Virtual World Subgroup Segmentation for Server Load Balancing
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Solution Overview
Problem
Virtual universes face server overload issues due to uneven distribution of avatars and assets across servers, leading to performance degradation and user frustration, as some servers become overloaded while others remain underutilized.
Innovation Solution
A system and method that automatically determine relationships and computational expenditures among objects, segmenting them into optimal subgroups and redistributing these subgroups across servers based on load, ensuring efficient resource utilization and balanced server loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all avatars and assets in a geographic location are stored on a single server, then data completeness is maintained, but server load becomes unbalanced causing overload and performance degradation
Solution Approach 1:
The system segments the set of avatars and assets into multiple subgroups based on their interaction relationships. Each subgroup is then assigned to a different server, allowing load distribution while maintaining data completeness through relationship-based grouping that preserves necessary data co-location.
2Productivity
If avatars are distributed across multiple servers, then server load is balanced, but interaction speed decreases due to cross-server communication overhead
Solution Approach 1:
The system segments avatars into subgroups based on interaction relationships, ensuring that frequently interacting avatars remain in the same subgroup and are assigned to the same server. This minimizes cross-server communication while achieving load balance across the server cluster.
Solution Approach 2:
The system merges avatars with similar interaction patterns into the same subgroup, so that they are processed together on the same server. This combining approach reduces inter-server communication overhead while maintaining balanced distribution across the server infrastructure.
3Device complexity
If servers are statically assigned to geographic locations, then data organization is simple, but adaptability to load changes is poor causing overload situations
Solution Approach 1:
The system dynamically assigns avatars and assets to servers based on real-time interaction relationships and current server load conditions. This dynamic approach allows the system to adapt to changing loads while maintaining organized data structures through relationship-based grouping.
Solution Approach 2:
The system changes the assignment parameters from static geographic location-based assignment to dynamic relationship-and-load-based assignment. This parameter change enables adaptability to load fluctuations while maintaining data organization through interaction relationship grouping.
Data Source
AI summary
A system and method of decreasing server loads and, more particularly, to decrease server load by automatically determining subgroups based on object interactions and computational expenditures. The system includes a plurality of servers; a subgroup optimization module configured to segment a plurality of objects into optimal subgroups; and a server transfer module configured to apportion one or more of the optimal subgroups between the plurality of servers based on a load of each of the plurality of servers. The method includes determining a relationship amongst a plurality of objects; segmenting the objects into optimized subgroups based on the relationships; and apportioning the optimized subgroups amongst a plurality of servers based on server load.