Predictive Computational Resource Distribution for Dynamic Load Balancing
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Solution Overview
Problem
Business enterprises face challenges in redistributing computational tasks across separated resources due to incompatibility, insufficient preparation time, and impracticality in communicating large data sets, leading to insufficient computational resource availability during unexpected demand increases.
Innovation Solution
A system comprising local and global distribution modules, event identification, and predictive scaling engines that identify events and distribute computational tasks from servers with insufficient resources to those with available resources, ensuring dynamic hosting and meeting demand levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computational hardware is operated in excess of average demand to meet peak demands, then reliability is improved, but loss of energy increases due to idle resources during average demand periods
Solution Approach 1:
The patent implements dynamic load balancing that continuously monitors computational resource utilization across multiple locations and automatically redistributes computational tasks in real-time. This dynamic approach allows the system to adapt to changing demand patterns, ensuring peak demand reliability while minimizing idle resource energy consumption during lower utilization periods
Solution Approach 2:
The patent creates a universal computational resource pool where hardware at different locations can serve multiple functions - handling both local and redistributed workloads. This multi-functionality allows the same physical infrastructure to meet peak demands across different locations without requiring dedicated excess capacity at each site, thereby reducing overall idle energy consumption
2Adaptability or versatility
If computational tasks are redistributed from origin location to destination location, then adaptability is improved, but device complexity increases due to compatibility requirements
Solution Approach 1:
The patent enforces homogeneity by requiring that computational hardware at both origin and destination locations possess compatible capabilities to execute the same computational tasks. This compatibility requirement simplifies the redistribution process by eliminating complex compatibility checks and adaptation layers, allowing tasks to be moved freely between standardized hardware platforms
3Productivity
If computational resources are prepared in advance at destination locations, then productivity is improved, but loss of time increases due to preparation requirements
Solution Approach 1:
The patent implements preliminary action by pre-configuring destination locations with computational hardware capable of receiving and executing redistributed tasks. Rather than preparing resources on-demand, the system proactively establishes ready-to-use computational capacity at potential destination locations, eliminating preparation delays when redistribution is needed
4Adaptability or versatility
If large data sets are communicated to different locations for task redistribution, then adaptability is improved, but loss of energy increases due to data transmission requirements
Solution Approach 1:
The patent extracts only the essential task definition and parameters needed for redistribution, separating this small control data from the large data sets that remain localized. This extraction approach allows the system to redistribute computational tasks without requiring energy-intensive transmission of large data volumes, as the actual data processing remains at the origin location
Data Source
AI summary
A system for predicting one or more changes in demand for computational resources expected as a result of one or more events experienced by a business enterprise, and for selecting an appropriate distribution strategy for distributing computational tasks such that a single location with insufficient computational resources can communicate computational tasks in excess of a local computational resource capacity to one or more other locations for processing.