Sparse Mean Field Load Balancing in Large Localized Queueing Systems

arXiv:2312.12973v2 Announce Type: replace-cross
Abstract: Scalable load balancing algorithms are of great interest in cloud networks and data centers, necessitating the use of tractable techniques to compute optimal load balancing policies for good performance. However, most existing scalable techniques, especially asymptotically scaling methods based on mean field theory, have not been able to model large queueing networks with strong locality. Meanwhile, general multi-agent reinforcement learning techniques can be hard to scale and usually lack a theoretical foundation. In this work, …

abstract algorithms arxiv cloud compute cs.dc cs.lg cs.ni cs.sy data data centers eess.sy good however mean networks performance policies scalable scaling systems theory tractable type