5G Service Chain Instance Selection via Reliability Metrics
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Solution Overview
Problem
5G networks face challenges in ensuring the reliability and availability of service chains due to issues like failure of network functions or links, which can disrupt overall service availability and increase latency.
Innovation Solution
A computer-implemented method and system that evaluates the reliability of each service chain instance in a 5G network by using metrics such as weak link drop failure, continued availability, and chain continuity factor. This method selects the most reliable service chain instance to process service requests, thereby optimizing resource utilization and reducing failure risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If service chain instances are selected without reliability evaluation, then resource utilization is simplified and selection speed is improved, but service availability and reliability deteriorate due to potential failures of network functions or links
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing reliability metrics (weak link drop failure, continued availability, chain continuity factor) for each service chain instance before actual service requests arrive. This allows the selection system to quickly retrieve and compare pre-computed reliability values without performing complex real-time analysis, thus improving service availability while keeping the selection process simple and fast.
Solution Approach 2:
The patent introduces reliability metrics as intermediary parameters that mediate between the complex internal state of service chain instances (network function statuses, link conditions) and the selection decision. These metrics serve as simplified proxies that capture essential reliability information without requiring the selection system to directly analyze complex network states, thereby resolving the contradiction between reliability assessment and system complexity.
2Reliability
If the most reliable service chain instance is selected through comprehensive evaluation, then service continuity is improved, but processing time and latency increase due to evaluation overhead
Solution Approach 1:
The patent pre-computes reliability metrics including weak link drop failure, continued availability, and chain continuity factor before service requests arrive. This preliminary evaluation stores reliability information in advance, allowing the selection process to simply retrieve and compare pre-computed values rather than performing comprehensive real-time analysis, thus maintaining service continuity while minimizing selection processing time.
Solution Approach 2:
The patent transforms the complex multidimensional state of service chain instances into simplified scalar reliability parameters (weak link drop failure metric, continued availability metric, chain continuity factor). This parameter transformation condenses complex network function statuses and link conditions into comparable numerical values, enabling fast selection decisions that maintain service continuity without excessive processing time.
3Measurement precision
If reliability metrics such as weak link drop failure and chain continuity factor are calculated for all instances, then service selection accuracy is improved, but computational resources and energy consumption increase
Solution Approach 1:
The patent applies local quality by calculating reliability metrics selectively based on the actual state and importance of different service chain instances and their components. Rather than uniformly evaluating all instances with equal detail, the system focuses computational resources on instances that are more critical or have higher variability in their reliability characteristics, thus achieving accurate reliability measurement while reducing overall computational energy consumption.
Solution Approach 2:
The patent implements partial action by computing reliability metrics (weak link drop failure, continued availability, chain continuity factor) only when necessary or for a subset of service chain instances rather than continuously for all instances. This selective computation approach maintains measurement precision for critical decisions while avoiding unnecessary computational energy consumption during periods when full evaluation is not required.
Data Source
AI summary
Reliability-based service chain instance selection in a 5G network includes evaluating, for a service chain provided in a 5G network, the service chain comprising a series of network functions with processing hand-offs and take-overs therebetween, reliability of each service chain instance of a plurality of available service chain instances of the service chain, obtaining a service request for processing in the 5G network, the service request to be serviced by the service chain, selecting a service chain instance of the plurality of available service chain instances to process the service request, the selecting being based on the evaluated reliabilities of the plurality of available service chain instances, and invoking processing of the selected service chain instance to process the service request.