Serverless computing is growing in popularity by virtue of its lightweight and simplicity of
management. It achieves these merits by reducing the granularity of the computing unit to …

The development of cloud infrastructures inspires the emergence of cloud-native computing.
As the most promising architecture for deploying microservices, serverless computing has …

Despite existing work in machine learning inference serving, ease-of-use and cost efficiency
remain challenges at large scales. Developers must manually search through thousands of …

To comply with the increasing number of government regulations about data placement and
processing, and to protect themselves against major cloud outages, many users want the …

In many public and private Cloud systems, users need to specify a limit for the amount of
resources (CPU cores and RAM) to provision for their workloads. A job that exceeds its limits …

Applying deep convolutional neural networks (NN) to video data at scale poses a substantial
systems challenge, as improving inference accuracy often requires a prohibitive cost in …

Deep learning workloads are common in today's production clusters due to the proliferation
of deep learning driven AI services (eg, speech recognition, machine translation). A deep …

Serverless computing is becoming increasingly popular, enabling users to quickly launch
thousands of shortlived tasks in the cloud with high elasticity and fine-grain billing. These …

Recently, machine learning has been used in every possible field to leverage its amazing
power. For a long time, the networking and distributed computing system is the key …

We present ByteScheduler, a generic communication scheduler for distributed DNN training
acceleration. ByteScheduler is based on our principled analysis that partitioning and …