Various research communities have independently arrived at stream processing as a
programming model for efficient and parallel computing. These communities include digital …

Datacenter applications demand microsecond-scale tail latencies and high request rates
from operating systems, and most applications handle loads that have high variance over …

The microservice architecture is a popular software engineering approach for building
flexible, large-scale online services. Serverless functions, or function as a service (FaaS) …

Clipper: A Low-Latency Online Prediction Serving System Page 1 This paper is included in the
Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation …

The conventional wisdom is that CPU resources such as cores, caches, and memory
bandwidth must be partitioned to achieve performance isolation between tasks. Both the …

We describe TensorFlow-Serving, a system to serve machine learning models inside
Google which is also available in the cloud and via open-source. It is extremely flexible in …

Traffic optimizations (TO, eg flow scheduling, load balancing) in datacenters are difficult
online decision-making problems. Previously, they are done with heuristics relying on …

Naiad is a distributed system for executing data parallel, cyclic dataflow programs. It offers
the high throughput of batch processors, the low latency of stream processors, and the ability …

Serving ML prediction pipelines spanning multiple models and hardware accelerators is a
key challenge in production machine learning. Optimally configuring these pipelines to meet …

Cassandra is a distributed storage system for managing very large amounts of structured
data spread out across many commodity servers, while providing highly available service …