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The Maths of Microscaling Liz Rice @lizrice | @microscaling
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What is Microscaling? Assumptions Some theory Some experiments
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What is Microscaling?
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Traffic spike
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Too much work Spare capacity
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container scaling work performance metrics
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work performance metrics container scaling VM autoscaling
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True for regular autoscaling too VMs take much longer to scale
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Orchestration Heterogenous services Cattle not pets
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Performance targets
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How many containers? Request processing time Rate of requests known? predictable?
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performance target actual performance error time t
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performance target p time t actual performance x e(t) = x(t) - p(t) e(t) → 0 error e
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x(t) is proportional to n(t) n(t) = k x(t) error e time t number of containers n
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x(t) is proportional to n(t) nope! error e time t number of containers n d(t) is proportional to e(t) d
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Time delays It’s a dynamical system
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Woah, the future! error e time t d(t) is proportional to e(t + T) T d
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No content
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Control theory!
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PID controller
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error e time t Proportional term d(t) = Kp e(t) The further we are below target the more containers we need
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error e time t Derivative term The faster we approach target the fewer containers we need d(t) = Kp e(t) + Kd ė(t)
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error e time t Integral term d(t) = Kp e(t) + Kd ė(t) + Ki e(t) Offset errors accumulated over time ∫
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Which values for K? Discrete containers?
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Simulator goo.gl/KAqT5y
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It works! But it’s non-trivial to tune
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Known behaviours Machine learning
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Container parameters = metadata microbadger.com
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github.com/microscaling @lizrice | @microscaling app.microscaling.com microbadger.com