Cloud inference systems have recently emerged as a solution to the ever-increasing
integration of AI-powered applications into the smart devices around us. The wide adoption …

The complex, distributed nature of data centers have spawned the adoption of distributed,
multi-tiered software architectures, consisting of many inter-connected microservices. These …

The energy efficiency of cloud computing has recently attracted a great deal of attention. As
a result of raised expectations, cloud providers such as Amazon and Microsoft have started …

Machine learning (ML) inference workloads present significantly different challenges than
ML training workloads. Typically, inference workloads are shorter running and under-utilize …

The rapid adoption of cloud storage and computing services led to unprecedented growth of
data centers in the world. As bulk energy consumers, large-scale data centers in the US rack …

With the growing energy demands from server farms, it becomes necessary to understand
the tradeoffs between energy consumption and application performance. Typically, server …

Energy proportionality of data center severs have improved drastically over the past decade
to the point where near ideal energy proportional servers are now common. These highly …

Modern data centers improve resource utilization with power oversubscription. The power
hierarchy in a data center is oversubscribed by installing more servers than allowed by the …

The huge energy consumption in data centers produces not only high electricity bill but also
tremendous carbon footprints. Although today's servers and data centers of leading internet …

With the rapid scaling of data centers, understanding their power characteristics and
optimizing data center energy consumption is a critical task. Typically, data centers are …