We present the NBA framework, which extends the architecture of the Click modular router to exploit modern hardware, adapts to different hardware configurations, and reaches close to their maximum performance without manual optimization. NBA takes advantages of existing performance-excavating solutions such as batch processing, NUMA-aware memory management, and receiveside scaling with multi-queue network cards. Its abstraction resembles Click but also hides the details of architecturespecific optimization, batch processing that handles the path diversity of individual packets, CPU/GPU load balancing, and complex hardware resource mappings due to multi-core CPUs and multi-queue network cards. We have implemented four sample applications: an IPv4 and an IPv6 router, an IPsec encryption gateway, and an intrusion detection system (IDS) with Aho-Corasik and regular expression matching. The IPv4/IPv6 router performance reaches the line rate on a commodity 80 Gbps machine, and the performances of the IPsec gateway and the IDS reaches above 30 Gbps. We also show that our adaptive CPU/GPU load balancer reaches near-optimal throughput in various combinations of sample applications and traffic conditions.
We present the KENSv2 (KAIST Educational Network System) framework for network protocol implementation. The framework is event-driven to guarantee deterministic behaviour and reproducibility, which in turn delivers ease of debugging and evaluation. Our framework consists of four components: the event generator, the virtual host, the TCP driver and the IP driver. The two drivers are what students have to implement, and we offer to the students the drivers in the binary format for paired testing and debugging. We have developed a test suite that covers three categories of test cases: specification, paired, and logic tests. The framework logs packet transmissions in the PCAP format to allow use of widely available packet analysis tools. Those tools help inspecting logical behaviour of student solutions, such as congestion control. We have designed five step-by-step assignments and evaluated student submissions. With our automated test suite, we have cut down the number of TAs by half for the doubled class size from the previous semester, in total of 3 TAs and 49 students. We plan to continue using KENSv2 in our undergraduate networking course and expand the test suite.
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