Self-Optimizing Fabric Architecture for 5G Service Convergence
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Solution Overview
Problem
Current network architectures are inadequate for managing the complexity and convergence of telco, cloud, and IoT services in a 5G environment, as they struggle with multi-dimensional variance in service characteristics, traffic, and control, leading to inefficiencies and limitations in scalability and adaptability.
Innovation Solution
A Self-Optimizing Fabric (SOF) architecture that utilizes a federation of controllers to dynamically organize and optimize resources across different domains and technologies, employing a control pattern of Sense, Discern, Infer, Decide, and Act (SDIDA) to create adaptive and self-optimizing compositions of connect, compute, sense, and act resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional network architectures are used to manage telco, cloud, and IoT services, then service delivery is maintained, but the system cannot handle multi-dimensional variance in service characteristics, traffic, and control, leading to inefficiencies and scalability limitations
Solution Approach 1:
The patent implements dynamic network architecture where controllers and domain configurations can be dynamically adjusted based on service requirements. The system transitions from static to dynamic composition of resources, allowing the network to adapt its structure and behavior in real-time to handle varying service characteristics, traffic patterns, and control demands across telco, cloud, and IoT domains.
Solution Approach 2:
The patent creates a universal controller framework that can manage multiple types of domains (connect, compute, sense, store, act) and service providers (telco, cloud, IoT) through a common architecture. This multi-functional approach allows a single controller system to handle diverse service characteristics and traffic types, reducing the need for separate specialized systems while maintaining adaptability across different service domains.
2Adaptability or versatility
If more controllers and domains are added to handle service convergence, then service coverage and functionality are improved, but system complexity and difficulty of management increase
Solution Approach 1:
The patent segments the network management system into independent controllers, each responsible for specific domains (connect, compute, sense, store, act). This segmentation allows the system to scale by adding individual domain controllers rather than expanding a monolithic controller, making the system more manageable while handling service convergence. Each controller operates semi-autonomously, reducing the complexity burden on the overall system.
Solution Approach 2:
The patent introduces a controller federation architecture with intermediary mechanisms that coordinate between multiple domain controllers. This intermediary layer manages the complexity of service convergence by providing standardized interfaces and coordination protocols, allowing controllers to work together seamlessly without directly managing the full complexity of all domain interactions.
3Productivity
If dynamic resource composition is implemented to meet service requirements on demand, then resource optimization and scalability are improved, but control and coordination overhead increase
Solution Approach 1:
The patent implements preliminary configuration and planning mechanisms where resource compositions are pre-planned and prepared based on anticipated service requirements. This allows the system to have resource allocation strategies ready in advance, reducing the real-time control and coordination overhead when services need to be deployed or scaled, thereby improving resource optimization efficiency without excessive coordination delays.
4Extent of automation
If autonomous control patterns (SDIDA) are implemented, then self-optimization and adaptability are improved, but system intelligence requirements and operational complexity increase
Solution Approach 1:
The patent implements self-service control patterns where controllers autonomously perform sensing, discerning, inferring, deciding, and acting (SDIDA) operations for their respective domains. Each controller is equipped with the intelligence to independently optimize its domain resources and make control decisions, reducing the need for complex centralized control while achieving system-wide self-optimization. This distributes the intelligence requirement across multiple independent controllers rather than concentrating it in a single complex system.
Data Source
AI summary
A controller associated with a domain includes a network interface; one or more processors communicatively coupled to the network interface; and memory storing instructions that, when executed, cause the one or more processors to communicate with one or more additional controllers via the network interface, wherein each of the one or more additional controllers is in one or more additional domains, and wherein each domain provides different characteristics, utilize at least part of a control pattern to obtain requirements for a service, and cause, utilizing any of a peer relationship and a hierarchical relationship with the one or more additional controllers, at least part of implementation of a composition of resources to meet the requirements for the service, wherein the composition defines the resources provided in each domain for the service, and wherein the composition is based on the requirements and the different characteristics in each domain.