Year: 2021
Type: preprint
Abstract: Resource-disaggregated data centres (RDDC) propose a resource-centric, and high-utilisation architecture for data centres (DC), avoiding resource fragmentation and enabling arbitrarily sized resource pools to be allocated to tasks, rather than server-sized ones. RDDCs typically impose greater demand on the network, requiring more infrastructure and increasing cost and power, so new resource allocation algorithms that co-manage both server and networks resources are essential to ensure that allocation is not bottlenecked by the network, and that requests can be served successfully with minimal networking resources. We apply reinforcement learning (RL) to this problem for the first time and show that an RL policy based on graph neural networks can learn resource allocation policies end-to-end that outperform previous hand-engineered heuristics by up to 22.0\%, 42.6\% and 22.6\% for acceptance ratio, CPU and memory utilisation respectively, maintain performance when scaled up to RDDC topologies with $10^2\times$ more nodes than those seen during training and can achieve comparable performance to the best baselines while using $5.3\times$ less network resources.
Source: arXiv (Cornell University)
Authors: Shabka, Zacharaya, Zervas, Georgios
Institution: University College London
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: DTP 2018-19 University College London, OptoCloud: Ultra-fast optically interconnected heterogeneous Data Centers, Photonically-synthesized Digital-to-Analogue Conversion, G453597767, Transforming networks - building an intelligent optical infrastructure (TRANSNET), G64284825, G7564478775, EPSRC Centre for Doctoral Training in Integrated Photonic and Electronic Systems
OpenAlex ID: W3213844784