Resource Allocation Modeling via In-House Activity Forecasting
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Solution Overview
Problem
Current computing systems face challenges in managing and processing large amounts of data efficiently, particularly in distributed environments, where failures in nodes can lead to service interruptions and data loss, and existing resource allocation policies do not adequately account for in-house activity modifications.
Innovation Solution
A distributed computing system with a communications grid architecture that includes multiple control nodes for redundancy, event stream processing engines for real-time data handling, and in-house activity modeling to optimize resource allocation policies based on historical data and extension/early relinquishment behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If resource allocation policies are based on traditional demand forecasting methods, then implementation is simple, but accuracy of demand prediction is insufficient leading to suboptimal resource utilization
Solution Approach 1:
The patent segments the resource allocation system into multiple independent components: demand forecasting module, in-house activity modeling module, policy optimization module, and resource allocation module. Each module performs a specific function and can be developed, tested, and maintained independently, reducing overall system complexity while enabling sophisticated demand prediction through coordinated module interactions.
Solution Approach 2:
The patent introduces an intermediary modeling layer that sits between raw historical data and resource allocation decisions. This intermediary layer processes historical data through specialized models to generate refined demand forecasts and policy recommendations, acting as a buffer that simplifies the interface between data sources and allocation mechanisms while improving prediction accuracy.
2Productivity
If traditional resource allocation policies are used without considering in-house activity modifications, then policy management is straightforward, but resource utilization efficiency decreases due to inadequate accounting for extensions and early relinquishments
Solution Approach 1:
The patent implements preliminary action by proactively modeling in-house activity patterns (extensions and early relinquishments) using historical data before resource allocation decisions are made. The system pre-calculates probable modification behaviors and incorporates these predictions into policy optimization, allowing the system to anticipate and prepare for in-house activities rather than reacting to them after they occur.
Solution Approach 2:
The patent establishes a feedback mechanism where actual in-house activity modifications (extensions and early relinquishments) are tracked and fed back into the modeling system. This feedback loop continuously refines the in-house activity models by comparing predicted versus actual behaviors, improving the accuracy of future predictions and enabling progressively better resource utilization without increasing operational complexity.
3Reliability
If distributed computing systems lack redundancy and failover mechanisms, then system architecture is simpler, but reliability decreases causing service interruptions during node failures
Solution Approach 1:
The patent merges multiple critical functions (demand forecasting, in-house activity modeling, policy optimization, and resource allocation) into an integrated system that operates across distributed nodes. This unified approach ensures that essential capabilities are replicated and coordinated across nodes, providing reliability through functional integration rather than through complex point-to-point redundancy mechanisms.
Solution Approach 2:
The patent implements multi-functionality by designing distributed nodes that can perform multiple roles: data processing, model execution, policy implementation, and coordination functions. Each node is equipped with universal capabilities to handle various tasks, allowing the system to maintain operations even when individual nodes fail, as other nodes can assume their functions without requiring specialized redundant hardware.
Data Source
AI summary
Exemplary embodiments are generally directed to methods, mediums, and systems for accounting for extensions or reductions of the period for which a resource (e.g., computer processor time, scientific apparatus, storage units, devices, etc.) is allocated. According to exemplary embodiments, allocation-based aggregated effects of extension and relinquishment are modeled. The modeled effects are used to offset allocation forecasts based on historical data. As a result, the dimensionality of the problem of incorporating in-house data is greatly reduced as compared to other techniques, and allocation forecasts can be made more accurately and efficiently.