Distributed Hash Table Aggregation Tree for Data Efficiency
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Solution Overview
Problem
Distributed systems using overlay networks face inefficiencies in data aggregation due to reliance on partial routing information and successive forwarding, which can lead to suboptimal data delivery and aggregation processes.
Innovation Solution
The method involves creating an aggregation tree structure using existing Distributed Hash Tables (DHTs) to specify an aggregation scope, initiate data aggregation, and apply an aggregation function across nodes, allowing for efficient data collection and verification by dynamically adjusting the tree structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If successive forwarding through intermediate nodes is used for data aggregation, then data can be delivered in a distributed overlay network, but the aggregation process becomes inefficient and suboptimal
Solution Approach 1:
The system pre-establishes an aggregation tree structure using DHT routing information before data aggregation begins. This preliminary organization of nodes into a hierarchical tree structure with designated root nodes enables direct aggregation paths to be ready in advance, eliminating the need for ad-hoc successive forwarding during the actual aggregation process.
Solution Approach 2:
The distributed network is segmented into multiple aggregation trees, each with a designated root node and hierarchical structure. This segmentation divides the large-scale aggregation problem into smaller, manageable tree structures, allowing parallel aggregation operations across multiple trees and improving overall efficiency.
2Device complexity
If partial routing information is maintained in overlay networks, then network complexity is reduced, but data delivery becomes suboptimal
Solution Approach 1:
The system transitions from flat partial routing information to a hierarchical tree structure dimension. By organizing nodes into hierarchical levels with clear parent-child relationships in the aggregation tree, the system adds a structural dimension to routing that enables more efficient data delivery without significantly increasing the complexity of individual node routing tables.
3Reliability
If aggregation trees are dynamically adjusted to verify results, then accuracy of aggregation results is improved, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where aggregation results are verified by checking against the hierarchical tree structure. Intermediate aggregation results from child nodes are validated against parent node expectations, and the tree structure itself provides a feedback loop for detecting improperly acting nodes. This feedback-based verification improves reliability without requiring completely redundant aggregation paths.
Data Source
AI summary
The invention involves a method for aggregating data in a distributed system. The method includes specifying an aggregation scope including a plurality of nodes, where the plurality of nodes includes a destination node and a plurality of non-destination nodes. The method also includes defining an aggregation tree for the plurality of nodes using at least one Distributed Hash Table, where the destination node is the root of the aggregation tree and where each of the plurality of non-destination nodes is one selected from a group of an interior node and a leaf node in the aggregation tree. The method also includes determining a final aggregation result for the aggregation tree using an aggregation function and aggregation data stored on the destination node and the plurality of non-destination nodes.