Hierarchical Computing Optimizer for Automated Resource Procurement
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Solution Overview
Problem
Current systems lack an efficient method to automatically determine an optimal plan for executing user requests in hierarchical distributed computing, requiring manual resource procurement and bottleneck identification, which is tedious and costly.
Innovation Solution
A system and method for hierarchical cooperative computing, utilizing a vector definition service, rules engine, parametric evaluator, and optimizer to evaluate user requests, identify optimal localities, and automatically acquire resources, while detecting bottlenecks using a decentralized architecture with centralized control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to acquire services and calculate costs for data migration and process execution, then resource procurement can be performed, but the process becomes tedious and time-consuming
Solution Approach 1:
The system enables automated self-service through the optimizer component that automatically determines optimal plans for executing computing requests. The optimizer evaluates multiple factors including data locality, processing locality, regulations, and costs to autonomously procure resources without manual intervention, thereby increasing productivity while reducing time loss.
2Productivity
If data is transferred for processing in centralized systems, then processing can be performed, but the demand and burden on global networks increases
Solution Approach 1:
The system implements local quality by evaluating data locality and processing locality to determine the best endpoint for execution. The optimizer analyzes whether data should be processed where it resides or migrated to specialized processing locations, making localized decisions that reduce unnecessary data transfer and minimize network burden while maintaining processing productivity.
3Productivity
If specialized computer systems are used to process large amounts of data, then processing capability is enhanced, but resource acquisition and bottleneck identification become more complex
Solution Approach 1:
The optimizer acts as an intermediary between computing requests and specialized processing resources. It automatically evaluates system capabilities, identifies bottlenecks, and procures appropriate resources by translating user requests into optimized execution plans, thereby enhancing processing capability while reducing the complexity of resource acquisition and system configuration.
4Reliability
If manual calculation of costs is performed to ensure budgets are met, then cost control is possible, but the process is tedious and inefficient
Solution Approach 1:
The system implements feedback through the optimizer that automatically evaluates costs associated with different execution plans and resource procurement options. By continuously monitoring cost factors and comparing them against budget constraints, the system provides automated feedback to ensure budget compliance while eliminating tedious manual calculations and improving overall efficiency.
Data Source
AI summary
A system for hierarchical cooperative computing is provided, comprising a vector definition service configured to receive a user-submitted request, and compile the request into a vector; a rules engine configured to retrieve the vector from the vector definition service, and evaluate the vector for appropriateness; a parametric evaluator configured to parameterize the vector, and generate at least a run from the parameterized vector; and an optimizer configured to retrieve the run from the parametric evaluator, and determine an optimal plan for executing the user-submitted request.