No abstract

Programmable network switches promise flexibility and high throughput, enabling applications such as load balancing and traffic engineering. Network measurement is a fundamental building block for such applications, including tasks such as the identification of heavy hitters (largest flows) or the detection of traffic changes.However, high-throughput packet processing architectures place certain limitations on the programming model, such as restricted branching, limited capability for memory access, and a limited number of processing stages. These limitations restrict the types of measurement algorithms that can run on programmable switches. In this paper, we focus on the RMT programmable high-throughput switch architecture, and carefully examine its constraints on designing measurement algorithms. We demonstrate our findings while solving the heavy hitter problem.We introduce PRECISION, an algorithm that uses Probabilistic Recirculation to find top flows on a programmable switch. By recirculating a small fraction of packets, PRECISION simplifies the access to stateful memory to conform with RMT limitations and achieves higher accuracy than previous heavy hitter detection algorithms that avoid recirculation. We also analyze the effect of each architectural constraint on the measurement accuracy and provide insights for measurement algorithm designers.

We present a deterministic distributed 2-approximation algorithm for the Minimum Weight Vertex Cover problem in the CONGEST model whose round complexity is O(log n log ∆/ log 2 log ∆). This improves over the currently best known deterministic 2-approximation implied by [KVY94]. Our solution generalizes the (2 + ǫ)-approximation algorithm of [BCS17], improving the dependency on ǫ −1 from linear to logarithmic. In addition, for every ǫ = (log ∆) −c , where c ≥ 1 is a constant, our algorithm computes a (2 + ǫ)-approximation in O(log ∆/ log log ∆) rounds (which is asymptotically optimal).

Software switches are emerging as a vital measurement vantage point in many networked systems. Sketching algorithms or sketches, provide high-fidelity approximate measurements, and appear as a promising alternative to traditional approaches such as packet sampling. However, sketches incur significant computation overhead in software switches. Existing efforts in implementing sketches in virtual switches make sacrifices on one or more of the following dimensions: performance (handling 40 Gbps line-rate packet throughput with low CPU footprint), robustness (accuracy guarantees across diverse workloads), and generality (supporting various measurement tasks). In this work, we present the design and implementation of Ni-troSketch, a sketching framework that systematically addresses the performance bottlenecks of sketches without sacrificing robustness and generality. Our key contribution is the careful synthesis of rigorous, yet practical solutions to reduce the number of per-packet CPU and memory operations. We implement NitroSketch on three popular software platforms (Open vSwitch-DPDK, FD.io-VPP, and BESS) and evaluate the performance. We show that accuracy is comparable to unmodified sketches while attaining up to two orders of magnitude speedup, and up to 45% reduction in CPU usage.

The recent introduction of SDN allows deploying new centralized network algorithms that dramatically improve network operations. In such algorithms, the centralized controller obtains a network-wide view by merging measurement data from Network Measurement Points (NMPs). A fundamental challenge is that several NMPs may count the same packet, reducing the accuracy of the measurement. Existing solutions circumvent this problem by assuming that each packet traverses a single NMP or that the routing is fixed and known.This work suggests novel algorithms for three fundamental network-wide measurement problems without making any assumptions on the topology and routing and without modifying the underlying traffic. Specifically, this work introduces two algorithms for estimating the number of (distinct) packets or byte volume in the measurement, estimating per-flow packet and byte counts, and finding the heavy hitter flows. Our work includes formal accuracy guarantees and an extensive evaluation consisting of the realistic fat-tree topology and three real network traces. Our evaluation shows that our algorithms outperform existing works and provide accurate measurements within reasonable space parameters.