Enterprise Wireless Microslices for QoS and SLO Control
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Solution Overview
Problem
Existing network slicing mechanisms are inflexible, costly, and difficult for smaller enterprises to implement and administer, lacking the ability to efficiently tailor network access to specific business needs and optimize resource use.
Innovation Solution
Implementing microslices in wireless communication networks, which are end-to-end logical networks that can be dynamically adjusted to meet Quality of Service (QoS) and Service Level Objective (SLO) requirements, allowing for granular customization, efficient bandwidth use, and simplified administration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If network slicing mechanisms are implemented to tailor network access to specific business needs, then adaptability and service customization are improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent divides the network into multiple microslices, each tailored to specific business needs with dedicated resources and configurable parameters. This segmentation allows enterprises to customize network access for different applications while managing complexity through modular, isolated network segments rather than attempting to customize the entire network at once.
Solution Approach 2:
The microslice configuration is dynamically adjustable, allowing network parameters, resource allocations, and service policies to be modified in real-time based on changing business requirements. This dynamic capability enables adaptability without requiring permanent structural changes, reducing implementation complexity while maintaining versatility.
2Productivity
If network slicing is implemented to optimize resource use, then productivity and efficiency are improved, but device complexity and administrative burden increase
Solution Approach 1:
The microslice architecture enables automated resource management and self-configuring capabilities, where network resources automatically allocate and optimize themselves based on predefined policies and actual usage patterns. This self-service approach improves resource efficiency while minimizing the administrative burden on network operators.
Solution Approach 2:
The system optimizes resource efficiency by dynamically changing network parameters such as bandwidth allocation, latency thresholds, and priority levels across different microslices. These parameter adjustments occur automatically based on network conditions and service requirements, improving productivity without requiring complex manual administrative intervention.
3Reliability
If microslices are used to monitor and manage data flows for QoS and SLOs, then reliability and service quality are improved, but measurement and monitoring difficulty increase
Solution Approach 1:
The microslice implementation incorporates continuous monitoring and feedback mechanisms that track data flow performance, QoS metrics, and SLO compliance for each microslice. This feedback is automatically collected, analyzed, and used to adjust network parameters, improving service quality while simplifying monitoring through automated closed-loop control rather than complex manual measurement systems.
Data Source
AI summary
A method and apparatus for utilizing microslices in an enterprise wireless communication network to manage and control network performance. The microslice instances are monitored during network operation, for example at communication nodes, functional blocks, and end-to-end to provide Key Performance Indicators (KPIs). These KPI are compared with performance objectives, which may be Service Level Objectives (SLOs). If the performance objectives are not met by the KPIs, then one or more of the microslice instances may be dynamically adjusted until the performance objectives are sufficiently met. Alternatively, the lower priority microslice instances may be dropped (i.e., terminated) until the performance objectives are sufficiently met.