Dynamic random-access memory (DRAM) is a key driver of performance and cost in public
cloud servers. At the same time, a significant amount of DRAM is underutilized due to …

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

Since emerging edge applications such as Internet of Things (IoT) analytics and augmented
reality have tight latency constraints, hardware AI accelerators have been recently proposed …

The heterogeneous nature of workloads running in cloud environments makes future
resource usage prediction a complicated problem. Virtual machines can be described in five …

Scheduling plays a pivotal role in cloud computing systems. Designing an efficient
scheduler is a challenging task. The challenge comes from several aspects, including the …

Many resource management problems require sequential decision-making under
uncertainty, where the only uncertainty affecting the decision outcomes are exogenous …

To improve resource utilization and thereby reduce costs, leading cloud infrastructure
operators such as Google and Alibaba co-locate transient batch jobs with long-running …

Prior work has used power capping to shave rare power peaks and add more servers to a
datacenter, thereby oversubscribing its resources and lowering capital costs. This works well …

Resource under-utilization is common in cloud data centers. Prior works have proposed
improving utilization by running provider workloads in the background, colocated with tenant …

Redy is a cloud service that provides high performance caches using RDMA-accessible
remote memory. An application can customize the performance of each cache with a service …