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新论文 · v1arXiv:2611.06601

AtomicFaaS: Offloading Side-Effect Compensation to the Serverless Platform

Jonas Heisterberg, Sebastian Koch, Trever Schirmer, David Bermbach

原文摘要

Function-as-a-Service (FaaS) simplifies distributed application development by offloading operational concerns to the platform, but developers remain responsible for recovering from failures. Retries require idempotent functions, which means that developers have to write complex, error-prone code that undoes or skips the already completed operations of a failed function. To address this, we propose AtomicFaaS, a platform extension that provides semantic atomicity for the external side effects of a function invocation, whether the function fails or its instance crashes, without developer-written compensation code. AtomicFaaS intercepts outbound calls and records their effects in a fault-tolerant log, from which the platform compensates leaked effects outside the failed instance. Compared with a manually implemented saga, AtomicFaaS eliminates compensation code and leaves no compensatable effects after any tested failure, while adding ~1 ms per intercepted call. Under load, it lowers the sustainable throughput of CPU-bound functions, while its latency overhead becomes negligible once calls to external services dominate the invocation time.

cs.DC

新论文 · v1arXiv:2611.01255

AdapBench: Adaptive Early-Stopping and Prioritization for Efficient Microbenchmarking in Cloud-based CI/CD

Sebastian Koch, Jonas Heisterberg, Nils Japke, Trever Schirmer, David Bermbach

原文摘要

Cloud-based software systems frequently release new versions, a fact further accelerated through agentic coding. Each new release, however, brings the risk of performance regressions which can irritate end users, increase infrastructure costs, and increase carbon footprints. Therefore, developers strive to catch such regressions in testing stages, usually through so-called continuous benchmarking. The standard way of doing this is using microbenchmark suites which, however, take notoriously long to execute. Optimizing the execution of microbenchmark suites is, hence, an active field of research. In this paper, we propose AdapBench, an online scheduling system for reducing the execution duration of microbenchmark suites. AdapBench is the first approach which dynamically makes at-runtime decisions for prioritizing the most informative benchmarks and early-stopping each one once enough evidence has accumulated to accept or reject a performance change hypothesis. We evaluate our prototype on eight popular Java projects, commonly used by microbenchmark research, and four additional Go projects. At matched 90% change detections and false-positive rate compared to an optimistic uniform run, AdapBench saves 42% of the aggregate execution duration on the Java projects, with negligible scheduling overhead (below 0.001% of the total runtime). On the Go projects, AdapBench finds every performance change with zero false positives while saving 85% of the full run.

cs.SE · cs.DC · cs.PF