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arxiv: 2108.03221 · v1 · pith:AAVQ353Y · submitted 2021-08-06 · cs.NI

FloMore: Meeting bandwidth requirements of flows

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classification cs.NI
keywords flomorefailureservicesbandwidthrequirementsschemesstatescritical
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Wide-area cloud provider networks must support the bandwidth requirements of diverse services (e.g., applications, product groups, customers) despite failures. Existing traffic engineering (TE) schemes operate at much coarser granularity than services, which we show necessitates unduly conservative decisions. To tackle this, we present FloMore, which directly considers the bandwidth needs of individual services and ensures they are met a desired percentage of time. Rather than meet the requirements for all services over the same set of failure states, FloMore exploits a key opportunity that each service could meet its bandwidth requirements over a different set of failure states. FloMore consists of an offline phase that identifies the critical failure states of each service, and on failure allocates traffic in a manner that prioritizes those services for which that failure state is critical. We present a novel decomposition scheme to handle FloMore's offline phase in a tractable manner. Our evaluations show that FloMore outperforms state-of-the-art TE schemes including SMORE and Teavar, and also out-performs extensions of these schemes that we devise. The results also show FloMore's decomposition approach allows it to scale well to larger network topologies.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. NeuroRisk: Physics-Informed Neural Optimization for Risk-Aware Traffic Engineering

    cs.NI 2026-05 unverdicted novelty 7.0

    NeuroRisk is a physics-informed deep unrolled optimizer for risk-aware traffic engineering that achieves small optimality gaps and 100-100000x speedup over solvers while outperforming neural baselines on throughput.