Molly Pan will present her MSE talk, "Memory-Efficient Membership Encoding in Switches" on Tuesday, May 5, 2020 at 3pm via Zoom. Link to Zoom: [ https://princeton.zoom.us/j/7979634003 | https://princeton.zoom.us/j/7979634003 ] The members of her committee are as follows: Jennifer Rexford (adviser), and Wyatt Lloyd (reader). All are invited to attend. The talk abstract follows below. Network applications often define policies to manage network traffic based on its attributes. For example, service chaining forwards traffic to reach the middleboxes it wants to visit, and access control restricts traffic by checking the permission flags it carries. These policies match against packets' attributes in switches before being applied. However, the prior works of attribute encoding all incur a high memory cost to identify the attributes in the data plane. This paper presents MEME, an encoding scheme that clusters the attributes which tend to appear together in the traffic to reduce the memory usage. Naive clustering would still fail since it is ineffective when a cluster contains an excessive number of attributes. To tackle this, MEME breaks the clusters into smaller ones by encoding a minimal number of attributes separately and by taking advantage of the special structures within the attributes. MEME also leverages match-action tables and reconfigurable parsers on modern hardware switches to achieve a final 87.7% lower memory usage, and applies an approximate graph algorithm to achieve 1-2 orders of magnitude faster compilation time than the prior state of the art. These performance gains pave the way for deployment of a real traffic management system desired by the world's largest Internet Exchange Points.