How does it function? Characterizing long-term trends in production serverless workloads

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.Postprin

FoldFormer:sequence folding and seasonal attention for fine-grained long-term FaaS forecasting

Fine-grained long-term (FGLT) time series forecasting is a fundamental challenge in Function as a Service (FaaS) platforms. The data that FaaS function requests produce are fine-grained (per-second/minute), often have daily periodicity, and are persistent over the long term. Forecasting in the FGLT data regime is challenging, and Transformer models can scale poorly for long sequences. We propose FoldFormer that combines several novel elements - time-to-latent folding, seasonal attention, and convolutions over FFT representations - as a new solution for FGLT forecasting of FaaS function requests. FoldFormer is designed to efficiently consume very fine-grained multi-day data with nearly no additional model, memory, or compute overhead, when compared to consuming coarse-grained data. We show either state-of-the-art or competitive performance for per-minute function requests on the top 5 most requested functions for three data sources, including two in-house Huawei Cloud sources and Azure 2019. We also show state-of-the-art performance at per-second granularity --- a regime that critically limits most other methods

TSMix:time series data augmentation by mixing sources

Data augmentation for time series is challenging because of the complex multi-scale relationships spanning ordered continuous sequences: one cannot easily alter a single datum and expect these relationships to be preserved. Time series datum are not independent and identically distributed random variables. However, modern Function as a Service (FaaS) infrastructure yields a unique opportunity for data augmentation because of the multiple distinct functions within a single data source. Further, common strong periodicity afforded by the human diurnal cycle and its link to these data sources enables mixing distinct functions to form pseudo-functions for improved model training. Herein we propose time series mix (TSMix), where pseudo univariate time series are created by mixing combinations of real univariate time series. We show that TSMix improves the performance on held-out test data for two state-of-the-art forecast models (N-BEATS and N-HiTS) and linear regression.<br/

TSMix:time series data augmentation by mixing sources

Data augmentation for time series is challenging because of the complex multi-scale relationships spanning ordered continuous sequences: one cannot easily alter a single datum and expect these relationships to be preserved. Time series datum are not independent and identically distributed random variables. However, modern Function as a Service (FaaS) infrastructure yields a unique opportunity for data augmentation because of the multiple distinct functions within a single data source. Further, common strong periodicity afforded by the human diurnal cycle and its link to these data sources enables mixing distinct functions to form pseudo-functions for improved model training. Herein we propose time series mix (TSMix), where pseudo univariate time series are created by mixing combinations of real univariate time series. We show that TSMix improves the performance on held-out test data for two state-of-the-art forecast models (N-BEATS and N-HiTS) and linear regression.<br/

FoldFormer:sequence folding and seasonal attention for fine-grained long-term FaaS forecasting

Fine-grained long-term (FGLT) time series forecasting is a fundamental challenge in Function as a Service (FaaS) platforms. The data that FaaS function requests produce are fine-grained (per-second/minute), often have daily periodicity, and are persistent over the long term. Forecasting in the FGLT data regime is challenging, and Transformer models can scale poorly for long sequences. We propose FoldFormer that combines several novel elements - time-to-latent folding, seasonal attention, and convolutions over FFT representations - as a new solution for FGLT forecasting of FaaS function requests. FoldFormer is designed to efficiently consume very fine-grained multi-day data with nearly no additional model, memory, or compute overhead, when compared to consuming coarse-grained data. We show either state-of-the-art or competitive performance for per-minute function requests on the top 5 most requested functions for three data sources, including two in-house Huawei Cloud sources and Azure 2019. We also show state-of-the-art performance at per-second granularity --- a regime that critically limits most other methods