Cross-domain Service Optimization via Multi-domain Architecture
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Solution Overview
Problem
Existing self-optimizing networks (SONs) in radio access networks lack end-to-end service optimization across multiple domains, leading to suboptimal performance and quality of experience for video content delivery, particularly due to the lack of service awareness and inefficient resource allocation.
Innovation Solution
A multi-domain service optimizer (MDSO) architecture that defines key quality indicators (KQIs) for end-to-end services across multiple network domains, including radio access and evolved packet core domains, and applies service optimization instructions to reallocate resources based on quality of experience (QoE) metrics, enabling cross-domain orchestration and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing self-optimizing networks operate independently within single domains, then domain-specific optimization is achieved, but end-to-end service performance across multiple domains deteriorates
Solution Approach 1:
The system segments the optimization function by domain (RAN domain optimizer, EPC domain optimizer) while maintaining coordination through a common service level agreement framework. Each domain optimizer operates independently with domain-specific KPIs but must collectively satisfy E2E service requirements, enabling distributed optimization without requiring complete system redesign.
Solution Approach 2:
The service level agreement (SLA) framework acts as an intermediary between domain-specific optimizers and end-to-end service requirements. The SLA translates E2E service quality targets into domain-specific KPI targets, enabling coordination across domains without direct complex interactions between optimizers. This mediator layer simplifies the overall system architecture.
2Ease of operation
If service awareness is introduced across multiple domains, then quality of experience optimization improves, but network resource allocation efficiency deteriorates
Solution Approach 1:
The system applies local quality optimization by allowing each domain to have its own KPIs and optimization parameters tailored to local conditions, while these local optimizations are coordinated through the SLA framework to achieve overall E2E service quality. This enables quality optimization without requiring centralized control of all resources.
Solution Approach 2:
The system changes optimization parameters dynamically based on service requirements and network conditions. Domain-specific KPIs can be adjusted independently while maintaining alignment with E2E service targets through SLA constraints. This enables flexible resource allocation that adapts to changing conditions without requiring complete system reconfiguration.
3Adaptability or versatility
If video service awareness is implemented, then load balancing decisions improve, but operational complexity increases
Solution Approach 1:
The service level agreement framework provides universal functionality across different domains and service types. The same SLA-based coordination mechanism can handle various service types (video, voice, data) and multiple domains (RAN, EPC) without requiring separate specialized systems. This multi-functionality reduces overall system complexity despite the enhanced adaptability.
Data Source
AI summary
A method for cross-domain service optimization is implemented on a computing device and includes defining at least one key quality indicator (KQI) target for at least one end-to-end (E2E) service, where the at least one E2E service crosses more than one domain of a communications network, receiving an indication of quality of experience (QoE) for the at least one E2E service, based on the indication of QoE, translating the at least one KQI target to at least one set of service optimization instructions for each domain from among the more than one domain, and for each domain, applying the service optimization instructions.