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Heavy-Hitter Detection Entirely in the Data Plane (1611.04825v2)

Published 15 Nov 2016 in cs.NI

Abstract: Identifying the "heavy hitter" flows or flows with large traffic volumes in the data plane is important for several applications e.g., flow-size aware routing, DoS detection, and traffic engineering. However, measurement in the data plane is constrained by the need for line-rate processing (at 10-100Gb/s) and limited memory in switching hardware. We propose HashPipe, a heavy hitter detection algorithm using emerging programmable data planes. HashPipe implements a pipeline of hash tables which retain counters for heavy flows while evicting lighter flows over time. We prototype HashPipe in P4 and evaluate it with packet traces from an ISP backbone link and a data center. On the ISP trace (which contains over 400,000 flows), we find that HashPipe identifies 95% of the 300 heaviest flows with less than 80KB of memory.

Citations (410)

Summary

  • The paper introduces HashPipe, a completely data-plane algorithm using pipelined hash tables to efficiently detect heavy-hitter flows.
  • It detects 95% of the top flows on a 400K-flow ISP trace with under 80KB memory and less than 5% false negatives.
  • The method outperforms traditional techniques like Sample and Hold and Count-Min Sketch, significantly reducing false negatives and resource use.

Overview of "Heavy-Hitter Detection Entirely in the Data Plane"

The paper "Heavy-Hitter Detection Entirely in the Data Plane" introduces HashPipe, a novel algorithm designed to detect heavy-hitter flows efficiently in programmable data-plane switches. The identification of heavy-hitter flows—flows with notably large traffic volumes—is critical for network applications, including flow-size aware routing, Denial of Service (DoS) detection, and traffic engineering.

Programmable switches offer the flexibility to operate at line rates of up to 100 Gbps while maintaining state information across packet processes. This functionality is pivotal for detecting heavy-hitter flows directly in the data plane without offloading tasks to the control plane, which can lead to significant overheads.

Algorithm Design and Implementation

HashPipe is engineered to operate entirely within the constraints of high-speed switches. It functions by organizing a pipeline of hash tables that collectively track the k heavy-hitter flows within constrained memory limits. HashPipe uses a pipelined hash table where each incoming packet updates its count if it matches an existing flow entry. If there is no match and no empty slot, the lightest flow in the hash table is evicted to accommodate the new flow. This systematic eviction ensures the retention of the heaviest flows over time.

Implemented in P4, HashPipe was evaluated using packet traces from an ISP backbone link and a data center, showcasing its practical application. The performance results are compelling: on the ISP trace with approximately 400,000 flows, HashPipe identified 95% of the top 300 heaviest flows using less than 80KB of memory. The false negatives were limited to less than 5%, while false positives were negligible at 0.001%.

Comparison With Traditional Methods

The paper compares HashPipe with traditional heavy-hitter detection techniques such as Sample and Hold and Count-Min Sketch augmented with a flow cache. It is demonstrated that HashPipe significantly outperforms these methods, notably reducing false negatives by over 15% compared to Sample and Hold and by 3-4% compared to Count-Min Sketch. HashPipe's advantage is attributed to its efficient handling of memory constraints and accurate flow count estimation, using only a fraction of the memory size typically required by these traditional methods.

Practical and Theoretical Implications

The practical implications of HashPipe are particularly significant for environments with substantial network traffic and dynamic flow patterns. By efficiently detecting heavy hitters in real-time, network switches can adaptively manage congestion and optimize routing policies directly at the data plane. Theoretically, this research demonstrates the potential for advanced stream processing algorithms to operate within the stringent resource limitations and operational requirements of high-speed networking hardware.

Future Directions

The advancement presented in this paper suggests several future research directions. One possibility is extending HashPipe to handle dynamic adjustments of memory allocation based on traffic volume and diversity. Another area for development is the algorithm's resilience to rapidly changing heavy-hitter flow dynamics, ensuring it adapts consistently to varying network conditions. Further research might also explore the scalability of HashPipe in even larger network topologies with increased traffic complexity.

In conclusion, HashPipe exemplifies how data-plane programmability can be leveraged to perform advanced packet processing tasks previously deemed suitable only for the control plane. This work opens avenues for innovatively rethinking network traffic measurement and management in modern programmable network environments.