This paper explores the environmental impact of the super-linear growth trends for AI from a
holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the …

User-facing latency-sensitive web services include numerous distributed,
intercommunicating microservices that promise to simplify software development and …

Cloud applications are increasingly shifting from large monolithic services to complex
graphs of loosely-coupled microservices. Despite the advantages of modularity and …

FPGA has been an emerging computing infrastructure in datacenters benefiting from
features of fine-grained parallelism, energy efficiency, and reconfigurability. Meanwhile …

Datacenter-scale AI training clusters consisting of thousands of domain-specific accelerators
(DSA) are used to train increasingly-complex deep learning models. These clusters rely on a …

Workload autoscaling is widely used in public and private cloud systems to maintain stable
service performance and save resources. However, it remains challenging to set the optimal …

Data serialization is critical for many datacenter applications, but the memory copies
required to move application data into packets are costly. Recent zero-copy APIs expose …

Serialization frameworks are a fundamental component of scale-out systems, but introduce
significant compute overheads. However, they are amenable to acceleration with …

The global scale and challenging requirements of modern cloud applications have led to the
development of complex, widely distributed, service-oriented applications. One enabler of …

Cloud service providers conservatively provision excessive resources to ensure service
level objectives (SLOs) are met. They often set lower CPU utilization targets to ensure …