Finite-Time Queue Peak Laws in Stochastic Networks: Logarithmic Scaling After Geometric Thresholds
arXiv:2606.18218v1 Announce Type: cross Abstract: We study finite-horizon queue peaks in generalized switches, a standard stochastic-network model in which many queues share constrained service resources. Arrivals may be dependent, time-varying, and adapted to the past; the standing load condition is uniform interior slack, meaning the conditional mean arrival vector stays in a fixed contraction of the capacity region. We show that this slack reshapes the finite-time peak law for drift-minimizing scheduling policies such as MaxWeight. The square-root envelope that is sharp without slack persis
This research provides theoretical advancements in understanding the finite-time behavior of stochastic networks, which is crucial for optimizing system performance amidst increasing complexity in AI and distributed systems.
Improved models for queue peak laws can lead to more efficient resource allocation and scheduling in complex AI systems, data centers, and telecommunication networks, directly impacting performance and cost.
The understanding of how system slack reshapes finite-time peak laws allows for more robust and predictable design of distributed systems, moving beyond square-root envelopes without slack.
- · AI compute infrastructure providers
- · Cloud service providers
- · Researchers in queueing theory and stochastic networks
- · Developers of distributed systems
- · Systems relying on suboptimal scheduling policies
- · Inefficient data centers
More stable and predictable performance in large-scale AI and computing systems due to optimized resource scheduling.
Reduced operational costs and energy consumption in data centers through improved efficiency and lower peak-load demands.
Acceleration of research into more advanced, self-optimizing distributed AI architectures, potentially impacting the scalability of generative models.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG