Dynamic Network Slice Bidding for Edge Resource Allocation
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Solution Overview
Problem
Current network architectures struggle to dynamically allocate resources in a manner that optimizes performance and efficiency, failing to account for real-time changes in resource availability and demand, leading to inefficient utilization and suboptimal application performance.
Innovation Solution
Implement a bidding engine within the network to dynamically allocate combinations of network slices and edge computing resources through real-time bidding, considering current demand, availability, and geographical proximity to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static resource allocation is used in current network architectures, then network configuration is simple, but resource utilization efficiency deteriorates due to inability to respond to real-time demand changes
Solution Approach 1:
The patent implements dynamic resource allocation by enabling network slices to bid for resources in real-time based on current demand. The system transitions from static pre-allocation to dynamic auction-based allocation where slices continuously compete for available compute resources, allowing the network to adapt to changing workload patterns and user demands automatically.
Solution Approach 2:
The system incorporates feedback mechanisms where network slices report their actual resource consumption and performance metrics back to the auction engine. This feedback loop enables the auction engine to adjust allocation decisions, optimize resource distribution, and respond to real-time conditions, thereby improving overall resource utilization efficiency.
2Reliability
If real-time bidding for resource slices is implemented, then application performance is improved through optimal resource allocation, but system complexity increases due to bidding engine requirements
Solution Approach 1:
The auction engine is designed as a multi-functional platform that handles multiple operations including resource allocation, slice selection, performance optimization, and resource conservation. By consolidating these functions into a single universal system, the patent reduces the need for separate complex subsystems while maintaining high application performance through coordinated resource management.
3Productivity
If dynamic resource allocation based on real-time demand is implemented, then resource efficiency is improved, but allocation time and processing overhead increase
Solution Approach 1:
The system performs preliminary actions by pre-configuring network slices with bid parameters and resource requirements before actual allocation is needed. The auction engine maintains continuous lists of available resources and slice configurations, enabling rapid matching and allocation decisions when resources become available or demand arises, thereby reducing real-time processing overhead.
Data Source
AI summary
One or more computing devices, systems, and/or methods for dynamic resource slice allocation are provided. A bidding engine dynamically tracks available network slices and available edge computing resources to generate a current list of resource slices corresponding to combinations of available network slices and available edge computing resources. A bidding auction is conducted for the current list of resource slices. A user equipment device is determined to be a winner of the bidding auction for a resource slice based upon a bidding request from the user equipment device. In this way, the user equipment device is provide with access to the resource slice based upon a determination that the user equipment device won the bidding auction.