Randomized algorithms
Parallel randomized load balancing
Random Structures & Algorithms
SIAM Journal on Computing
Balanced allocations: the heavily loaded case
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
The Power of Two Choices in Randomized Load Balancing
IEEE Transactions on Parallel and Distributed Systems
Balls and bins models with feedback
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
How Asymmetry Helps Load Balancing
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Load balancing and density dependent jump Markov processes
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
The power of two choices in randomized load balancing
The power of two choices in randomized load balancing
From balls and bins to points and vertices
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
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This paper studies the maximum load in the approximatedd-choice balls-and-bins game where the current load of each bin is available only approximately In the model of this game, we have r thresholds T1,...,Tr(0T1Tr) for an integer r (≥ 1) For each ball, we select d bins and put the ball into the bin of the lowest range, i.e., the bin of load i such that Tk ≤ i ≤ Tk+1–1 and no other selected bin has height less than Tk If there are two or more bins in the lowest range (i.e., their height is between Tk and Tk+1–1), then we assume that those bins cannot be distinguished and so one of them is selected uniformly at random We then estimate the maximum load for n balls and n bins in this game In particular, when we put the r thresholds at a regular interval of an appropriate Δ, i.e., Tr−Tr−1=...T2−T1=T1=Δ, the maximum load L(r) is given as $(r+O(1))\sqrt[r+1]{\frac{r+1}{(d-1)^{r}}{\rm ln}{\it n}/{\rm ln}(\frac{r+1}{(d-1)^{r}}{\rm ln}{\it n})}$ The bound is also described as L(Δ) ≤ {(1 + o(1))ln ln n + O(1)}Δ/ln ((d – 1)Δ) using parameter Δ Thus, if Δ is a constant, this bound matches the (tight) bound in the original d-choice model given by Azar et al., within a constant factor The bound is also tight within a constant factor when r = 1.