Optimizing Cost and Performance with arm64

Note: Presentation times are in Coordinated Universal Time (UTC).

Wednesday, 2021, October 13 - 05:0005:30

Liz Fong-Jones, honeycomb.io

Abstract: 

Honeycomb.io, a Series B startup in the observability space, evaluated the arm64 processor architecture in order to improve cost and performance of its telemetry ingest and indexing workload. Over a year, 92% of all its compute workloads migrated successfully to arm64, cost of compute dropped by 40%, and end-user visible latency improved modestly. However, the journey was not without roadblocks and challenges such as lack of full software compatibility, hidden performance quirks, and additional complexity. This talk describes the process of setting up the evaluation, full migration, and improvements made to the ecosystem to make the workload run smoothly at scale in the end.

Liz Fong-Jones, honeycomb.io

Liz is a developer advocate, labor and ethics organizer, and Site Reliability Engineer (SRE) with 16+ years of experience. She is an advocate at Honeycomb for the SRE and Observability communities, and previously was an SRE working on products ranging from the Google Cloud Load Balancer to Google Flights.

BibTeX
@conference {276735,
author = {Liz Fong-Jones},
title = {Optimizing Cost and Performance with arm64},
year = {2021},
publisher = {{USENIX} Association},
month = oct,
}