Service Placement Assistance for Network Resource Allocation
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Solution Overview
Problem
Manual allocation of network resources to satisfy customer service requirements and constraints is inefficient and impractical, leading to increased costs and potential overloading of network devices due to the complexity of permutations and constraints involved.
Innovation Solution
A computing device uses an internal placement model to map customer service, network service, and network resource models, enabling automated or assisted allocation of resources that satisfy requirements and constraints, optimizing resource utilization and minimizing disruption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual provisioning is used to allocate network resources to customer services, then flexibility and customization are improved, but efficiency and cost are worsened due to the complexity of permutations and constraints
Solution Approach 1:
The patent introduces an intermediary system (resource allocation system) that acts as a mediator between customer service requirements and network resource provisioning. This system automatically processes allocation decisions by evaluating permutations and constraints, thereby maintaining customization flexibility while dramatically improving provisioning efficiency and reducing manual effort
Solution Approach 2:
The patent replaces the manual mechanical provisioning process with an automated computational system. The system uses algorithms to evaluate resource allocation options against constraints and requirements, substituting human manual operations with automated computing processes that handle complexity efficiently while maintaining the ability to customize allocations
2Reliability
If manual allocation is used to satisfy customer service requirements, then resource optimization is improved through human judgment, but time consumption and operational complexity are worsened
Solution Approach 1:
The patent implements preliminary action by pre-configuring resource models, service models, and constraint definitions in the system. When allocation requests occur, the system rapidly evaluates against pre-established criteria and constraints, ensuring requirement satisfaction while dramatically reducing allocation time compared to manual analysis of each request
3Productivity
If automated service provisioning is implemented, then efficiency and speed are improved, but adaptability to varying customer requirements and limited reuse across different customers are worsened
Solution Approach 1:
The patent implements universality through a generalized resource allocation system that can handle multiple customer services, resource types, and constraint configurations through a unified framework. The system uses configurable models and parameters that can be adapted to different customers and service types, enabling high-speed automated provisioning while maintaining versatility across diverse requirements
Solution Approach 2:
The patent employs parameter changes by allowing the system to dynamically adjust allocation parameters, constraints, and service models based on specific customer requirements. The configurable nature of the system enables it to adapt to varying customer needs while maintaining automated efficiency, as parameters can be modified without changing the core system architecture
4Adaptability or versatility
If generalized service provisioning platforms are used, then scalability and reuse are improved, but intensive customization and professional services requirements are worsened
Solution Approach 1:
The patent introduces an intermediary layer (the resource allocation system) that sits between the generalized platform and customer-specific requirements. This intermediary handles the complexity of customization through automated algorithms and configurable parameters, allowing the platform to remain reusable and scalable while eliminating the need for intensive manual customization and professional services
Data Source
AI summary
An example computing device is configured to receive, from a customer device, an indication of a plurality of resources and an indication of a plurality of customer services, each of the plurality of customer services being associated with a corresponding at least one requirement and a corresponding at least one constraint. The computing device is configured to automatically determine, for each requirement and each constraint, whether the requirement or the constraint can only be satisfied by a particular resource of the plurality of resources, and allocate, based on the determining, at least one resource of the plurality of resources to at least one customer service of the plurality of customer services. The example computing device is configured to provide, to the customer device and subsequent to the determining for every requirement and for every constraint, information to enable the customer device to provision the at least one customer service.