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August 2010 Choice-memory tradeoff in allocations
Noga Alon, Ori Gurel-Gurevich, Eyal Lubetzky
Ann. Appl. Probab. 20(4): 1470-1511 (August 2010). DOI: 10.1214/09-AAP656


In the classical balls-and-bins paradigm, where n balls are placed independently and uniformly in n bins, typically the number of bins with at least two balls in them is Θ(n) and the maximum number of balls in a bin is Θ(log n / log log n). It is well known that when each round offers k independent uniform options for bins, it is possible to typically achieve a constant maximal load if and only if k = Ω(log n). Moreover, it is possible w.h.p. to avoid any collisions between n / 2 balls if k > log2n.

In this work, we extend this into the setting where only m bits of memory are available. We establish a tradeoff between the number of choices k and the memory m, dictated by the quantity km / n. Roughly put, we show that for km ≫ n one can achieve a constant maximal load, while for km ≪ n no substantial improvement can be gained over the case k = 1 (i.e., a random allocation).

For any k = Ω(log n) and m = Ω(log2n), one can achieve a constant load w.h.p. if km = Ω(n), yet the load is unbounded if km = o(n). Similarly, if km > Cn then n / 2 balls can be allocated without any collisions w.h.p., whereas for km < ɛn there are typically Ω(n) collisions. Furthermore, we show that the load is w.h.p. at least log(n/m) / (log k+log log(n / m)). In particular, for k ≤ polylog (n), if m = n1−δ the optimal maximal load is Θ(log n / log log n) (the same as in the case k = 1), while m = 2n suffices to ensure a constant load. Finally, we analyze nonadaptive allocation algorithms and give tight upper and lower bounds for their performance.


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Noga Alon. Ori Gurel-Gurevich. Eyal Lubetzky. "Choice-memory tradeoff in allocations." Ann. Appl. Probab. 20 (4) 1470 - 1511, August 2010.


Published: August 2010
First available in Project Euclid: 20 July 2010

zbMATH: 1205.60023
MathSciNet: MR2676945
Digital Object Identifier: 10.1214/09-AAP656

Primary: 60C05, 60G50, 68Q25

Rights: Copyright © 2010 Institute of Mathematical Statistics


Vol.20 • No. 4 • August 2010
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