Search for: All records

Modern network applications and environments, ranging from data centers and IoT devices to AR/VR headsets and underwater robotics, present diverse requirements that cannot be satisfied by the all or-nothing approach of TCP and UDP protocols. Network researchers and engineers need to create highly tailored protocols targeting individual problem domains. Existing library-based approaches either fall short on the flexibility in features or offer them at a significant performance overhead. To address this challenge, we present NetBlocks, a domain-specific language, and compiler for designing ad-hoc protocols and generating their highly optimized host network stack implementations. NetBlocks DSL input allows users to configure protocols by selecting and customizing features. Unlike other DSL compilers, NetBlocks also allows network researchers to extend the system and add more features easily without any prior compiler knowledge. Our design and implementation employ a high-performance Aspect-Oriented Programming framework written with the staging framework BuildIt. We also introduce a novel Layout Customization Layer that allows staging packet layouts alongside the implementation, which is critical for getting the best performance out of the protocol when possible, while allowing the practitioners to maintain compatibility with existing protocol layers where needed. Our evaluations on three applications ranging across deployments in data centers and underwater acoustic networks demonstrate a trade-off between performance (both latency and throughput) and selected features allowing the user to only pay-for what-they-use.

 

The bandwidth and latency requirements of modern datacenter applications have led researchers to propose various topology designs using static, dynamic demand-oblivious (rotor), and/or dynamic demand-aware switches. However, given the diverse nature of datacenter traffic, there is little consensus about how these designs would fare against each other. In this work, we analyze the throughput of existing topology designs under different traffic patterns and study their unique advantages and potential costs in terms of bandwidth and latency ''tax''. To overcome the identified inefficiencies, we propose Cerberus, a unified, two-layer leaf-spine optical datacenter design with three topology types. Cerberus systematically matches different traffic patterns with their most suitable topology type: e.g., latency-sensitive flows are transmitted via a static topology, all-to-all traffic via a rotor topology, and elephant flows via a demand-aware topology. We show analytically and in simulations that Cerberus can improve throughput significantly compared to alternative approaches and operate datacenters at higher loads while being throughput-proportional.