Cellular Resource Allocation With On-Demand Tenant Pricing
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Solution Overview
Problem
Traditional resource allocation methods for provisioning access to resources such as computational, network-related, or storage-based resources are inflexible and lack adaptability, leading to complex pricing structures and increased administrative burden for network providers and customers.
Innovation Solution
Implementing an access on-demand (AoD) provisioning system that utilizes supply-and-demand models to dynamically allocate resources based on predicted and actual demands, optimizing unit prices and resource allocation for tenants, and providing access to radio frequency spectrum, infrastructure, and transport services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed and predetermined allocation approaches are used, then resource allocation is simple to manage, but adaptability to evolving user needs and operational demands deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation by transitioning from fixed predetermined quotas to a flexible system that continuously adjusts resource distribution based on real-time demand signals from tenants and current network conditions, allowing the system to adapt to evolving user needs while maintaining manageable complexity through automated control mechanisms
Solution Approach 2:
The system incorporates feedback loops where tenant demand signals and network provider supply information are continuously exchanged and processed, enabling the resource allocation system to automatically adjust allocations in response to changing conditions, thereby improving adaptability without proportionally increasing administrative complexity
2Adaptability or versatility
If non-commoditized resources are provided with customized pricing structures, then resource allocation meets specific customer needs, but administrative burden and complexity increase
Solution Approach 1:
The patent applies parameter changes by transforming resource pricing from customized per-customer structures to a standardized unit price model based on commodity-like units of resource consumption. This allows the system to maintain adaptability to customer needs through flexible quantity allocation while dramatically reducing administrative burden through simplified pricing parameters that can be automatically applied
3Productivity
If dynamic resource allocation based on predicted demand is implemented, then responsiveness to user needs improves, but system complexity and computational requirements increase
Solution Approach 1:
The system implements preliminary action by using historical data and demand signals to predict future tenant resource needs before actual consumption occurs. This allows the provisioning system to pre-configure resource allocations and pricing structures in advance, improving responsiveness to user needs while managing complexity through automated prediction algorithms rather than manual real-time adjustments
Data Source
AI summary
Systems, devices, and methods related to allocating cellular wireless network resources and providing access on-demand (AoD) to the cellular wireless network resources are provided. An example method includes: obtaining tenant information and historical tenant data of multiple tenants, predicting an anticipated/optimized demand of a resource of a cellular network for each tenant, determining aa anticipated/optimized unit price of the resource, providing the anticipated/optimized unit price to each tenant, receiving an actual demand from each tenant, determining that a deviation of the actual demand from the anticipated/optimized demand is of or below a predetermined threshold, allocating the resource to the tenants based on the actual demand received from each one of the plurality of tenants, and providing access to the allocated resource to one of the plurality of tenants for one or more user equipment (UE) connected to a tenant network of the tenant to use the allocated resource.