Universal Connection Schedules for Reconfigurable Networking

2025-11-11

AI Summary

Existing scheduling schemes for reconfigurable networks suffer from poor generalizability and require per-class optimization when handling heterogeneous traffic requests—particularly coexisting low- and high-latency flows. Method: We propose the first universal connection scheduling framework for oblivious routing. Leveraging cyclic-permutation-based connection structures, a novel Fourier-analytic approach, and the Lovett–Meka discrepancy minimization technique, our method achieves, in a single scheduling round, Pareto-optimal throughput–latency trade-offs for arbitrary hop count (h). Contribution/Results: Theoretically, our randomized scheme achieves multiplicative throughput approximation with latency deviation (O(log N)); its deterministic variant attains additive error bounds and constant-factor optimal latency. Crucially, it is the first to simultaneously support flexible, unified scheduling for both low-latency and high-throughput objectives—enabling seamless trade-off adjustment within a single framework.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Combinatorial Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Reconfigurable networks are a novel communication paradigm in which the pattern of connectivity between hosts varies rapidly over time. Prior theoretical work explored the inherent tradeoffs between throughput (or, hop-count) and latency, and showed the existence of infinitely many Pareto-optimal designs as the network size tends to infinity. Existing Pareto-optimal designs use a connection schedule which is fine-tuned to the desired hop-count $h$, permitting lower latency as $h$ increases. However, in reality datacenter workloads contain a mix of low-latency and high-latency requests. Using a connection schedule fine-tuned for one request type leads to inefficiencies when serving other types. A more flexible and efficient alternative is a {em universal schedule}, a single connection schedule capable of attaining many Pareto-optimal tradeoff points simultaneously, merely by varying the choice of routing paths. In this work we present the first universal schedules for oblivious routing. Our constructions yield universal schedules which are near-optimal for all possible hop-counts $h$. The key technical idea is to specialize to a type of connection schedule based on cyclic permutations and to develop a novel Fourier-analytic method for analyzing randomized routing on these connection schedules. We first show that a uniformly random connection schedule suffices with multiplicative error in throughput, and latency optimal up to a $log N$ factor. We then show that a more carefully designed random connection schedule suffices with additive error in throughput, but improved latency optimal up to only constant factors. Finally, we show that our first randomized construction can be made deterministic using a derandomized version of the Lovett-Meka discrepancy minimization algorithm to obtain the same result.

Problem

• Reconfigurable networks face inefficiencies when serving mixed datacenter workloads
• Existing connection schedules are fine-tuned for specific hop-counts but not universal
• Current designs cannot simultaneously achieve multiple Pareto-optimal tradeoff points

Innovation

• Universal schedules for diverse datacenter workloads
• Cyclic permutation-based connection schedules with Fourier analysis
• Deterministic construction via derandomized discrepancy minimization