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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

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02990

Same Function, Different CPU: Quantifying Hardware Heterogeneity in Commercial FaaS Platforms

Jonas Heisterberg, Natalie Carl, Trever Schirmer, David Bermbach

原文摘要

Serverless computing, particularly Function-as-a-Service (FaaS), abstracts operational concerns such as scaling, scheduling, and resource management from users. This abstraction, however, conceals a heterogeneous hardware fleet on which function execution environments are scheduled behind a unified function interface. Consequently, otherwise identical invocations may run on different CPU types that differ in performance, a hardware lottery that users can neither see nor control. Since the resulting variability is entangled with other sources, such as noisy neighbors or diurnal effects, its contribution to overall performance variability and how this contribution differs across providers remain unclear. In this paper, we systematically study hardware heterogeneity and its impact on function performance across four widely used commercial FaaS platforms, covering regional, configurational, and temporal differences. Using variance decomposition, we find that CPU heterogeneity alone explains 4 % to 97 % of the performance variance on AWS Lambda and Google Cloud Run Functions. On Azure Functions, in contrast, we identify a platform-level mechanism that produces bimodal execution durations and overshadows CPU effects. We observe that the CPU composition on AWS Lambda shifts within a single week. Additionally, we reveal that on Google Cloud Run Functions and Alibaba Function Compute, the availability of instruction set extensions such as SHA-NI differs between CPU types, widening the performance gap further. Since all platforms bill by duration, performance gaps between CPU types translate directly into cost, with invocations on the slowest CPU type costing up to three times as much as those on the fastest.

cs.DC

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arXiv ↗PDF ↗
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

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arXiv ↗PDF ↗