Datacenters provide cost-effective and flexible access to scalable compute and storage
resources necessary for today's cloud computing needs. A typical datacenter is made up of …

Deep learning workloads are common in today's production clusters due to the proliferation
of deep learning driven AI services (eg, speech recognition, machine translation). A deep …

Modern GPU datacenters are critical for delivering Deep Learning (DL) models and services
in both the research community and industry. When operating a datacenter, optimization of …

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 …

Modern resource management frameworks for largescale analytics leave unresolved the
problematic tension between high cluster utilization and job's performance predictability …

In this article, we study online deadline-aware task dispatching and scheduling in edge
computing. We jointly considerthe management of the networking and computing resources …

Leveraging application-level requirements using coflows has recently been shown to
improve application-level communication performance in data-parallel clusters. However …

Cloud platforms are increasing their emphasis on sustainability and reducing their
operational carbon footprint. A common approach for reducing carbon emissions is to exploit …

Large data centers are currently the mainstream infrastructures for big data processing. As
one of the most fundamental tasks in these environments, the efficient execution of …

We propose repair pipelining, a technique that speeds up the repair performance in general
erasure-coded storage. By carefully scheduling the repair of failed data in small-size units …