Publication

Inside the Social Network’s (Datacenter) Network

SIGCOMM ’15


Abstract

Large cloud service providers have invested in increasingly larger datacenters to house the computing infrastructure required to support their services. Accordingly, researchers and industry practitioners alike have focused a great deal of effort designing network fabrics to efficiently interconnect and manage the traffic within these datacenters in performant yet efficient fashions. Unfortunately, datacenter operators are generally reticent to share the actual requirements of their applications, making it challenging to evaluate the practicality of any particular design. Moreover, the limited large-scale workload information available in the literature has, for better or worse, heretofore largely been provided by a single datacenter operator whose use cases may not be widespread. In this work, we report upon the network traffic observed in some of Facebook’s datacenters. While Facebook operates a number of traditional datacenter services like Hadoop, its core Web service and supporting cache infrastructure exhibit a number of behaviors that contrast with those reported in the literature. We report on the contrasting locality, stability, and predictability of network traffic in Facebook’s datacenters, and comment on their implications for network architecture, traffic engineering, and switch design.

 

Related Publications

All Publications

11-Gbps Broadband Modem-Agnostic Line-of-Sight MIMO Over the Range of 13 km

Yan Yan, Pratheep Bondalapati, Abhishek Tiwari, Chiyun Xia, Andy Cashion, Dawei Zhang, Tobias Tiecke, Qi Tang, Michael Reed, Dudi Shmueli, Hongyu Zhou, Bob Proctor, Joseph Stewart

IEEE GLOBECOM - January 21, 2019

Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems

Maxim Naumov, John Kim, Dheevatsa Mudigere, Srinivas Sridharan, Xiaodong Wang, Whitney Zhao, Serhat Yilmaz, Changkyu Kim, Hector Yuen, Mustafa Ozdal, Krishnakumar Nair, Isabel Gao, Bor-Yiing Su, Jiyan Yang, Mikhail Smelyanskiy

arXiv - September 3, 2020

PyTorch Distributed: Experiences on Accelerating Data Parallel Training

Shen Li, Yanli Zhao, Rohan Verma, Omkar Salpekar, Pieter Noordhuis, Teng Li, Adam Paszke, Jeff Smith, Brian Vaughan, Pritam Damania, Soumith Chintala

VLDB - August 31, 2020

MyRocks: LSM-Tree Database Storage Engine Serving Facebook’s Social Graph

Yoshinori Matsunobu, Siying Dong, Herman Lee

VLDB - August 31, 2020

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy