Hypergraph Partitioning for Call Center Load Distribution
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Solution Overview
Problem
Large call centers face challenges in achieving real-time operations due to excessive inter-node data traffic, which is exacerbated by the need for parallel processing and sophisticated software, making it difficult to maintain efficient computing power and statistics reporting.
Innovation Solution
The method involves partitioning a call center by assigning agents and agent groups as vertices and hyper-edges in a hypergraph, using a hypergraph partitioning algorithm to distribute load equally among nodes and minimize inter-node traffic, and employing a hash function to map objects to nodes for decentralized computation and aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If parallel processing with multiple nodes is used to handle large-scale call centers, then computing power and processing capability are improved, but inter-node data traffic increases excessively
Solution Approach 1:
The patent segments the call center data processing into distinct partitions, each handled by a separate computational node. Agents are divided into groups, with each group assigned to a specific node, allowing parallel processing while minimizing the need for inter-node data exchange. This segmentation enables the system to scale computing power without proportionally increasing inter-node traffic.
2Speed
If sophisticated software and parallel processing are implemented, then real-time information capability is improved, but system complexity increases
Solution Approach 1:
The system divides the complex call center operations into segmented partitions, with each node handling a specific subset of agents and data. This segmentation simplifies the software architecture at each node while enabling real-time processing through parallel execution. The hypergraph partitioning algorithm automatically creates these segments, reducing the complexity burden on individual nodes.
Solution Approach 2:
Each computational node operates autonomously to compute statistics and maintain real-time information for its assigned partition. The nodes perform self-service computations locally without requiring constant coordination with other nodes, thereby achieving real-time information capability while minimizing system complexity and inter-node communication overhead.
3Device complexity
If a single node processes all call center data, then system simplicity is maintained, but processing efficiency decreases for large-scale operations
Solution Approach 1:
The patent implements segmentation by dividing the call center data into multiple partitions, each processed by a dedicated node. This allows the system to maintain relative simplicity within each node while achieving high processing efficiency through parallel execution across multiple nodes. The hypergraph partitioning ensures balanced workloads and minimizes inter-node dependencies.
4Measurement precision
If inter-node data traffic is increased to enable comprehensive statistics computation, then accuracy of statistics is improved, but real-time operation capability deteriorates
Solution Approach 1:
The system segments statistics computation into partition-specific calculations performed locally at each node. Each node computes accurate statistics for its assigned agents and groups independently, eliminating the need for extensive inter-node data traffic. This segmentation maintains measurement precision while enabling real-time operation capability.
Solution Approach 2:
Each node performs self-service statistics computation for its partition, maintaining accurate local statistics without requiring continuous data exchange with other nodes. This approach preserves statistical accuracy while achieving real-time operation, as each node independently maintains and updates its partition's statistics without time-consuming inter-node communication.
Data Source
AI summary
A method for partitioning a call center having N agents associated with M agent groups, for computation by a plurality of computational nodes, has steps for (a) assigning each agent as a vertex in a hypergraph; (b) assigning each agent group as a hyper-edge in the hypergraph; and (c) applying a hypergraph partitioning algorithm to partition the agents and groups relative to the nodes with the hypergraph cost function awarding equal load distribution to nodes and minimizing inter-node traffic.