Public cloud providers seek to meet stringent performance requirements and low hardware
cost. A key driver of performance and cost is main memory. Memory pooling promises to …

We report on experiences with Swift congestion control in Google datacenters. Swift targets
an end-to-end delay by using AIMD control, with pacing under extreme congestion. With …

The monolithic server model where a server is the unit of deployment, operation, and failure
is meeting its limits in the face of several recent hardware and application trends. To improve …

As memory requirements grow, and advances in memory technology slow, the availability of
sufficient main memory is increasingly the bottleneck in large compute clusters. One solution …

High-performance systems are increasingly bottlenecked by the growing energy and
communications costs of interconnecting numerous compute and memory resources …

We present FireSim, an open-source simulation platform that enables cycle-exact
microarchitectural simulation of large scale-out clusters by combining FPGA-accelerated …

This paper provides an overview of software-defined “hardware” infrastructures (SDHI).
SDHI builds upon the concept of hardware (HW) resource disaggregation. HW resource …

It is challenging for cloud-native relational databases to meet the ever-increasing needs of
scaling compute and memory resources independently and elastically. The recent …

With cloud providers constantly seeking the best infrastructure trade-off between
performance delivered to customers and overall energy/utilization efficiency of their data …

Disaggregated racks comprise a dense cluster of separate pools of compute, memory and
storage blades, all inter-connected through an internal network within a single rack …