Packet Deduplication via Hash Table for High-Speed Network Streams
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Solution Overview
Problem
Existing network packet analysis systems face challenges in real-time processing due to high volumes and speeds of network traffic, exacerbated by duplicate packets caused by monitoring redundant links and misconfigurations, which increase bandwidth and processing requirements.
Innovation Solution
The implementation of a packet deduplication system that uses hash algorithms to identify and remove duplicate packets in real-time, employing a deduplication window to manage data storage and processing, allowing for efficient operation at speeds up to 10 Gbps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If duplicate packets are removed using off-line processing, then duplicate packets are eliminated, but real-time processing capability is lost
Solution Approach 1:
The system performs preliminary hashing of packet data as packets arrive, storing hash values in a hash table before downstream processing. This preliminary action enables duplicate detection to occur in real-time as packets flow through the system, rather than requiring off-line batch processing after packet capture.
Solution Approach 2:
A hash table data structure serves as an intermediary between incoming packets and the deduplication decision. The hash table stores computed hash values and enables rapid lookup to determine if a packet is a duplicate, allowing real-time duplicate elimination without comparing entire packet contents.
2Measurement precision
If hash algorithms are applied to all incoming packets, then duplicate packets are accurately identified, but processing time and computational resources increase
Solution Approach 1:
The system applies hash algorithms to only the necessary portion of packet data (such as header fields or selected payload segments) rather than processing entire packets. This partial action provides sufficient accuracy for duplicate detection while significantly reducing computational overhead and processing time.
Solution Approach 2:
The system can adjust hash algorithm parameters such as hash function complexity, packet portion selected for hashing, and hash table size to optimize the balance between detection accuracy and processing speed based on specific network conditions and requirements.
3Reliability
If a large deduplication window is used to store previous packet data, then more duplicates are detected, but memory bandwidth and depth requirements increase
Solution Approach 1:
The system extracts only the essential identifying features of packets (represented as hash values) and stores these in the hash table, rather than storing complete packet data. This extraction enables effective duplicate detection while consuming minimal memory resources, as hash values are compact representations that capture packet identity without requiring full packet storage.
Solution Approach 2:
Instead of storing actual packet data, the system creates and stores simplified copies in the form of hash values. These hash copies serve as proxies for the original packets, enabling duplicate detection through comparison of the compact hash representations rather than requiring storage and comparison of full packet contents.
Data Source
AI summary
Systems and methods are disclosed for in-line removal of duplicate network packets in network packet streams operating at high speeds (e.g., 1-10 Gbps and above). A hash generator applies at least one hash algorithm to incoming packets to form one or more different hash values. The packet deduplication systems and methods then use the one or more hash values for each incoming packet to identify data stored for previously received backs and use the identified data to determine if incoming packets are duplicate packets. Duplicate packets are then removed from the output packet stream thereby reducing duplicate packets for downstream processing. A deduplication window can further be utilized to limit the amount of data stored for previous packets based upon one or more parameters, such as an amount of time that has passed and/or a number of packets for which data has been stored. These parameters can also be selected, configured and/or adjusted to achieve desired operational objectives.