HORSE: Ultra-low latency workloads on FaaS platforms

We investigate if FaaS platforms can handle ultra-low latency workloads that run as low as less than 1μs and show that even for a warm start, the initialization time takes up to 99, 99% of the total execution time. This is due to the resume process of warm sandboxes that takes more time as the number of the sandbox's allocated virtual CPUs (vCPUs) increases. We uncover that two operations use up to 93, 1% of the resume time. The first is the insertion of the paused sandbox's vCPUs to a CPU-sorted run queue. The second is the update of a lock-protected variable, which represents the vCPUs' load on each CPU. This variable is used for frequency scaling.

Authors

Institutions

Publication Details

Published
2024-11-27
DOI
https://doi.org/10.1145/3652892.3700784
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

HORSE: Ultra-low latency workloads on FaaS platforms

Yérom-David Bromberg, François Taı̈ani, Djob Mvondo
Cloud Computing and Resource Management
article

HORSE: Ultra-low latency workloads on FaaS platforms

Yérom-David Bromberg, François Taı̈ani, Djob Mvondo
article en

Abstract

We investigate if FaaS platforms can handle ultra-low latency workloads that run as low as less than 1μs and show that even for a warm start, the initialization time takes up to 99, 99% of the total execution time. This is due to the resume process of warm sandboxes that takes more time as the number of the sandbox's allocated virtual CPUs (vCPUs) increases. We uncover that two operations use up to 93, 1% of the resume time. The first is the insertion of the paused sandbox's vCPUs to a CPU-sorted run queue. The second is the update of a lock-protected variable, which represents the vCPUs' load on each CPU. This variable is used for frequency scaling.

Centre National de la Recherche Scientifique (FR), Institut de Recherche en Informatique et Systèmes Aléatoires (FR), Université de Rennes (FR)
Agence Nationale de la Recherche
Openalex Percentile: Top 67%
Cloud Computing and Resource Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.