Tax Balancing Distributed Node Clusters Actor Model
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Solution Overview
Problem
Current tax balancing software fails to scale with large employee headcounts, resulting in prolonged importation and reconciliation times, which delays tax filings and payments for organizations with thousands of employees.
Innovation Solution
Implementing a computer-implemented method using the Actor Model with AKKA toolkit, distributing tax balancing tasks across a cluster of JVM nodes, where actors are created for each employee and processing load is balanced by redistributing tasks from high-load nodes to low-load nodes, enabling efficient aggregation and output of tax data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current software implementations are used for tax balancing, then the system is simple to operate, but processing time increases significantly for large employee headcounts (from 1 second for 25 employees to 60 minutes for 30,000 employees)
Solution Approach 1:
The patent divides the tax balancing system into multiple independent nodes that can process different portions of employee data simultaneously. Each node handles a subset of the total workload, allowing parallel processing across the cluster. This segmentation enables the system to scale horizontally by adding more nodes to handle larger employee headcounts without proportionally increasing processing time.
Solution Approach 2:
The patent transitions from a single-threaded, sequential processing model to a multi-dimensional distributed architecture using the Actor Model. By introducing spatial distribution across multiple nodes and temporal parallelism through concurrent actor execution, the system processes tax balancing requests across multiple dimensions simultaneously, dramatically improving throughput for large datasets.
2Productivity
If more processing power is allocated to handle large employee headcounts, then processing speed improves, but system complexity increases
Solution Approach 1:
The patent creates a universal node template that can handle any portion of the tax balancing workload. Each node in the cluster is identical in structure and capability, capable of processing any employee data subset. This universality simplifies system management and scaling, as nodes can be added or removed without reconfiguration, and load balancing automatically distributes work across homogeneous units.
Solution Approach 2:
The Actor Model implementation enables nodes to autonomously manage their own state and processing queues. Each node independently handles its assigned actors and data partitions without requiring centralized coordination for every operation. This self-service capability reduces system-wide complexity by distributing decision-making to individual nodes rather than requiring complex centralized control mechanisms.
Data Source
AI summary
Tax balancing comprising receiving tax balancing requests from a number of clients and distributing each client tax balancing request among a number of nodes comprising a cluster. Nodes are added to the cluster according to processing needs of a client tax balancing request. A number of actors are created within each node for each employee of a client, wherein each actor performs a specified computational task related to tax balancing for the employee, and wherein actors are added to a node according to processing needs of a client tax balancing request, and processing load is balanced within the cluster by redistributing actors from nodes with the highest processing loads to nodes with the lowest processing loads. Employee tax balancing calculations are aggregated for each client, and tax balancing filing and payment data for each client are output to a database.