GCAC Node Resource Allocation via Weighted Data Estimation
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Solution Overview
Problem
The complexity of tracking customer resource allocation across multiple data centers with multiple nodes in a telecommunications network makes it challenging to accurately estimate usage within specified tolerances, requiring techniques that allow nodes to calculate and maintain network-wide resource allocation without disrupting resource management.
Innovation Solution
A replication algorithm that allows GCAC nodes to randomly communicate and share resource usage data, combining it with their own data to estimate network-wide resource usage, which is continuously updated at specified intervals, avoiding full synchronous data replication and traditional synchronization strategies that are processing intensive.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full synchronous data replication is used to track resource allocation across all nodes, then measurement precision of network-wide resource usage is improved, but device complexity and processing overhead increase significantly
Solution Approach 1:
The patent extracts the core function of resource tracking from a centralized synchronous replication system and implements it through individual GCAC nodes that independently estimate network-wide usage by combining their local data with weighted data from other nodes. This eliminates the need for complex centralized synchronization while maintaining tracking accuracy.
Solution Approach 2:
Each GCAC node autonomously performs resource usage estimation by combining its own current key data with weighted key data from other nodes. The nodes self-organize the data collection and estimation process without requiring centralized coordination, reducing synchronization complexity while maintaining measurement precision.
2Reliability
If traditional synchronization strategies are implemented to maintain network-wide resource allocation data, then reliability of resource tracking is improved, but productivity and processing efficiency deteriorate due to intensive processing requirements
Solution Approach 1:
The system pre-calculates weighted key data for each node and stores it locally before estimation is needed. When a GCAC node needs to estimate network-wide resource usage, it already has the weighted data from other nodes readily available, eliminating the need for intensive real-time processing and synchronization operations.
Solution Approach 2:
The patent implements periodic updating of weighted key data at predetermined intervals. Instead of continuous real-time synchronization, each node updates its estimate of other nodes' key data periodically, reducing processing overhead while maintaining sufficient reliability for resource allocation decisions.
3Measurement precision
If real-time data from all nodes is collected to estimate network-wide resource usage, then measurement precision is improved, but loss of time for data collection and processing increases
Solution Approach 1:
Instead of collecting and processing complete real-time data from all nodes, the patent uses a weighted sampling approach where each node contributes its key data multiplied by a weight factor. This partial action approach provides sufficient estimation precision without requiring complete real-time data from every node, significantly reducing data collection and processing time.
Data Source
AI summary
In some implementations, a computer may during a repeating interval in a local GCAC node: (a) receiving and storing key data from one or more other GCAC nodes, the key data indicative of resource usage data for each key in the respective one or more other GCAC nodes wherein each key is associated with an allocation limit equal to a number of resources it may use at any given time across all GCAC nodes; (b) storing current key data, the current key data indicative of the resource usage data for each key in the local GCAC node; (c) determining estimated network-wide key data, the estimated network-wide key data indicative of an estimated network-wide resource usage across all GCAC nodes for each key, the estimated network-wide key data based on a combination of the current key data for the local GCAC node and a weighted multiple of the key data in each of the one or more other GCAC nodes; (d) storing the estimated network-wide key data for each key; and (e) repeating steps (a)-(d) during each interval.