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Friday, July 12 • 9:55am - 10:15am
PostMan: Rapidly Mitigating Bursty Traffic by Offloading Packet Processing

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Unexpected bursty traffic due to certain sudden events, such as news in the spotlight on a social network or discounted items on sale, can cause severe load imbalance in backend services. Migrating hot data---the standard approach to achieve load balance---meets a challenge when handling such unexpected load imbalance, because migrating data will slow down the server that is already under heavy pressure.

This paper proposes PostMan, an alternative approach to rapidly mitigate load imbalance for services processing small requests. Motivated by the observation that processing large packets incurs far less CPU overhead than processing small ones, PostMan deploys a number of middleboxes called helpers to assemble small packets into large ones for the heavily-loaded server. This approach essentially offloads the overhead of packet processing from the heavily-loaded server to others. To minimize the overhead, PostMan activates helpers on demand, only when bursty traffic is detected. To tolerate helper failures, PostMan can migrate connections across helpers and can ensure packet ordering despite such migration. Our evaluation shows that, with the help of PostMan, a Memcached server can mitigate bursty traffic within hundreds of milliseconds, while migrating data takes tens of seconds and increases the latency during migration.

Speakers
PJ

Panpan Jin

National Engineering Research Center for Big Data Technology and System, Key Laboratory of Services Computing Technology and System, Ministry of Education, School of Computer Science and Technology, Huazhong University of Science and Technology, China
JG

Jian Guo

National Engineering Research Center for Big Data Technology and System, Key Laboratory of Services Computing Technology and System, Ministry of Education, School of Computer Science and Technology, Huazhong University of Science and Technology, China
YX

Yikai Xiao

National Engineering Research Center for Big Data Technology and System, Key Laboratory of Services Computing Technology and System, Ministry of Education, School of Computer Science and Technology, Huazhong University of Science and Technology, China
RS

Rong Shi

The Ohio State University, USA
YN

Yipei Niu

National Engineering Research Center for Big Data Technology and System, Key Laboratory of Services Computing Technology and System, Ministry of Education, School of Computer Science and Technology, Huazhong University of Science and Technology, China
FL

Fangming Liu

National Engineering Research Center for Big Data Technology and System, Key Laboratory of Services Computing Technology and System, Ministry of Education, School of Computer Science and Technology, Huazhong University of Science and Technology, China
CQ

Chen Qian

University of California Santa Cruz, USA
YW

Yang Wang

The Ohio State University


Friday July 12, 2019 9:55am - 10:15am PDT
USENIX ATC Track II: Grand Ballroom VII–IX