Serverless computing separates function execution from state management. Simple retry-
based fault tolerance might corrupt the shared state with duplicate updates. Existing …

Serverless computing is renowned for its computation elasticity, yet its full potential is often
constrained by the requirement for functions to operate within local and dedicated …

Serverless ML inference is an emerging cloud computing paradigm for low-cost, easy-to-
manage inference services. In serverless ML inference, each call is executed in a container; …

Well-known defenses exist to detect and mitigate common faults and memory safety
vulnerabilities in software. Yet, many of these mitigations do not address the challenge of …

Serverless computing allows developers to deploy and scale stateless functions in
ephemeral workers easily. As a result, serverless computing has been widely used for many …

The industry has embraced snapshotting to tackle the cold starts and efficiently manage
numerous short-lived functions for microservice-native architectures, serverless computing …

Modern data analytics pipelines are highly dynamic, as they are constantly monitored and
fine-tuned by both data engineers and scientists. Recent systems managing pipelines ease …

Serverless computing allows users to execute pieces of code (so called functions) on-
demand in the cloud without having to provision any hardware resources. However, by …

Model inference systems are essential for implementing end-to-end data analytics pipelines
that deliver the benefits of machine learning models to users. Existing cloud-based model …

Serverless computing is the most innovative and affordable cloud service model that
improves three strategic business objectives: flexibility, efficiency, and innovation …