MEC Network Slice Control for 5G Latency Optimization
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Solution Overview
Problem
Current MEC-enabled 5G deployments face challenges in achieving end-to-end (E2E) latency requirements and optimal resource allocation for multi-slice support, leading to suboptimal performance in latency-sensitive applications like V2V, V2I, and V2X communications.
Innovation Solution
The implementation of a slice control function (SCF) within a network function virtualization (NFV) domain, which performs E2E latency function modeling and evaluation, and dynamically allocates virtualized resources across the edge cloud using a slice-aware strategy to optimize network slice instantiation and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If dynamic resource allocation is implemented to meet E2E latency requirements, then latency performance is improved, but device complexity increases
Solution Approach 1:
The system segments the network into multiple network slices, each dedicated to specific latency-sensitive applications. The slice control function divides resource allocation into per-slice management, reducing the complexity of managing all resources centrally while ensuring low latency for each slice.
Solution Approach 2:
A slice control function is introduced as an intermediary between the resource manager and network slices. This mediator handles the complexity of dynamic resource allocation and latency optimization, shielding the overall system from the complexity of real-time adjustments.
2Adaptability or versatility
If multi-slice support is added to MEC-enabled 5G deployments, then service versatility is improved, but device complexity increases
Solution Approach 1:
The MEC server is designed with multi-functionality to support multiple network slices simultaneously. It provides universal resource allocation capabilities that work across different slice types (e.g., V2V, V2I, V2X), allowing a single system to handle diverse services without proportionally increasing complexity.
Solution Approach 2:
Each network slice is configured with slice-specific parameters and quality attributes tailored to its particular latency requirements. This allows customized optimization for each slice type while maintaining a unified management framework, improving versatility without uniformly increasing system complexity.
3Reliability
If guaranteed E2E latency is implemented, then reliability is improved, but loss of energy increases
Solution Approach 1:
The system dynamically adjusts resource allocation based on actual latency requirements and network conditions. Rather than maintaining fixed high resource levels to guarantee latency, the system optimizes resource distribution in real-time, ensuring latency guarantees are met only when needed and reducing energy consumption during normal operation.
Data Source
AI summary
A system configured to track network slicing operations within a 5G communication network includes processing circuitry configured to determine a network slice instance (NSI) associated with a QoS flow of a UE. The NSI communicates data for a network function virtualization (NFV) instance of a Multi-Access Edge Computing (MEC) system within the 5G communication network. Latency information for a plurality of communication links used by the NSI is retrieved. The plurality of communication links includes a first set of non-MEC communication links associated with a radio access network (RAN) of the 5G communication network and a second set of MEC communication links associated with the MEC system. A slice configuration policy is generated based on the retrieved latency information and slice-specific attributes of the NSI. Network resources of the 5G communication network used by the NSI are reconfigured based on the generated slice configuration policy.