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UID:910@lincs.fr
DTSTART;TZID=Europe/Paris:20250618T140000
DTEND;TZID=Europe/Paris:20250618T150000
DTSTAMP:20250701T152827Z
URL:https://www.lincs.fr/events/geometric-lower-bounds-for-stochastic-proc
 essing-networks-with-limited-connectivity/
SUMMARY:Geometric lower bounds for stochastic processing networks with
 limited connectivity
DESCRIPTION:Abstract:\n\nWe consider processing networks where multiple
 dispatchers are connected to single-server queues by a bipartite
 compatibility graph\, modeling constraints that are common in distributed
 data center networks\, typical in cloud systems\, due to geographic reasons
 or data locality issues. We prove lower bounds for the steady-state
 occupancy\, i.e.\, the complementary cumulative distribution of the
 empirical queue length measure. Our lower bounds are geometric\, with
 ratios determined by two flexibility metrics: the average degree of the
 dispatchers and a novel metric that averages the minimum degree over the
 compatible dispatchers across the servers. Using these lower bounds\, we
 establish that the large scale asymptotic performance of a processing
 network cannot match that of the classic Power-of-d or JSQ policies unless
 these flexibility metrics approach infinity in this large scale
 limit.\n\nPreprint: https://arxiv.org/abs/2505.08974\n\nSpeaker: Andres
 Ferragut\, https://aferragu.github.io
CATEGORIES:Seminars,Youtube
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DTSTART:20250330T030000
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