arXiv subjects

Eva Loeser

Publications and source records attributed to Eva Loeser.

5 recordsLinked to original sources

arXiv2026

Fluid and Diffusion Approximation of a Multiclass Proportional Processor Sharing Queue with Reneging and General Distributions

In this paper, we resolve the long open problem of the diffusion approximation for a generally distributed processor sharing queue with reneging. In particular, we first extend the known fluid limit for a single class processor sharing queue with reneging to the multiclass proportional processor sharing queue with reneging. We prove uniqueness of fluid model solutions and convergence of fluid scaled models to fluid model solutions. Next, we show that, under diffusion-scaling, the models are tight, and that subsequential limits can be uniquely characterized by a certain system of SDEs driven by a Gaussian sheet on $\R_+^2$ with a nontrivial covariation structure.

math.PR↗

arXiv2026

Rank Based Routing in Large Server Systems under Extreme Congestion

We study $n$ parallel queues in an extreme heavy-traffic regime: each server works at rate $n$, while jobs arrive to a dispatcher at rate $n^2-(a-b)\sqrt{n}$, with fixed $a>b>0$. Arrivals are routed by a marginal join-the-shortest-queue policy: a small stream of rate $b\sqrt{n}$ joins the current shortest queue, while the remaining stream of rate $n^2-a\sqrt{n}$ is routed uniformly at random. This policy greatly reduces communication cost relative to full JSQ, while improving load balancing and offering a natural mechanism for premium jobs to join shorter queues. Under diffusive scaling, we prove limit theorems for the ranked queue lengths and associated gap process. The limit is an infinite-dimensional reflected Atlas process, with reflection at the origin and rank-based drift acting on the lowest particle. Its dynamics depend only on $b$, the shortest-queue arrival rate, while $a$ enters through the choice of invariant distribution. We prove well-posedness of this reflected infinite Atlas model and characterize a one-parameter family of product-form stationary gap distributions, parametrized by $a$ and $b$. To connect the diffusion limit with the stationary behavior of the queueing system, we introduce a related "system with pauses'' that agrees with the original dynamics at diffusion scale but admits an exact open Jackson network representation. This yields explicit finite-$n$ stationary gap distributions, whose heavy-traffic limits select the corresponding product-form invariant laws of the infinite reflected Atlas process. As consequences, we obtain sharp asymptotics for the lowest-ranked queues, system imbalance, and average queue length, quantifying the tradeoff between communication cost and load-balancing performance relative to random routing and full join-the-shortest-queue policies.

math.PR↗

arXiv2025

Diffusive Scaling limit of stochastic Box-Ball systems and PushTASEP

We introduce the Stochastic Box-Ball System (SBBS), a probabilistic cellular automaton that generalizes the classic Takahashi-Satsuma Box-Ball System. In SBBS, particles are transported by a carrier with a fixed capacity that may fail to pick up any given particle with a fixed probability $\epsilon$. This model interpolates between two known integrable systems: the Box-Ball System (as $\epsilon\rightarrow 0$) and the PushTASEP (as $\epsilon\rightarrow 1$). We show that the long-term behavior of SBBS is governed by isolated particles and the occasional emergence of short solitons, which can form longer solitons but are more likely to fall apart. More precisely, we first show that all particles are isolated except for a $1/\sqrt{n}$-fraction of times in any given $n$ steps, and solitons keep forming for this fraction of times. We then show that under diffusive scaling, both SBBS (for any carrier capacity) and PushTASEP converge weakly to semimartingale reflecting Brownian Motions (SRBMs) on the Weyl chamber with explicit covariance and reflection matrices, which are consistent with the microscale relations between these systems. The reflection matrix for SBBS is determined by how 2-solitons behave and exhibit ``solitonic bias'' visible in the diffusive scale. Our proof relies on a new, extended SRBM invariance principle that we develop in this work. This principle can handle processes with complex boundary behavior that can be written as "overdetermined" Skorokhod decompositions, which is crucial for analyzing the complex solitonic interaction in SBBS. We believe this tool may be of independent interest.

math.PR↗

arXiv2019

Quantifying Robotic Swarm Coverage

In the field of swarm robotics, the design and implementation of spatial density control laws has received much attention, with less emphasis being placed on performance evaluation. This work fills that gap by introducing an error metric that provides a quantitative measure of coverage for use with any control scheme. The proposed error metric is continuously sensitive to changes in the swarm distribution, unlike commonly used discretization methods. We analyze the theoretical and computational properties of the error metric and propose two benchmarks to which error metric values can be compared. The first uses the realizable extrema of the error metric to compute the relative error of an observed swarm distribution. We also show that the error metric extrema can be used to help choose the swarm size and effective radius of each robot required to achieve a desired level of coverage. The second benchmark compares the observed distribution of error metric values to the probability density function of the error metric when robot positions are randomly sampled from the target distribution. We demonstrate the utility of this benchmark in assessing the performance of stochastic control algorithms. We prove that the error metric obeys a central limit theorem, develop a streamlined method for performing computations, and place the standard statistical tests used here on a firm theoretical footing. We provide rigorous theoretical development, computational methodologies, numerical examples, and MATLAB code for both benchmarks.

eess.SY↗

arXiv2018

Quantitative Assessment of Robotic Swarm Coverage

This paper studies a generally applicable, sensitive, and intuitive error metric for the assessment of robotic swarm density controller performance. Inspired by vortex blob numerical methods, it overcomes the shortcomings of a common strategy based on discretization, and unifies other continuous notions of coverage. We present two benchmarks against which to compare the error metric value of a given swarm configuration: non-trivial bounds on the error metric, and the probability density function of the error metric when robot positions are sampled at random from the target swarm distribution. We give rigorous results that this probability density function of the error metric obeys a central limit theorem, allowing for more efficient numerical approximation. For both of these benchmarks, we present supporting theory, computation methodology, examples, and MATLAB implementation code.

math.OC↗