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CLIP: Accelerating Features Deployment for Programmable Switch

Tingting Xu, Xiaoliang Wang, Chen Tian, Yun Xiong, Yun Lin, Baoliu Ye

2023Year
6Citations

Abstract

Cloud network serves a large number of tenants and a variety of applications. The continuously changing demands require a programmable data plane to achieve fast feature velocity. However, the years-long release cycle of traditional function-fixed switches can not meet this requirement. Emerging programmable switches provide the flexibility of packet processing without sacrificing hardware performance. Due to the trade-off between performance and flexibility, the current programmable switches make compromises in some aspects such as limited memory/computation resources, and lack of the capacity to realize complicated computation. The programmable switches can not satisfy the demand for network services and applications in production networks. We propose a framework that leverages host servers to extend the capability of network switches quickly, accelerates new feature deployment, and verifies new ideas in production networks. Specifically, to build the unified programmable data plane, we propose essential design and implementation challenges including a programming abstraction that allows automatically and effectively deploying network functions on switch and server clusters, allocating traffic to fully utilize the server resources, and supporting flexible scaling of the system. The quick deployment of self-defined functions in a realistic system has verified the feasibility and practicality of the proposed framework.

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