search

stevana / elastically-scalable-thread-pools

An experiment in controlling the size of a thread pool using a PID controller.
MIT License
116 stars 0 forks source link
elastic pid-controller scalable thread-pool

readme

Elastically scalable thread pools

An experiment in controlling the size of a thread pool using a PID controller.

Motivation

A tried and tested way to achieve parallelism is to use pipelining. It's used extensively in manufacturing and in computer hardware.

For example, Airbus apparently outputs two airplanes per day on average, even though it takes two months to build a single airplane from start to finish. It's also used inside CPUs to pipeline instructions.

Let's imagine we want to take advantage of pipelining in some software system. To make things more concrete, let's say we have a system where some kind of requests come on over the network and we want to process them in some way. The first stage of the pipeline is to parse the incoming requests from raw bytestrings into some more structured data, the second stage is to apply some validation logic to the parsed data and the third stage is to process the valid data and produce some outputs that are then sent back to the client or stored somewhere.

The service time of an item can differ from stage to stage, for example parsing might be slower than validation, which can create bottlenecks. Luckily it's quite easy to spot bottlenecks by merely observing the queue lengths and once a slow stage is found we can often fix it by merely adding an additional parallel processor to that stage. For example we could spin up two or more threads that take bytestrings from the first queue and turn them into structured data and thereby compensate for parsing being slow.

By spinning up more threads we can decrease latency (waiting time in the queue) and increase throughput (process more items), but we are also on the other hand using more energy and potentially hogging CPU resources that might be better used elsewhere in the pipeline or system at large.

So here's the question that the rest of this post is concerned about: can we dynamically spin up and spin down threads at a stage in response to the input queue length for that stage?

Plan

Let's focus on a single stage of the pipeline to make things easier for ourselves.

We'd like to increase the parallelism of the processors if the input queue grows, and decrease it when the queue shrinks. One simple strategy might be to establish thresholds, i.e. if there's over $100$ items in the input queue then allocate more processors and if there's no items in the queue then deallocate them.

Since allocating and deallocating processors can be an expense in itself, we'd like to avoid changing them processor count unnecessarily.

The threshold based approach is sensitive to unnecessarily changing the count if the arrival rate of work fluctuates. The reason for this is because it only takes the present queue length into account.

We can do better by also incorporating the past and trying to predict the future, this is the basic idea of PID controllers from control theory.

Here's what the picture looks like with a PID controller in the loop:

                                            +----------------------------------+
                                            |                                  |
    ------------------------------------------->[Input queue]-->[Worker pool]----->[Output queue]-->
                                            |                                  |
     r(t)   e(t)                    u(t)    |                                  |
    ----->+------>[PID controller]--------> |                                  |
          ^                                 |                                  |
          |                                 +----------------------------------+
          |                                                 | y(t)
          +-------------------------------------------------+

The PID controller monitors the queue length $y(t)$, compares it to some desired queue length $r(t)$ (also known as the setpoint) and calculates the error $e(t)$. The error determines the control variable $u(t)$ which is used to grow or shrink the processor pool.

Pseudo-code

Let's start top-down with the main function which drives our whole experiment.

Main

main =

  // Create the in- and out-queues.
  inQueue  := newQueue()
  outQueue := newQueue()

  // The workers don't do anything interesting, they merely sleep for a bit to
  // pretend to be doing some work.
  worker := sleep 0.025s

  // Create an empty worker pool.
  pool := newPool(worker, inQueue, outQueue)

  // Start the PID controller in a background thread. The parameters provided
  // here allow us to tune the PID controller, we'll come back to them later.
  kp := 1
  ki := 0.05
  kd := 0.05
  dt := 0.01s
  fork(pidController(kp, ki, kd, dt, pool))

  // Create a workload for our workers. We use the sine function to create
  // between 0 and 40 work items every 0.1s for 60s. The idea being that because
  // the workload varies over time the PID controller will have some work to do
  // figuring out how many workers are needed.
  sineLoadGenerator(inQueue, 40, 0.1s, 60s)

Worker pool

The worker pool itself is merely a struct which packs up the necessary data we need to be able to scale it up and down.

struct Pool =
  { inQueue:  Queue<Input>
  , outQueue: Queue<Output>
  , worker:   Function<Input, Output>
  , pids:     List<ProcessId>
  }

Creating a newPool creates the struct with an empty list of process ids.

newPool worker inQueue outQueue = Pool { ..., pids: emptyList }

Scaling up and down are functions that take and return a Pool.

scaleUp pool =
  work := forever
            x := readQueue(pool.inQueue)
            y := pool.worker(x)
            writeQueue(pool.outQueue, y)
  pid   := fork(work)
  pool' := pool.pids = append(pid, pool.pids)
  return pool'

The function scaleDown does the inverse, i.e. kills and removes the last process id from pool.pids.

Load generator

In order to create work load that varies over time we use the sine function. The sine function oscillates between $-1$ and $1$:

We would like to have it oscillate between $0$ and some max value $m$. By multiplying the output of the sine function by $m/2$ we get an oscillation between $-m/2$ and $m/2$, we can then add $m/2$ to make it oscillate between $0$ and $m$.

We'll sample the resulting function once every timesStep seconds, this gives us the amount of work items (n) to create we then spread those out evenly in time, rinse and repeat until we reach some endTime.

sineLoadGenerator inQueue workItem maxItems timeStep endTime =
  for t := 0; t < endtime; t += timeStep
    n := sin(t) * maxItems / 2 + maxItems / 2
    for i := 0; i < n; i++
      writeQueue(inQueue, workItem)
      sleep(timeStep / n)

PID controller

The PID controller implementation follows the pseudo-code given at Wikipedia:

previous_error := 0
integral := 0
loop:
   error := setpoint − measured_value
   proportional := error;
   integral := integral + error × dt
   derivative := (error − previous_error) / dt
   output := Kp × proportional + Ki × integral + Kd × derivative
   previous_error := error
   wait(dt)
   goto loop

Where Kp, Ki and Kd is respectively the proportional, integral and derivative gain and dt is the loop interval time. The proportional part acts on the present error value, the integral acts on the past and the derivative tries to predict the future. The measured value is the input queue length and the setpoint, i.e. desired queue length, is set to zero. If the output of the PID controller is less than $-100$ (i.e. the queue length is over $100$ taking the present, past and possible future into account) then we scale up and if it's more than $-20$ (i.e. the queue length is less than $20$) then we scale down the worker pool.

How it works

We start off by only setting the proportional part and keeping the integral and derivative part zero, this is called a P-controller. We see below that it will scale the worker count up and down proportionally to the sine wave shaped load:

A P-controller only focuses on the present, and we see that it allocates and deallocates workers unnecessarily. In order to smooth things out we introduce the integral part, i.e. a PI-controller. The integral part takes the past into account. We see now that the worker count stabilises at $28$:

We can improve on this by adding the derivative part which takes the future into account. We then see that it stabilises at $26$ workers:

With the full PID controller, which stabilises using less workers than the PI-controller, we see that the queue length spikes up to $20$ or so each time the work load generator hits one of the sine function's peaks. Recall that we started scaling down once the queue length was less than $20$.

Usage

The above graphs were generated by running: cabal run app -- kp ki kd, where the $K_p$, $K_i$, and $K_d$ parameters are the tuning parameters for the PID controller.

If you don't have the GHC Haskell compiler and the cabal build tool already installed, then the easiest way to get it is via ghcup. Alternatively if you got nix then nix-shell should give give you access to all the dependencies you need.

Contributing

There are many ways we can build upon this experiment, here are a few ideas:

If any of this sounds interesting, feel free to get in touch!

See also

Discussion