Allocation Graphs for Targeted Communication Resource Planning
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Solution Overview
Problem
Efficient allocation of limited resources in complex computing networks is challenging due to their finite nature and time constraints, necessitating improved methods for determining target allocation parameters for targeted communications.
Innovation Solution
A method and system for determining target allocation parameters involve establishing network communication channels with computing devices, receiving and analyzing data on execution events, generating allocation graphs, and calculating remaining available allocations to optimize resource usage for targeted communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resources are allocated to multiple execution events simultaneously, then resource utilization increases, but resource conflicts and allocation complexity increase
Solution Approach 1:
The system performs preliminary actions by generating allocation graphs in advance that predict future resource availability and execution event opportunities. These pre-computed allocation graphs enable the system to make informed allocation decisions without real-time complexity, allowing multiple execution events to be coordinated efficiently while maintaining manageable allocation complexity through advance planning
Solution Approach 2:
The allocation system dynamically adjusts resource allocation based on changing conditions by updating allocation graphs with new execution event data and instantaneous available allocation information. This dynamic adaptation allows the system to optimize resource utilization across multiple execution events while managing complexity through flexible, data-driven reconfiguration of allocation parameters
2Measurement precision
If allocation decisions are made in real-time based on instantaneous available allocation, then allocation accuracy improves, but processing time and system complexity increase
Solution Approach 1:
The system generates allocation graphs in advance that contain pre-analyzed resource availability patterns and execution event characteristics. This preliminary computation allows the system to make accurate allocation decisions by referencing pre-processed data rather than performing complex real-time analysis, thereby maintaining high allocation accuracy while reducing processing time
Solution Approach 2:
The system incorporates feedback mechanisms that update allocation graphs with actual execution event outcomes and instantaneous available allocation data. This feedback loop refines future allocation decisions by learning from past performance, improving allocation accuracy over time while the system processes updates efficiently by building upon existing allocation graph structures
3Productivity
If comprehensive data from multiple execution events is collected, then allocation optimization improves, but data processing complexity and storage requirements increase
Solution Approach 1:
The system extracts only the most relevant features and parameters from comprehensive execution event data for inclusion in allocation graphs. By selectively extracting critical information such as resource consumption patterns, timing characteristics, and success metrics while discarding redundant data, the system achieves effective allocation optimization without the burden of processing complete raw datasets
Solution Approach 2:
The allocation graph structure serves multiple functions simultaneously: it stores historical execution event data, predicts future resource availability, guides allocation decisions, and provides performance feedback. This multi-functional design consolidates what would otherwise require separate complex systems into a unified structure, reducing overall data processing complexity while maintaining comprehensive allocation optimization capabilities
Data Source
AI summary
This disclosure is directed to systems and methods for determining target allocation parameters for initiating targeted communications in complex computing networks, which may be associated with the allocation of allocatables in execution events over a period of time. The systems and methods may include receiving a desired allocation; determining a first available allocation at a first time; generating allocation information for a second period comprising the first time; determining a second available allocation at a second time; determining a remaining available allocation, based on the allocation information and the second available allocation; and determining one or more target allocation parameters for initiating a targeted communication to a computing device after the second time.