CNS Core: Small: Collaborative: Salvaging Commodity Operating Systems toSupport Emerging Networking Technologies

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The networking landscape has changed dramatically along with two main advances: (1) fast hardware has led to high-speed, high-bandwidth computer networks; (2) new networking architectures, such as software-defined networking, have given rise to a flexible way to operate networking services. Unfortunately, traditional systems software, such as commodity operating systems, faces critical challenges to efficiently support such high-speed networks and new networking architectures. This project will conduct a holistic study of network software stacks in commodity operating systems to identify critical bottlenecks, propose new solutions to address these bottlenecks, and finally validate the proposed solutions using real prototype implementations.Specifically, the project entails three research thrusts: First, to maximize packet-level parallelism, it will develop a stress-testing approach to locate the serialization bottleneck and design a highly efficient pipelining process to parallelize packet processing in virtualized networks. Second, to improve per-packet processing efficiency for small packets, it will develop a multi-level packet coalescing approach, including hardware interrupt coalescing, software interrupt coalescing, and lossless packet coalescing. Third, to strike a good balance between parallelism and data locality, it will design a holistic scheduling algorithm to optimally multiplex in-kernel interrupts and user-level threads for virtualized network functions.The knowledge developed in this project will help to improve the key aspects of network performance in commodity operating systems, thus benefiting all systems and applications running on these systems. The research outcomes from this project will have influence on the design and implementation of production networking systems and be integrated into core computer science courses. This project will provide training to undergraduate students, graduate students, and students from underrepresented groups.The project mainly generates four types of data including prototype implementations, software instrumentation benchmarks, detailed reports of empirical evaluations, and curriculum materials. These data will be maintained on the project website during the execution of the project and for a minimum of three years after the project's ending date: http://www.cs.binghamton.edu/~huilu/projects/CNets.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.