Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1902.05870

Formal Foundations of Serverless Computing

Abhinav Jangda·Donald Pinckney·Yuriy Brun·Arjun Guha
View original source ↗Read source document ↗Export metadata ↓

Abstract

Serverless computing (also known as functions as a service) is a new cloud computing abstraction that makes it easier to write robust, large-scale web services. In serverless computing, programmers write what are called serverless functions, and the cloud platform transparently manages the operating system, resource allocation, load-balancing, and fault tolerance. When demand for the service spikes, the platform automatically allocates additional hardware to the service and manages load-balancing; when demand falls, the platform silently deallocates idle resources; and when the platform detects a failure, it transparently retries affected requests. In 2014, Amazon Web Services introduced the first serverless platform, AWS Lambda, and similar abstractions are now available on all major cloud computing platforms. Unfortunately, the serverless computing abstraction exposes several low-level operational details that make it hard for programmers to write and reason about their code. This paper sheds light on this problem by presenting $λ_Λ$, an operational semantics of the essence of serverless computing. Despite being a small (half a page) core calculus, $λ_Λ$ models all the low-level details that serverless functions can observe. To show that $λ_Λ$ is useful, we present three applications. First, to ease reasoning about code, we present a simplified naive semantics of serverless execution and precisely characterize when the naive semantics and $λ_Λ$ coincide. Second, we augment $λ_Λ$ with a key-value store to allow reasoning about stateful serverless functions. Third, since a handful of serverless platforms support serverless function composition, we show how to extend $λ_Λ$ with a composition language. We have implemented this composition language and show that it outperforms prior work.

Explore related subjects

cs.PL

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Abhinav Jangda, Donald Pinckney, Yuriy Brun, Arjun Guha. 2020-10-04. Formal Foundations of Serverless Computing. https://doi.org/10.1145/3360575

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Opportunistic ZGC: Leveraging Idle Cores for More Effective Concurrent Garbage Collection

Managed language runtimes often provide concurrent garbage collectors so that latency-critical applications with large working sets can keep running while most collection work proceeds in the background. ZGC is a production-quality, generational, concurrent collector in OpenJDK with sub-millisecond pause times. While ZGC is designed to run concurrently, frequent and excessive collections with ZGC can still slow the mutators due to synchronization costs and interference with shared computing resources. Hence, the ZGC scheduler is conservative by default, and in most cases, will grow the heap toward the maximum allowed before scheduling a collection. While this approach minimizes collection effort, it can be wasteful, or even harmful, if the maximum heap size is not well tuned to the actual working set. We propose Opportunistic ZGC (OppZGC), a feedback-directed ZGC scheduling policy that constrains the heap dynamically and automatically, without per-application tuning. OppZGC identifies periods when CPU cores are underutilized and leverages them for concurrent collection with ZGC. We describe the design and implementation of OppZGC in OpenJDK's HotSpot Java VM and evaluate it with standard and latency-sensitive benchmarks from DaCapo Chopin and SPECjbb. OppZGC limits heap usage when there is CPU capacity sufficient for additional collections, and avoids scheduling extra collections when they would substantially degrade performance. Overall, it reduces maximum heap usage for our DaCapo benchmarks by between 61% and 90%, on average, depending on configuration, with minimal impact on throughput and request latency compared to default ZGC.

cs.PL↗
arXiv2026

From Rocq to Metal: A Pipeline for Formally Verified Microcontroller Firmware

Enforcing invariants in safety-critical firmware is increasingly urgent as generated code becomes widespread, but standard extraction targets for proof assistants require runtimes too large for many embedded devices. We present a pipeline for running formally verified Rocq firmware logic on Cortex-M microcontrollers. The pipeline extracts Gallina to Scheme, compiles it with Encore!, a bare-metal Continuation Passing Style (CPS) bytecode virtual machine, and embeds the result in no_std Rust firmware. We structure applications as pure state-transition functions, so the business logic is proved in Rocq while the event/effect boundary, host callbacks, compiler, and VM remain explicit trusted components. On ST33-class targets with a 50 KB RAM lower bound, Encore! executes Rocq-extracted code end-to-end, stays within the target memory budget on our benchmarks, and validates a transaction-signing application on physical Ledger Flex hardware.

cs.PL↗
arXiv2026

Practical Range Refinement Types with Inference

Refinement types are a static verification technique that aims at increasing the expressivity of traditional type systems while remaining easy and natural to use. While systems based on refinement types have been developed for several mainstream languages, their practical adoption remains limited by their annotation overhead, which is often a more significant burden than when using the "plain" type annotations of languages like Java or Scala. To improve the state of the art, this paper introduces Ranger: a refinement type system designed to keep the annotation overhead small and to seamlessly integrate with imperative-style constructs like variables and loops. As the name suggests, Ranger focuses on integer range types: a particular kind of refinement types that express bounded integer ranges. Such types are widely useful to verify correct index manipulation and in-bounds data accesses, among others. To combine expressiveness and succinctness, Ranger is based on a bidirectional type system, which runs a type inference algorithm to provide the typechecking pass with information useful to reduce the need for user-written auxiliary annotations. Ranger also integrates other forms of lightweight flow-sensitive static analysis techniques that precisely capture the program's behavior without explicit annotations. We implemented Ranger on top of the Licorne experimental programming language. Our experiments show that Ranger's implementation can concisely express and verify a variety of useful properties that fall beyond the capabilities of standard static type systems like those of Java and Scala, and that Ranger compares favorably to other extended type systems, such as the Java Checker Framework and Liquid Java, that can also check properties about ranges.

cs.PL↗