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Compiling Cross-Language Network Programs Into Hybrid Data Plane
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AI
DOI：10.1109/TNET.2021.3132303.png)
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摘要
En 中文
Network programming languages (NPLs) empower operators to program network data planes (NDPs) with unprecedented efficiency. Currently, various NPLs and NDPs coexist and no one can prevail over others in the short future. Such diversity is raising many problems including: (1) programs written with different NPLs can hardly interoperate in the same network, (2) most NPLs are bound to specific NDPs, hindering their independent evolution, and (3) compilation techniques cannot be readily reused, resulting in much wasteful work. These problems are mostly owing to the lack of modularity in the compilers, where the missing part is an intermediate representation (IR) for NPLs. To this end, we propose Network Transaction Automaton (NTA), a highly-expressive and language-independent IR, and show it can express semantics of 7 mainstream NPLs. Then, we design CODER, a modular compiler based on NTA, which currently supports 2 NPLs and 3 NDPs. Experiments with real and synthetic programs show CODER can correctly compile those programs for real networks within moderate time.
Keyword:
Semantics
Automata
Switches
Program processors
Costs
Bandwidth
Topology
Network programming language
intermediate representation
software defined networks
hybrid data plane
期刊
I
IF:
3.6
论文数:
4.4K
被引数:
9.5K
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