Traffic optimizations (TO, eg flow scheduling, load balancing) in datacenters are difficult
online decision-making problems. Previously, they are done with heuristics relying on …

Data centers are widely used for big data analytics, which often involve data-parallel jobs,
including query and web service. Meanwhile, cluster frameworks are rapidly developed for …

Machine learning (ML) has become the de-facto approach for various scientific domains
such as computer vision and natural language processing. Despite recent breakthroughs …

We present Sincronia, a near-optimal network design for coflows that can be implemented
on top on any transport layer (for flows) that supports priority scheduling. Sincronia achieves …

The past few years have witnessed a wide deployment of software defined networks
facilitating a separation of the control plane from the forwarding plane. However, the work on …

This paper argues that fair-sharing, the holy grail of congestion control algorithms for
decades, is not necessarily a desirable property in Machine Learning (ML) training clusters …

Recent research has proposed several packet, flow, and coflow scheduling methods that
could substantially improve data center network performance. Most of this work assumes …

To improve the processing efficiency of jobs in distributed computing, the concept of coflow
is proposed. A coflow is a collection of flows that are semantically correlated in a multi-stage …

Most existing data center network (DCN) flow scheduling solutions aim to minimize flow
completion times (FCT). However, these solutions either require precise flow information …

In Machine Learning (ML) system research, efficient resource scheduling and utilization
have always been an important topic given the compute-intensive nature of ML applications …