Electric power systems and the companies and customers that interact with them are
experiencing increasing levels of uncertainty due to factors such as renewable energy …

The recently proposed dataplanes for microsecond scale applications, such as IX and
ZygOS, use non-preemptive policies to schedule requests to cores. For the many real-world …

Inter-coflow scheduling improves application-level communication performance in data-
parallel clusters. However, existing efficient schedulers require a priori coflow information …

Production datacenters operate under various uncertainties such as traffic dynamics,
topology asymmetry, and failures. Therefore, datacenter load balancing schemes must be …

Caching refers to the act of replicating information at a faster (or closer) medium with the
purpose of improving performance. This deceptively simple idea has given rise to some of …

Runtime performance variability has been a major issue, hindering predictable and scalable
performance in modern distributed systems. Executing requests or jobs redundantly over …

Learning the tail behavior of a distribution is a notoriously difficult problem. By definition, the
number of samples from the tail is small, and deep generative models, such as normalizing …

With the explosive growth of the Internet of Things (IoT), an increasing amount of sensor
data generated by soft real-time IoT applications has been moved to data centers for storage …

Flow completion times (FCTs) are critical for many cloud applications. To minimize the
average FCT, recent transport designs, such as pFabric, PASE, and PIAS, approximate the …

The vector autoregressive (VAR) models provide a significant tool for multivariate time series
analysis. Owing to the mathematical simplicity, existing works on VAR modeling are rigidly …