This paper releases and analyzes two new Huawei cloud serverless traces. The traces span a period of over 7 months with over 1.4 trillion function invocations combined. The first trace is derived from Huawei's internal workloads and contains detailed per-second statistics for 200 functions running across multiple Huawei cloud data centers. The second trace is a representative workload from Huawei's public FaaS platform. This trace contains per-minute arrival rates for over 5000 functions running in a single Huawei data center. We present the internals of a production FaaS platform by characterizing resource consumption, cold-start times, programming languages used, periodicity, per-second versus per-minute burstiness, correlations, and popularity. Our findings show that there is considerable diversity in how serverless functions behave: requests vary by up to 9 orders of magnitude across functions, with some functions executed over 1 billion times per day; scheduling time, execution time and cold-start distributions vary across 2 to 4 orders of magnitude and have very long tails; and function invocation counts demonstrate strong periodicity for many individual functions and on an aggregate level. Our analysis also highlights the need for further research in estimating resource reservations and time-series prediction to account for the huge diversity in how serverless functions behave. Datasets and code available at https://github.com/sir-lab/data-release

翻译：本文发布并分析了两个华为云无服务器轨迹数据集。两个轨迹跨越7个多月，合计包含超过1.4万亿次函数调用。第一个轨迹源自华为内部工作负载，包含跨多个华为云数据中心的200个函数每秒详细统计数据。第二个轨迹是来自华为公有FaaS平台的代表性工作负载，包含单个华为数据中心内5000多个函数的每分钟到达率数据。我们通过特征化资源消耗、冷启动时间、编程语言使用、周期性、每秒与每分钟突发性、相关性和流行度，展示了生产级FaaS平台内部机制。研究发现表明，无服务器函数的行为存在显著多样性：不同函数间请求量差异高达9个数量级，部分函数每天执行超10亿次；调度时间、执行时间和冷启动分布在2到4个数量级范围内变化，且具有极长尾特征；函数调用次数在单个函数和聚合层面均表现出强周期性。我们的分析还强调了需要进一步研究资源预留估计和时序预测方法，以应对无服务器函数行为的巨大差异。数据集和代码见https://github.com/sir-lab/data-release