Network Resource Allocation Policy via Utility Optimization
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Solution Overview
Problem
Manual resource allocation in large networks is inefficient due to the inability to quickly consider all factors and adapt to changing workload conditions, leading to suboptimal configurations and difficulties in managing shared resources effectively.
Innovation Solution
A method that constructs queuing models for each application, defines utility functions, and performs optimization to identify an optimal configuration that maximizes overall utility, determining adaptation policies for dynamic resource allocation based on service level agreements and workload changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual resource allocation is performed by service provider personnel, then resource allocation decisions can be made based on human knowledge and experience, but the reaction time is too slow and the service provider personnel cannot consider all factors when making resource allocation decisions
Solution Approach 1:
The system performs self-service by automatically generating resource allocation policies through optimization algorithms that consider all system factors, eliminating the need for manual human decision-making while achieving both comprehensive factor consideration and rapid response to workload changes
Solution Approach 2:
The patent replaces the mechanical human decision-making process with an automated optimization system that uses mathematical models and algorithms to determine resource allocation, thereby achieving faster reaction times and more comprehensive factor consideration
2Loss of time
If a rule based management system is implemented to automate resource allocation, then the reaction time is improved, but the rules may not take the system to an optimal configuration for a particular workload condition
Solution Approach 1:
The system dynamically changes allocation parameters based on actual workload conditions by performing optimization that adjusts resource distribution according to measured performance metrics and current system state, enabling both rapid response and optimal configuration
Solution Approach 2:
The patent implements feedback mechanisms where the system monitors workload conditions and performance metrics, then uses this information to continuously refine and adjust resource allocation policies, ensuring optimal configuration while maintaining fast reaction times
3Extent of automation
If the act of creating effective rule sets based on human knowledge and experience is performed, then resource allocation can be automated, but it is challenging for human beings to quickly consider all factors for large networks
Solution Approach 1:
The patent segments the complex resource allocation problem into manageable components by creating separate optimization models for different resource types and workload conditions, allowing the system to handle large network complexity through systematic decomposition
Solution Approach 2:
The system introduces an intermediary optimization engine that bridges human knowledge and automated decision-making by translating expert rules into mathematical models that can be automatically solved, enabling comprehensive factor consideration without human intervention in the decision process
Data Source
AI summary
A method and apparatus for providing a resource allocation policy in a network are disclosed. For example, the method constructs a queuing model for each application. The method defines a utility function for each application and for each transaction type of each application, and defines an overall utility in a system. The method performs an optimization to identify an optimal configuration that maximizes the overall utility for a given workload, and determines one or more adaptation policies for configuring the system in accordance with the optimal configuration.