Within a Data Center (DC) environment network upgrades are often challenging and may consume significant amounts of time and network administrator resources. Additionally, DC networks tend to consume large amounts of energy and dissipate considerable amounts of heat that can be challenging to evacuate in densely populated fabrics. To address these challenges techniques are presented herein that support, possibly among other things, the construction of a network model; the use of Machine Learning (ML) to predict low and high load periods and, in low periods, determining the ratio of resources that may be taken offline; updating the equal-cost multi-path (ECMP) rules in the leaves to avoid selected planes so as to take the full plane spine and super-spine nodes offline; and upgrading one of the super-spine nodes and then one of the spine nodes to ensure that there is always a rollback path in case of a problem. If the upgrading is successful, the techniques may include proceeding to upgrade all of the super-spine nodes and all of the spine nodes.
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Sundaram, Ramakrishnan Chokkanathapuram and Thubert, Pascal, "MAINTENANCE AND POWER SAVINGS IN LARGE MULTIPLANE DATA CENTER FABRICS", Technical Disclosure Commons, (October 27, 2020)