Genetic Algorithm Resource Allocation for Network Capacity and Power
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital communications systems face challenges in efficiently allocating resources to resource consumers, particularly in balancing capacity, performance, and power consumption, while minimizing service interruptions and optimizing resource utilization.
Innovation Solution
A computer-implemented method using a genetic algorithm to generate and iteratively improve multiple allocation schemes, each encoding associations between communications network resources and resource consumers, with adjustable metrics indicating the age of associations and derived metrics calculated based on these metrics, to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resource allocation is optimized to balance capacity and performance, then resource utilization improves, but service interruptions may increase during transitions
Solution Approach 1:
The system performs preliminary evaluation of allocation schemes before implementing changes. The genetic algorithm evaluates multiple candidate allocation schemes and selects optimal transitions that minimize service interruptions, rather than making reactive changes that could cause disruptions.
Solution Approach 2:
The system dynamically adjusts resource allocation based on changing conditions while maintaining service continuity. The genetic algorithm iteratively evolves allocation schemes, allowing the system to adapt to varying resource demands and consumer needs without causing abrupt service interruptions.
2Use of energy by moving object
If network equipment is reduced to lower power consumption, then energy efficiency improves, but resource capacity may become insufficient
Solution Approach 1:
The system changes the operational parameters of remaining network equipment to maximize their utilization efficiency. By optimizing how existing resources are allocated and used, the system maintains adequate service capacity while operating with fewer physical devices, thereby reducing overall power consumption.
Solution Approach 2:
The genetic algorithm allocates resources to enable multi-functionality where possible, allowing remaining network equipment to serve multiple purposes and resource consumers. This maximizes the utility of each device, reducing the total number of devices needed while maintaining service capacity.
3Reliability
If resource allocation changes are made reactively rather than proactively, then service interruptions are minimized, but resource optimization is reduced
Solution Approach 1:
The system incorporates feedback mechanisms that monitor resource utilization and service performance continuously. This feedback drives the genetic algorithm to make informed allocation decisions that balance service continuity with optimization goals, rather than making purely reactive or purely theoretical changes.
Solution Approach 2:
The system performs preliminary evaluation of potential allocation changes before implementation. By assessing the impact of proposed changes on service continuity beforehand, the system can optimize resource allocation while minimizing disruptions, rather than making unplanned reactive changes.
Data Source
Figure 1~2
Figure 3
AI summary
A computer implemented method of allocating a set of communications network resources to resource consumers in a digital communications system, the method comprising: generating a set of multiple allocation schemes each encoding the set of resources such that each resource is associated with a set of resource consumers, wherein each allocation scheme has associated a fitness measure evaluated based on the encoded set of resources and consumers in the scheme; iteratively applying a genetic algorithm to the set of allocation schemes to generate, at each iteration, a new set of allocation schemes as a generation of allocation schemes based on a preceding generation such that each allocation scheme in at least a subset of the allocation schemes in a generation is generated based on selected pairs of allocation schemes in a preceding generation, wherein the pairs of allocation schemes are selected by ranking the allocation schemes in the preceding generation based on the fitness measures, wherein each resource in each allocation scheme has associated an adjustable metric indicative of an age of an association between the resource and resource consumers in the allocation scheme, and the allocation scheme has associated a derived metric calculated based on the adjustable metric for each resource in the allocation scheme, wherein the ranking of the allocation schemes is based on the fitness measures of the allocation schemes weighted by the derived metric for the allocation schemes, and wherein the method further comprises: responsive to a predetermined stopping condition for the iteration, assigning resource consumers to resources in the communications system in accordance with an allocation scheme in a most recent generation determined to satisfy a ranking condition.