Power of Two Choices - Load Balancing Algorithm

I just saw a nice talk from a good friend of mine about load balancing techniques, you can take a look here: https://www.infoq.com/presentations/load-balancing. One of the interesting approaches mentioned in the talk is the "Power of Two Choices" algorithm, which has been explained in detailed here: http://www.eecs.harvard.edu/~michaelm/postscripts/tpds2001.pdf. It is amazing for its simplicity and effectiveness: a simple variation of random choice, but instead of selecting a bin (server) randomly, the idea becomes the following:

  • Pick two bins (servers) randomly, call them "A" and "B"
  • If "A" is under less load (define load as you wish), then select "A" as your target
  • Otherwise, "B"
The simplicity is striking, to a point that it is even questionable whether or not it works. But comparing the approach with a simple random approach you can definitely see that the distribution of the servers load balanced becomes much more "balanced", with a much smaller standard deviation. The code down below implements both concepts - take a look at the distribution results:


Random
Bin[0]: 8.46075433231396%
Bin[1]: 11.7227319062181%
Bin[2]: 10.1936799184506%
Bin[3]: 10.1936799184506%
Bin[4]: 7.95107033639144%
Bin[5]: 11.2130479102956%
Bin[6]: 10.6014271151886%
Bin[7]: 10.2956167176351%
Bin[8]: 9.98980632008155%
Bin[9]: 9.37818552497452%

Power of Two Choices
Bin[0]: 10.0917431192661%
Bin[1]: 9.98980632008155%
Bin[2]: 9.98980632008155%
Bin[3]: 9.88786952089704%
Bin[4]: 9.98980632008155%
Bin[5]: 9.98980632008155%
Bin[6]: 10.0917431192661%
Bin[7]: 9.88786952089704%
Bin[8]: 10.0917431192661%
Bin[9]: 9.98980632008155%

Fascinating! C# code below, cheers my friends!!! Marcelo

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;

namespace PowerOf2LoadBalancer
{
    class Program
    {
        static void Main(string[] args)
        {
            int[] bins = new int[10];
            int nRequests = 981;
            LoadBalancerRandom(nRequests, bins);

            bins = new int[10];
            LoadBalancerPowerOf2(nRequests, bins);
        }

        static void LoadBalancerRandom(int nRequests, int[] bins)
        {
            Random rd = new Random();

            for (int i = 0; i < nRequests; i++)
                bins[rd.Next(0, bins.Length)]++;

            PrintBinsPercentage(bins);
        }

        static void LoadBalancerPowerOf2(int nRequests, int[] bins)
        {
            Random rd = new Random();

            for (int i = 0; i < nRequests; i++)
            {
                int a = rd.Next(0, bins.Length);
                int b = rd.Next(0, bins.Length);
                bins[bins[a] < bins[b] ? a : b]++;
            }

            PrintBinsPercentage(bins);
        }

        static void PrintBinsPercentage(int[] bins)
        {
            int total = 0;
            for (int i = 0; i < bins.Length; i++) total += bins[i];
            for (int i = 0; i < bins.Length; i++) Console.WriteLine("Bin[{0}]: {1}%", i, (bins[i] * 100.0) / total);
            Console.WriteLine();
        }
    }
}

Comments

  1. Love it! It's just mind-blowing how easy this algorithm is, but how much better it performs than random bin selection - the variance is very low practically for free :) Thanks for the write-up!

    ReplyDelete
  2. "free" is incorrect, it depends on the `.length` "operation", in this case it's O(1) since it's just reading a variable; but it might not always be the case, say if the bin is an unbalanced tree structure then the worse case is O(n).

    ReplyDelete

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