Hierarchical Resource Allocation for Network Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing network resource allocation systems struggle to dynamically and efficiently allocate configurable resources across hierarchical networks, leading to suboptimal performance due to latency and resource utilization inefficiencies.
Innovation Solution
A Distributed Resource Allocation System (DRAS) monitors and selects optimal resource centers within a Distributed Resource Network (DRN) based on configuration requests, prioritizing resource levels and availability to meet specified parameters such as latency and coverage area, iteratively configuring resources across multiple levels to achieve the best possible match.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If configurable resources are deployed at the edge of the network, then latency is reduced, but the quantity of network elements served is limited
Solution Approach 1:
The network resource allocation system is segmented into multiple hierarchical levels (edge, intermediate, core). Each level serves different functions and different quantities of network elements. Edge resources provide low-latency services to local elements, while higher levels serve broader regions, collectively resolving the contradiction between serving few elements with low latency and serving many elements with higher latency.
Solution Approach 2:
The system introduces a hierarchical dimension to resource deployment, transitioning from a single-dimensional edge/center choice to a multi-dimensional hierarchical structure. This allows the system to simultaneously provide low-latency edge services and broad-coverage core services by distributing resources across multiple hierarchical levels.
2Adaptability or versatility
If configurable resources are deployed at central portions of the network, then the quantity of network elements served increases, but latency increases
Solution Approach 1:
The network is segmented into hierarchical levels where central/core resources handle broad-service requirements and edge resources handle low-latency requirements. This segmentation allows each level to optimize for its specific function rather than trying to simultaneously optimize for both broad coverage and low latency.
Solution Approach 2:
Different hierarchical levels are assigned different qualities/functions: edge resources are optimized for low latency and local service, while core resources are optimized for broad coverage and centralized management. Each level performs its specialized function, resolving the contradiction between serving many elements and maintaining low latency.
3Productivity
If existing resource allocation systems are used, then resource allocation is performed, but dynamic adaptation and efficiency are insufficient
Solution Approach 1:
The resource allocation system is made dynamic through hierarchical configuration where resources can be allocated and reconfigured across multiple levels based on changing network conditions and service requirements. The system adapts dynamically by selecting appropriate hierarchical levels for resource deployment rather than using static allocation methods.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor network conditions, service requirements, and resource utilization across hierarchical levels. This feedback enables dynamic adjustment of resource allocation decisions, improving both efficiency and adaptability by responding to actual network states rather than relying on predetermined static configurations.
Data Source
AI summary
A system described herein may provide a technique for the dynamic selection of configurable resources in an environment that includes a hierarchical or otherwise differentiated arrangement of configurable resources. The environment may include, or may be implemented by, a Distributed Resource Network (“DRN”), which may include hardware or virtual resources that may be configured, including the instantiation of containers, virtual machines, Virtualized Network Functions (“VNFs”), or the like. The DRN may be hierarchical in that some resources of the DRN may provide services to, and/or may otherwise be accessible to, a greater quantity of elements of the DRN or some other network.