Balls into bins

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The balls-into-bins problem is a classic problem in probability theory that has many applications in computer science. The problem involves m balls and n boxes (or "bins"). Each time, a single ball is placed into one of the bins. After all balls are in the bins, we look at the number of balls in each bin; we call this number the load on the bin and ask: what is the maximum load on a single bin?

Obviously, it is possible to make the load as small as m/n by putting each ball into the least loaded bin. The interesting case is when the bin is selected at random, or at least partially at random.

Random allocation[edit]

When the bin for each ball is selected at random, independent of other choices, the maximum load might be as large as . However, it is possible to calculate a tighter bound that holds with high probability. A "high probability" is a probability , i.e. the probability tends to when grows to infinity.

For the case , with high probability the maximum load is: [1] [2]

.

Gonnet [3] gave a tight bound for the expected value of the maximum load, which for is , where is the inverse of the gamma function, and it is known that .

The maximum load can also be calculated for , and for example, for it is , and for it is , with high probability.[4]

Partially random allocation[edit]

Instead of just selecting a random bin for each ball, it is possible to select two or more bins for each ball and then put the ball in the least loaded bin. This is a compromise between a deterministic allocation, in which all bins are checked and the least loaded bin is selected, and a totally random allocation, in which a single bin is selected without checking other bins.

In this case, if one allocates balls into bins (with ) sequentially one by one, and for each ball one chooses random bins at each step and then allocates the ball into the least loaded of the selected bins (ties broken arbitrarily), then with high probability the maximum load is:[5]

which is exponentially less than with totally random allocation.

This result can be generalized to the case (with ), when with high probability the maximum load is:[6]

which is tight up to an additive constant. (All the bounds hold with probability at least for any constant c.)

Note that for , the random allocation process gives only the maximum load of with high probability, so the improvement between these two processes is especially visible for large values of .

Infinite stream of balls[edit]

Instead of just putting m balls, it is possible to consider an infinite process in which, at each time step, a single ball is added and a single ball is taken, such that the number of balls remains constant. For m=n, after a sufficiently long time, with high probability the maximum load is similar to the finite version, both with random allocation and with partially random allocation.[5]

Applications[edit]

Dynamic resource allocation: consider a set of n identical computers. There are n users who need computing services. The users are not coordinated - each users comes on his own and selects which computer to use. Each user would of course like to select the least loaded computer, but this requires to check the load on each computer, which might take a long time. Another option is to select a computer at random; this leads, with high probability, to a maximum load of approximately . A possible compromise is that the user will check only two computers, and use the least loaded of the two. This leads, with high probability, to a much smaller maximum load of approximately .

Hashing: consider a hash table in which all keys mapped to the same location are stored in a linked list. The efficiency of accessing a key depends on the length of its list. If we use a single hash function which selects locations with uniform probability, with high probability the longest chain has keys. A possible improvement is to use two hash functions, and put each new key in the shorter of the two lists. In this case, with high probability the longest chain has only elements.[7]

Proportional division.[8]

References[edit]

  1. ^ Kolchin, Valentin F. (1978). Random allocations. Washington: Winston [usw.] ISBN 978-0470993941. 
  2. ^ Kotz, Samuel; Johnson, Norman Lloyd (1977). Urn models and their applications. New York, NY: John Wiley & Sons. ISBN 978-0471446309. 
  3. ^ Gonnet, Gaston H. (1981). "Expected Length of the Longest Probe Sequence in Hash Code Searching". Journal of the Association for Computing Machinery. 28 (2): 289–304. doi:10.1145/322248.322254. 
  4. ^ Raab, Martin (1998). ""Balls into Bins" — A Simple and Tight Analysis". Lecture Notes in Computer Science: 159–170. doi:10.1007/3-540-49543-6_13. 
  5. ^ a b Azar, Yossi; Broder, Andrei Z.; Karlin, Anna R.; Upfal, Eli (1999). "Balanced Allocations". SIAM Journal on Computing. 29 (1): 180–200. doi:10.1137/s0097539795288490. 
  6. ^ Berenbrink, Petra; Czumaj, Artur; Steger, Angelika; Vöcking, Berthold (2006). "Balanced Allocations: The Heavily Loaded Case". SIAM Journal on Computing. 35 (6): 180–200. doi:10.1137/S009753970444435X. 
  7. ^ Karp, R. M. (1996). "Efficient PRAM simulation on a distributed memory machine". Algorithmica. 16 (4-5): 517–542. doi:10.1007/bf01940878. 
  8. ^ Jeff Edmonds and Kirk Pruhs (2006). "Balanced Allocations of Cake". 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06): 623. doi:10.1109/focs.2006.17. ISBN 0-7695-2720-5.