Data center network operators often need accurate estimates of aggregate network
performance. Unfortunately, existing methods for estimating aggregate network statistics are …

Recently, researchers have proposed ML-driven traffic engineering (TE) schemes where a
neural network model is used to produce TE decisions in lieu of conventional optimization …

In today's virtualized cloud, containers and virtual machines (VMs) are prevailing methods to
deploy applications with different tenant requirements. However, these requirements are at …

We propose a performance analysis tool for learning-enabled systems that allows operators
to uncover potential performance issues before deploying DNNs in their systems. The tools …

Resource allocation is fundamental for cloud systems to ensure efficient resource sharing
among tenants. However, the scale of such optimization problems has outgrown the …

Resource management is ubiquitous in networked systems and ensures the effective
sharing of resources among various demands. Examples include traffic engineering, cluster …

Recently, researchers have proposed ML-driven traffic engineering (TE) schemes where a
neural network model is used to produce TE decisions in lieu of conventional optimization …

Production systems use heuristics because they are faster or scale better than their optimal
counterparts. Yet, practitioners are often unaware of the performance gap between a …