Publication

SilkRoad: Making Stateful Layer-4 Load Balancing Fast and Cheap Using Switching ASICs

Association for Computing Machinery's Special Interest Group on Data Communications (SIGCOMM)


Abstract

In this paper, we show that up to hundreds of software load balancer (SLB) servers can be replaced by a single modern switching ASIC, potentially reducing the cost of load balancing by over two orders of magnitude. Today, large data centers typically employ hundreds or thousands of servers to load-balance incoming traffic over application servers. These software load balancers (SLBs) map packets destined to a service (with a virtual IP address, or VIP), to a pool of servers tasked with providing the service (with multiple direct IP addresses, or DIPs). An SLB is stateful, it must always map a connection to the same server, even if the pool of servers changes and/or if the load is spread differently across the pool. This property is called per-connection consistency or PCC. The challenge is that the load balancer must keep track of millions of connections simultaneously.

Until recently, it was not possible to implement a load balancer with PCC in a merchant switching ASIC, because high-performance switching ASICs typically can not maintain per-connection states with PCC. Newer switching ASICs provide resources and primitives to enable PCC at a large scale. In this paper, we explore how to use switching ASICs to build much faster load balancers than have been built before. Our system, called SilkRoad, is defined in a 400 line P4 program and when compiled to a state-of-the-art switching ASIC, we show it can load-balance ten million connections simultaneously at line rate.

Related Publications

All Publications

NSDI - April 12, 2021

A Social Network Under Social Distancing: Risk-Driven Backbone Management During COVID-19 and Beyond

Yiting Xia, Ying Zhang, Zhizhen Zhong, Guanqing Yan, Chiun Lin Lim, Satyajeet Singh Ahuja, Soshant Bali, Alexander Nikolaidis, Kimia Ghobadi, Manya Ghobadi

HPCA - March 3, 2021

Heterogeneous Dataflow Accelerators for Multi-DNN Workloads

Hyoukjun Kwon, Liangzhen La, Michael Pellauer, Tushar Krishna, Yu-Hsin Chen, Vikas Chandra

MLSys - April 8, 2021

CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery

Kiwan Maeng, Shivam Bharuka, Isabel Gao, Mark C. Jeffrey, Vikram Saraph, Bor-Yiing Su, Caroline Trippel, Jiyan Yang, Mike Rabbat, Brandon Lucia, Carole-Jean Wu

TSE - January 1, 2020

Approximate Oracles and Synergy in Software Energy Search Spaces

Bobby R. Bruce, Justyna Petke, Mark Harman, Earl T. Barr

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