Automated Network Resource Allocation via Service Placement Models
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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 resource inefficiency due to the complexity of handling thousands of possible allocations and constraints in large networks.
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 productivity and efficiency deteriorate due to the excessive time and effort required
Solution Approach 1:
The system enables automated self-service provisioning where the computing device automatically allocates network resources to customer services based on service level agreements and network conditions, eliminating the need for manual human intervention while maintaining adaptability through automated decision-making algorithms
Solution Approach 2:
The system dynamically adjusts resource allocation parameters such as bandwidth, latency requirements, and priority levels based on changing network conditions and service demands, allowing flexible adaptation without manual reconfiguration while maintaining high provisioning efficiency through automated parameter optimization
2Ease of operation
If manual allocation methods are used to manage network resources, then detailed control over each resource assignment is improved, but loss of time increases due to the complexity of handling thousands of possible allocations
Solution Approach 1:
The system replaces manual mechanical allocation processes with automated computational algorithms that use machine learning and optimization techniques to rapidly analyze thousands of possible resource allocations and determine optimal assignments, maintaining precise control while reducing allocation time from days to minutes
Solution Approach 2:
The system introduces an automated intermediary computing device that acts as a mediator between network resources and customer services, handling the complex task of resource allocation through automated algorithms while preserving detailed control through service level agreements and policy-based management
3Adaptability or versatility
If generalized service provisioning platforms are used to serve multiple customers, then adaptability to different customers is improved, but device complexity increases requiring intensive customization
Solution Approach 1:
The system implements a universal service provisioning platform that can serve multiple different customers and service types through a single standardized interface, using automated resource allocation algorithms that adapt to different customer requirements without requiring custom development for each customer
Solution Approach 2:
The system segments customer requirements into standardized service level agreement parameters such as bandwidth requirements, latency constraints, and priority levels, allowing the platform to handle diverse customer needs through modular parameter configuration rather than complex customizations
Data Source
AI summary
An example computing device is configured to receive an instance of a customer service model representative of a plurality of customer services. Each of the plurality of customer services associated with a corresponding at least one requirement and a corresponding at least one constraint. The computing device is configured to receive an instance of a resource model representative of a plurality of resources and map the instance of the customer service model and the instance of the resource model to an internal placement model. The computing device is configured to allocate the plurality of resources to the plurality of customer services such that the at least one requirement and the at least one constraint for each of the plurality of customer services are satisfied and inverse map data indicating how the plurality of resources are allocated to a format consumable by the customer device and output the inverse mapped data.