LRGP Distributed Utility Optimization for Messaging Infrastructure
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Event-driven distributed infrastructures face challenges in optimizing resource utilization due to unpredictable workloads and varying consumer demands, requiring efficient resource allocation and tradeoffs between message flow rates and consumer admissions to maintain system performance.
Innovation Solution
The Lagrangian Rates, Greedy Populations (LRGP) solution integrates greedy allocation for consumer admission control with Lagrangian allocation to compute flow rates, allowing dynamic tradeoffs between consumer admissions and flow rates, enabling nodes to collaboratively optimize aggregate system performance in a self-optimization scheme.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are over-provisioned to meet peak load requirements, then system reliability is improved, but hardware cost and resource utilization efficiency deteriorate
Solution Approach 1:
The patent implements dynamic resource allocation through iterative optimization where flow rates and consumer populations are continuously adjusted based on system state. The Lagrangian optimization framework allows the system to dynamically adapt resource distribution to match actual demand, transitioning from static over-provisioning to dynamic right-sizing, thereby maintaining reliability while improving resource utilization efficiency
Solution Approach 2:
The system employs feedback mechanisms through iterative optimization loops that monitor system performance and adjust flow rates and consumer admissions accordingly. The Lagrangian multipliers serve as feedback signals that guide resource allocation decisions, enabling the system to respond to changing conditions and maintain optimal resource utilization while ensuring reliability
2Productivity
If message flow rate is increased to satisfy consumer demand, then consumer service quality is improved, but system resource consumption deteriorates
Solution Approach 1:
The patent changes the parameter of flow rate from a fixed or manually configured value to a dynamically optimized parameter determined through Lagrangian optimization. The system iteratively adjusts flow rates based on consumer populations, resource availability, and utility functions, enabling efficient resource consumption while maintaining high consumer service quality through data-driven parameter adaptation
Solution Approach 2:
The system transitions from static flow rate configuration to dynamic flow rate adjustment through iterative optimization. Flow rates are continuously adapted based on current system state, consumer demand, and resource constraints, allowing the system to efficiently balance consumer service quality with resource consumption under varying workload conditions
3Adaptability or versatility
If consumer admission control is relaxed to accommodate more consumers, then system versatility is improved, but resource availability for existing consumers deteriorates
Solution Approach 1:
The patent implements dynamic consumer admission control where the system iteratively adjusts the number of admitted consumers based on resource availability and utility optimization. Rather than using fixed admission thresholds, the system dynamically determines optimal consumer populations through Lagrangian optimization, enabling versatile consumer support while maintaining resource availability for existing consumers through adaptive population control
4Adaptability or versatility
If distributed optimization is implemented to improve scalability, then system adaptability is improved, but communication overhead between nodes deteriorates
Solution Approach 1:
The patent segments the optimization problem into distributed subproblems solved independently at each node. Each node performs local Lagrangian optimization using its own state information and communicates only essential aggregated data to neighboring nodes. This segmentation enables scalable distributed optimization while minimizing communication overhead by avoiding centralized coordination and reducing the volume of exchanged information
Data Source
AI summary
A system and method which integrates a greedy allocation for consumer admission control with a Lagrangian allocation to compute flow rates and which links the results of the two approaches to allow a tradeoff between consumer admission control and flow rates. The Lagrangian Rates, Greedy Populations (hereinafter, “LRGP”) solution is a scalable and efficient distributed solution for maximizing the total utility in an event-driven distributed infrastructure. The greedy population, consumer portion generates prices used in the LaGrangian rate flow approach. The method is iterative including a regular exchange of information for ongoing optimization, dynamically adjusting producer rates in response to changes to consumer service and dynamically adjusting the service to consumer populations in response to changes in the producer rates.