Probabilistic Data Structures in Gossip Protocols
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Solution Overview
Problem
Gossip protocols in large-scale distributed computing systems face challenges in synchronizing data quickly without imposing excessive network load, often resulting in slow synchronization due to the burden of network communications and errors associated with space-efficient probabilistic data structures.
Innovation Solution
Implementing a gossip protocol that generates changing probabilistic data structures using a set of hash functions, which are derived from base hash functions through simple arithmetic operations, reducing the likelihood of false positives and network load by varying the hash functions with each round.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If gossip protocols use space-efficient probabilistic data structures to reduce network load, then network load is reduced, but false positive errors increase
Solution Approach 1:
The patent applies dynamics by making the hash functions change over time rounds. Instead of using static hash functions, the system generates different hash functions in each round, which dynamically adapts to reduce false positives while maintaining space efficiency. This resolves the contradiction by allowing the data structure to evolve and improve reliability without increasing network load.
Solution Approach 2:
The patent changes the parameter of hash functions from fixed to variable. By modifying the hash function parameters in each round based on the round number, the system reduces false positive errors while maintaining the space-efficient properties of the probabilistic data structure, thus resolving the contradiction between network load reduction and error reduction.
2Speed
If gossip protocols synchronize data frequently to achieve faster synchronization, then synchronization speed improves, but network load increases
Solution Approach 1:
The patent applies partial action by using probabilistic data structures that provide approximate rather than complete information. This allows the gossip protocol to achieve useful synchronization progress with less data transmission, improving synchronization speed while keeping network load manageable through controlled approximation.
Solution Approach 2:
By dynamically changing hash functions across rounds, the system enables more frequent synchronization attempts without proportionally increasing network load. The dynamic nature allows efficient use of each communication round, improving overall synchronization speed while maintaining acceptable network load levels.
3Ease of manufacture
If gossip protocols use fixed hash functions to generate probabilistic data structures, then generation process is simple, but false positive errors occur frequently
Solution Approach 1:
The patent changes the hash function parameters from fixed to variable by incorporating the round number into the hash function generation. This maintains relative simplicity in the generation process while significantly reducing false positive errors, as the changing parameters provide better distribution without complex generation logic.
Solution Approach 2:
The system transitions from static to dynamic hash function generation, where the hash functions evolve with each round. This dynamic approach maintains ease of generation through systematic parameter updates while effectively reducing false positives, resolving the contradiction between simplicity and reliability.
Data Source
AI summary
Systems and methods are disclosed to implement a gossip protocol to synchronize data in among nodes of a distributed computing system. During a round of the gossip protocol, a first node generates a space-efficient probabilistic data structure (SEPDS) from its data set. The SEPDS is generated using a set of hash functions that changes from round to round. The set of hash functions may be derived using two base hash functions without reliance on the use of any randomizing operations, and the result of each hash function may be assigned to modify a different portion of the SEPDS. The generated SEPDS is sent to a second node, which performs probabilistic queries on the SEPDS to compare the contents of its own data set with the SEPDS. Any data items that are missing from the SEPDS are sent back to the first node, which updates its data set accordingly.