Flexible traffic management

Like all data networks, the networks that connect servers in giant server farms, or servers and workstations in large organizations, are prone to congestion. When network traffic is heavy, packets of data can get backed up at network routers or dropped altogether.

Also like all data networks, big private networks have control algorithms for managing network traffic during periods of congestion. But because the routers that direct traffic in a server farm need to be superfast, the control algorithms are hardwired into the routers’ circuitry. That means that if someone develops a better algorithm, network operators have to wait for a new generation of hardware before they can take advantage of it.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and five other organizations hope to change that, with routers that are programmable but can still keep up with the blazing speeds of modern data networks. The researchers outline their system in a pair of papers being presented at the annual conference of the Association for Computing Machinery’s Special Interest Group on Data Communication.

“This work shows that you can achieve many flexible goals for managing traffic, while retaining the high performance of traditional routers,” says Hari Balakrishnan, the Fujitsu Professor in Electrical Engineering and Computer Science at MIT. “Previously, programmability was achievable, but nobody would use it in production, because it was a factor of 10 or even 100 slower.”

“You need to have the ability for researchers and engineers to try out thousands of ideas,” he adds. “With this platform, you become constrained not by hardware or technological limitations, but by your creativity. You can innovate much more rapidly.”

The first author on both papers is Anirudh Sivaraman, an MIT graduate student in electrical engineering and computer science, advised by both Balakrishnan and Mohammad Alizadeh, the TIBCO Career Development Assistant Professor in Electrical Engineering and Computer Science at MIT, who are coauthors on both papers. They’re joined by colleagues from MIT, the University of Washington, Barefoot Networks, Microsoft Research, Stanford University, and Cisco Systems.