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 …

Model parallelism is conventionally viewed as a method to scale a single large deep
learning model beyond the memory limits of a single device. In this paper, we demonstrate …

Loosely-coupled and light-weight microservices running in containers are replacing
monolithic applications gradually. Understanding the characteristics of microservices is …

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 …

Functions as a Service (also called serverless computing) promises to revolutionize how
applications use cloud resources. However, functions suffer from cold-start problems due to …

Serverless computing promises auto-scalability and cost-efficiency (in" pay-as-you-go"
manner) for high-productive software development. Because of its virtue, serverless …

Serverless computing has seen rapid adoption due to its high scalability and flexible, pay-as-
you-go billing model. In serverless, developers structure their services as a collection of …

Serverless computing, an emerging computing model, relies on" warming up" functions prior
to its anticipated execution for faster and cost-effective service to users. Unfortunately …

The conference papers and full proceedings are available to registered attendees now and
will be available to everyone beginning Wednesday, July 14, 2021. Paper abstracts and …