Affinity Group Co-location for Distributed Network Latency Reduction
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Solution Overview
Problem
Conventional load balancing methods, such as DNS round robin and hardware load balancers, are inadequate for dynamically balancing tasks and clustering interacting tasks in distributed networks, especially in environments like online games and virtual worlds, where tasks are variable and interconnected, leading to increased latency and performance degradation.
Innovation Solution
A system and method that determines affinity groups among users in a distributed network by performing social network analysis, generating weighted values based on network interactions, and co-locating these groups to nodes with increased capacity, using a primary computing device and distributed computing devices to optimize task distribution and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional load balancing methods (DNS round robin or hardware load balancer) are used, then implementation simplicity is maintained, but task clustering and latency reduction are insufficient
Solution Approach 1:
The system automatically detects network interactions, generates weighted values, and forms affinity groups without manual intervention. The load balancer autonomously monitors client interactions and dynamically assigns clients to nodes based on detected patterns, eliminating the need for complex manual configuration while reducing latency through intelligent task clustering.
Solution Approach 2:
The system continuously monitors network interactions between clients and generates weighted values based on detected patterns. This feedback mechanism allows the load balancer to dynamically adjust client-to-node assignments in real-time, optimizing latency by grouping interacting clients together while maintaining implementation simplicity through automated feedback loops.
2Reliability
If hardware load balancer with affinity maintenance is used, then client-node affinity is preserved, but system scalability and adaptability to dynamic resources are limited
Solution Approach 1:
The system dynamically adjusts client-to-node assignments based on real-time detection of network interactions and changing resource conditions. Unlike static hardware load balancers, this software-based system can adapt to dynamic resource additions and removals while maintaining affinity relationships, enabling both reliability and scalability simultaneously.
Solution Approach 2:
The system changes the parameter of assignment criteria from fixed hardware-based affinity to dynamic software-based affinity groups formed by analyzed network interactions. This allows the system to maintain client-node affinity relationships while adapting to changing resource conditions, achieving both reliability and versatility.
3Device complexity
If simple DNS round robin is used, then implementation complexity is minimized, but task distribution effectiveness deteriorates
Solution Approach 1:
The system performs self-service by automatically analyzing network interactions, generating weighted values, and forming affinity groups without external intervention. This automated approach maintains implementation simplicity while significantly improving task distribution effectiveness through intelligent, data-driven clustering of interacting clients.
Solution Approach 2:
The system replaces the mechanical simplicity of DNS round robin with a sophisticated software-based analysis mechanism that detects network interactions and forms affinity groups. This substitution maintains ease of implementation while dramatically improving task distribution effectiveness through automated intelligent clustering.
4Device complexity
If nodes are balanced independently without considering task interactions, then load distribution is simplified, but network performance deteriorates
Solution Approach 1:
The system merges the load balancing function with affinity group formation by detecting network interactions and creating unified affinity groups that consider both load distribution and task interactions. This consolidation maintains manageable complexity while improving network performance through coordinated clustering of interacting clients.
Solution Approach 2:
The load balancer performs multiple functions: it monitors network interactions, generates weighted values, forms affinity groups, and assigns clients to nodes. This multi-functional approach simplifies the overall system by combining separate functions into a unified mechanism that simultaneously achieves load balancing and performance optimization.
Data Source
AI summary
In at least one embodiment, an apparatus for determining one or more affinity groups in a distributed network is provided. A first distributed computing device is operably coupled to a plurality of clients for enabling electronic interactive activities therebetween. The first distributed computing device is configured to detect at least one network interaction among the plurality of clients. The first distributed computing device is further configured to generate at least one weighted value based on the number of detected network interactions. The first distributed computing device is further configured to establish an affinity group comprising at least one client from the plurality of clients based on the at least one weighted value.