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 applications, such as computer vision, video processing, and HTML generation. However, we find that the stateless nature of serverless computing wastes many of the important benefits modern language runtimes have to offer. A notable example is the extensive profiling and Just-in-Time (JIT) compilation effort that runtimes implement to achieve acceptable performance of popular high-level languages, such as Java, JavaScript, and Python. Unfortunately, when modern language runtimes are naively adopted in serverless computing, all of these efforts are lost upon worker eviction. Checkpoint-restore methods alleviate the problem by resuming workers from snapshots taken after initialization. However, production-grade language runtimes can take up to thousands of invocations to fully optimize a single function, thus rendering naive checkpoint-restore policies ineffective.

Underrepresented students face many significant challenges in their education. In particular, they often have a harder time than their peers from majority groups in building long-term high-quality study groups. This challenge is exacerbated in remote-learning scenarios, where students are unable to meet face-to-face and must rely on pre-existing networks for social support.

The serverless computing model leverages high-level languages, such as JavaScript and Java, to raise the level of abstraction for cloud programming. However, today's design of serverless computing platforms based on stateless short-lived functions leads to missed opportunities for modern runtimes to optimize serverless functions through techniques such as JIT compilation and code profiling.