Multiple Choice Allocations with Small Maximum Loads

In this thesis we investigate multiple choice allocations from a slightly different perspective. Instead of minimizing the maximum load, we fix the bin capacities and focus on maximizing the number of balls that can be allocated without overloading any bin. In the process that we consider we have $m=\lfloor cn \rfloor$ balls and $n$ bins. Each ball chooses $k$ bins independently and uniformly at random. \emph{Is it possible to assign each ball to one of its choices such that the no bin receives more than $\ell$ balls?} For all $k\ge 3$ and $\ell\ge 2$ we give a critical value, $c_{k,\ell}^*$, such that when $c<c_{k,\ell}^*$ an allocation is possible with high probability and when $c>c_{k,\ell}^*$ this is not the case.

In case such an allocation exists, \emph{how quickly can we find it?} Previous work on total allocation time for case $k\ge 3$ and $\ell=1$ has analyzed a \emph{breadth first strategy} which is shown to be linear only in expectation. We give a simple and efficient algorithm which we also call \emph{local search allocation} (LSA) to find an allocation for all $k\ge 3$ and $\ell=1$. Provided the number of balls are below (but arbitrarily close to) the theoretical achievable load threshold, we give a \emph{linear} bound for the total allocation time that holds with high probability.
We demonstrate, through simulations, an order of magnitude improvement for total and maximum allocation times when compared to the state of the art method.

Our results find applications in many areas including hashing, load balancing, data management, orientability of random hypergraphs and maximum matchings in a special class of bipartite graphs.