Social Network Partitioning for Server Traffic Reduction
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Solution Overview
Problem
Conventional mechanisms for allocating users to servers in social networks, such as hash functions, do not consider user relationships, leading to excessive network traffic and slower data transfers due to random distribution, which becomes unsustainable as the number of users increases.
Innovation Solution
A system and methodology that partitions social networks to cluster related users, minimizing the number of edges across servers by examining user interactions and contacts, allowing for dynamic migration of users to optimize network traffic and maintain balanced server distribution without requiring metadata, using a stratification process to prioritize and manage user placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hash function is used to allocate users to servers, then user distribution is balanced and computation is fast, but network traffic between servers increases and user relationships are not preserved
Solution Approach 1:
The patent segments the social network into multiple communities or clusters based on user relationships and interaction patterns. Each segment is then assigned to specific servers, preserving local connections within segments while reducing cross-segment traffic. This segmentation approach maintains the benefits of distributed storage while minimizing network traffic between servers.
Solution Approach 2:
The patent applies local quality by optimizing server allocation for local user relationships rather than using a uniform hash function approach. Users with frequent interactions are placed on the same server or closely connected servers, creating locally optimized placements that reduce network traffic for common operations while maintaining overall system balance.
2Quantity of substance
If users are randomly distributed across servers, then server load is balanced, but data transfer speed decreases due to excessive server-to-server communication
Solution Approach 1:
The patent performs preliminary analysis of user interaction patterns and relationship data before finalizing server allocation. By pre-identifying frequently interacting user pairs and pre-optimizing their placement on the same or adjacent servers, the system reduces the need for cross-server data transfers during actual operations, thereby improving data transfer speed while maintaining balanced load distribution.
3Loss of energy
If social network is partitioned to cluster related users, then network traffic is reduced, but system complexity increases due to migration management
Solution Approach 1:
The patent implements dynamic partitioning that automatically adapts to changing user relationships and interaction patterns. The system continuously monitors user behavior and dynamically adjusts server allocations and community assignments, allowing the partitioning structure to evolve with the social network while maintaining optimization benefits without requiring manual intervention or complex static management.
4Ease of manufacture
If conventional allocation methods are used, then implementation is simple, but network traffic becomes unsustainable as user base grows
Solution Approach 1:
The patent introduces an additional dimension to server allocation by considering both the traditional hash-based server assignment and a relationship-based community dimension. Users are assigned to servers through a two-dimensional approach: primary server assignment via hash function and secondary community-based optimization. This dimensional extension allows the system to scale efficiently by adding relationship-aware routing on top of the existing simple hash-based infrastructure.
Data Source
AI summary
The present invention provides a unique system and method that facilitates reducing network traffic between a plurality of servers located on a social-based network. The system and method involve identifying a plurality of vertices or service users on the network with respect to their server or network locations. The vertices' contacts or connections can be located or determined as well. In order to minimize communication traffic, the vertices and their connections with respect to their respective server locations can be analyzed to determine whether at least a subset of nodes should be moved or relocated to another server to facilitate mitigating network traffic while balancing user load among the various servers or parts of the network. Thus, an underlying social network can be effectively partitioned. In addition, the network can be parsed into a collection of nested layers, whereby each successively less dense layer can be partitioned with respect to the previous (partitioned) more dense layer.