Performance Analysis of Cloud Applications


Dan Ardelean, Amer Diwan, and Chandra Erdman, Google


Many popular cloud applications are large-scale distributed systems with each request involving tens to thousands of RPCs and large code bases. Because of their scale, performance optimizations without actionable supporting data are likely to be ineffective: they will add complexity to an already complex system often without chance of a benefit. This paper describes the challenges in collecting actionable data for Gmail, a service with more than 1 billion active accounts.

Using production data from Gmail we show that both the load and the nature of the load changes continuously. This makes Gmail performance difficult to model with a synthetic test and difficult to analyze in production. We describe two techniques for collecting actionable data from a production system. First, coordinated bursty tracing allows us to capture bursts of events across all layers of our stack simultaneously. Second, vertical context injection enables us combine high-level events with low-level events in a holistic trace without requiring us to explicitly propagate this information across our software stack.

NSDI '18 Open Access Videos Sponsored by
King Abdullah University of Science and Technology (KAUST)

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

@inproceedings {211225,
author = {Dan Ardelean and Amer Diwan and Chandra Erdman},
title = {Performance Analysis of Cloud Applications},
booktitle = {15th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 18)},
year = {2018},
isbn = {978-1-931971-43-0},
address = {Renton, WA},
pages = {405--417},
url = {},
publisher = {{USENIX} Association},