Content Distribution in Collaborative Storage Networks
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Solution Overview
Problem
Conventional methods for optimizing content distribution in collaborative storage networks face significant computational complexity and latency issues as the number of nodes or content increases, making them inefficient and uninterpretable for downstream tasks.
Innovation Solution
The method involves acquiring neighboring topological information to determine a potential cost of content transmission between nodes, focusing on one-hop and two-hop relationships, and distributing content based on this cost to reduce computational workload and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods optimize content distribution by considering all node pairs in the network, then the distribution accuracy improves, but the computational complexity increases significantly
Solution Approach 1:
The patent segments the network topology into one-hop and two-hop relationships, focusing optimization only on neighboring nodes rather than all node pairs. This segmentation reduces the search space from O(N²) to O(k) where k is the average degree of neighboring nodes, thereby reducing computational complexity while maintaining distribution accuracy for the most relevant nodes.
Solution Approach 2:
The patent applies local quality by treating one-hop and two-hop neighboring nodes differently from distant nodes. The optimization algorithm prioritizes local neighboring relationships with higher weight, recognizing that content distribution decisions are most critically affected by immediate neighbors. This local focus reduces computational burden while preserving essential distribution accuracy.
2Quantity of substance
If the number of nodes or content increases in the network, then the network capacity and content variety improve, but the computational latency increases
Solution Approach 1:
By segmenting the optimization problem to consider only one-hop and two-hop neighboring nodes rather than all nodes in the network, the patent reduces computational latency from O(N²) to O(k) where k is the average degree of neighboring nodes. This segmentation allows the system to scale to larger networks with more nodes and content without proportionally increasing computational latency.
3Measurement precision
If conventional methods consider global network topology for content distribution, then the optimization completeness improves, but the interpretability for downstream tasks decreases
Solution Approach 1:
The patent extracts and focuses on the most critical local topological features (one-hop and two-hop relationships) while discarding less relevant global topology information. This extraction provides interpretable insights into content distribution decisions based on immediate neighboring relationships, making the optimization results more actionable for downstream tasks while maintaining sufficient optimization completeness for local distribution decisions.
Data Source
AI summary
Embodiments of the present disclosure relate to a method for distributing content, an electronic device, and a computer program product. The method for distributing content provided in embodiments of the present disclosure includes acquiring neighboring topological information of a plurality of nodes in a collaborative storage network, the plurality of nodes being used for collaboratively storing a plurality of contents requested by a client, the neighboring topological information at least indicating a one-hop relationship between directly connected one-hop node pairs among the plurality of nodes and a two-hop relationship between two-hop node pairs connected via an intermediate node among the plurality of nodes; determining a potential cost of transmitting a plurality of to-be-distributed contents among the plurality of nodes based on the neighboring topological information; and distributing a target content among the plurality of contents to a node among the plurality of nodes based on the potential cost.