Arbitrary program extension at run time in language-based VMs, e.g., Java's dynamic class loading, comes at a startup cost: high memory footprint and slow warmup.
Cloud computing amplifies the startup overhead.
Microservices and serverless cloud functions lead to small, self-contained applications that are started often.
Slow startup and high memory footprint directly affect the cloud hosting costs, and slow startup can also break service-level agreements.
Many applications are limited to a prescribed set of pre-tested classes, i.e., use a closed-world assumption at deployment time.
For such Java applications, GraalVM Native Image offers fast startup and stable performance.

GraalVM Native Image uses a novel iterative application of points-to analysis and heap snapshotting, followed by ahead-of-time compilation with an optimizing compiler.
Initialization code can run at build time, i.e., executables can be tailored to a particular application configuration.
Execution at run time starts with a pre-populated heap, leveraging copy-on-write memory sharing.
We show that this approach improves the startup performance by up to two orders of magnitude compared to the Java HotSpot VM, while preserving peak performance.
This allows Java applications to have a better startup performance than Go applications and the V8 JavaScript VM.