Balanced allocation on dynamic hypergraphs
Catherine Greenhill,Bernard Mans,Ali Pourmiri +2 more
- 01 Aug 2020
- pp 1-22
TL;DR: In this article, it was shown that for a dynamic hypergraph with pair visibility at most (n − 1 − varepsilon > 0, and some mild additional conditions hold, with high probability the process has maximum load O(log_d\log n) + O(1), where n is the number of rounds in which a pair of bins appears within a (hyper)edge.
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Abstract: The {balls-into-bins model} randomly allocates $n$ sequential balls into $n$ bins, as follows: each ball selects a set $D$ of $d\ge 2$ bins, independently and uniformly at random, then the ball is allocated to a least-loaded bin from $D$ (ties broken randomly). The \emph{maximum load} is the maximum number of balls in any bin. {In 1999, Azar et al.}\ showed that provided ties are broken randomly, after $n$ balls have been placed the \emph{maximum load}, is ${\log_d\log n}+O(1)$, with high probability. We consider this popular paradigm in a dynamic environment where the bins are structured as a \emph{dynamic hypergraph}. A dynamic hypergraph is a sequence of hypergraphs, say $\mathcal{H}^{(t)}$, arriving over discrete times $t=1,2,\ldots$, such that the vertex set of $\mathcal{H}^{(t)}$'s is the set of $n$ bins, but (hyper)edges may change over time. In our model, the $t$-th ball chooses an edge from $\mathcal{H}^{(t)}$ uniformly at random, and then chooses a set $D$ of $d\ge 2$ random bins from the selected edge. The ball is allocated to a least-loaded bin from $D$, with ties broken randomly. We quantify the dynamicity of the model by introducing the notion of \emph{pair visibility}, which measures the number of rounds in which a pair of bins appears within a (hyper)edge. We prove that if, for some $\varepsilon>0$, a dynamic hypergraph has pair visibility at most $n^{1-\varepsilon}$, and some mild additional conditions hold, then with high probability the process has maximum load $O(\log_d\log n)$. Our proof is based on a variation of the witness tree technique, which is of independent interest. The model can also be seen as an adversarial model where an adversary decides the structure of the possible sets of $d$ bins available to each ball.
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Citations
Balanced Allocations with Heterogeneous Bins: The Power of Memory
01 Jan 2023
TL;DR: In this paper , the authors consider the memory process where instead of two choices, we only sample one bin per step but we have access to a cache which can store the location of one bin.
On the Analysis and Evaluation of Proximity-based Load-balancing Policies
Nitish K. Panigrahy,Thirupathaiah Vasantam,Prithwish Basu,Don Towsley,Ananthram Swami,Kin K. Leung +5 more
TL;DR: A Spatial Power of two (sPOT) policy in which each job is allocated to the least loaded server among its two geographically nearest servers is designed and experimental results suggest the efficacy of sPOT with respect to expected implementation cost.
3
Balanced Allocations with the Choice of Noise
Dimitrios Los,Thomas Sauerwald +1 more
TL;DR: The Two-Choice allocation process with noisy load comparisons results in a gap of \(\mathcal {O}(g+\log n)\) with high probability, where g is the adversary's power and n is the number of bins.
An Improved Drift Theorem for Balanced Allocations
Dimitrios Los,Thomas Sauerwald +1 more
TL;DR: This paper improves the drift theorem for balanced allocations, achieving tighter gap bounds, resolving open problems, and extending applicability to new processes and settings, including weighted graphical allocations and varying ball allocations.
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