Load balancing under $d$-thinning

TL;DR

This paper studies a new load balancing model called $d$-thinning, where an overseer selectively accepts bins for each ball, and finds the optimal maximum load after many balls are allocated, revealing a new asymptotic behavior.

Contribution

It introduces the $d$-thinning model and determines the asymptotic maximum load achievable, highlighting differences from the classical $d$-choice model.

Findings

Maximum load after $ heta(n)$ balls is $(d+o(1)) oot{d}{rac{d ext{log} n}{ ext{loglog} n}}$ with high probability.

The $d$-thinning model's load is significantly larger than the $d$-choice setting, which has a maximum load of $rac{ ext{loglog} n}{ ext{log} d}+O(1)$.

The results provide insights into the efficiency of load balancing under constrained acceptance policies.

Abstract

In the classical balls-and-bins model, $m$ balls are allocated into $n$ bins one by one uniformly at random. In this note, we consider the $d$-thinning variant of this model, in which the process is regulated in an on-line fashion as follows. For each ball, after a random bin has been selected, an overseer may decide, based on all previous history, whether to accept this bin or not. However, one of every $d$ consecutive suggested bins must be accepted. The maximum load of this setting is the number of balls in the most loaded bin. We show that after $\Theta(n)$ balls have been allocated, the least maximum load achievable with high probability is $(d+o(1))\sqrt[d]{\frac{d\log n}{\log\log n}}$. This should be compared with the related $d$-choice setting, in which the optimal maximum load achievable with high probability is $\frac{\log\log n}{\log d}+O(1)$.