Dynamic Service Chain Capacity Management
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Solution Overview
Problem
Current solutions for scaling virtualized gateway infrastructures in network function virtualization (NFV) systems are inadequate as they primarily rely on CPU capacity and memory usage measurements, ignoring key performance indicators (KPIs) such as throughput, end-to-end service delay, and packet loss, which are crucial for effective service delivery.
Innovation Solution
A communication system that manages chained services by configuring measurement indications for packets, recording measurement information as they traverse service chains, and dynamically adjusting capacity based on end-to-end measurement information, identifying bottleneck services and underutilized services to scale up or down accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple services are provided to multiple subscribers, then service coverage and user satisfaction are improved, but network equipment becomes overwhelmed and performance deteriorates
Solution Approach 1:
The patent implements dynamic capacity management for service chains by continuously monitoring measurement information (throughput, delay, packet loss) and automatically adjusting the number of service chain instances. This allows the system to adapt service capacity in real-time based on actual network conditions and KPI thresholds, preventing equipment overload while maintaining service coverage.
Solution Approach 2:
The system establishes a feedback loop where measurement information is collected from service chains, evaluated against configured thresholds, and used to trigger capacity scaling decisions. This closed-loop control enables the system to respond to performance degradation by adding capacity or to over-provisioning by reducing capacity, thereby maintaining optimal network performance.
2Productivity
If service capacity is increased to handle more traffic, then throughput is improved, but network congestion and resource waste occur
Solution Approach 1:
The patent implements dynamic capacity adjustment by monitoring throughput KPIs and adjusting service chain capacity accordingly. When throughput falls below thresholds, capacity is increased; when it exceeds thresholds, capacity is reduced. This dynamic approach prevents both under-provisioning and over-provisioning, optimizing resource efficiency while maintaining service productivity.
Solution Approach 2:
The system changes operational parameters (number of service chain instances) based on measured KPIs. By continuously adjusting capacity parameters according to throughput, delay, and packet loss measurements, the system optimizes the balance between service productivity and resource efficiency, avoiding static over-provisioning.
3Measurement precision
If measurement information is collected from all service functions, then monitoring accuracy is improved, but system complexity and overhead increase
Solution Approach 1:
The patent segments the measurement collection process by having individual network elements (service chains, network elements) autonomously generate and report their own measurement information. This distributed segmentation reduces central coordination complexity while maintaining comprehensive monitoring accuracy across the service chain.
Solution Approach 2:
Network elements are empowered to self-measure and self-report their performance metrics (throughput, delay, packet loss) according to configured measurement indications. This self-service approach eliminates the need for complex centralized measurement infrastructure, reducing system overhead while preserving measurement precision.
Data Source
AI summary
An example method is provided in one example embodiment and may include configuring a measurement indication for a packet; forwarding the packet through a service chain comprising one or more service functions; recording measurement information for the packet as it is forwarded through the service chain; and managing capacity for the service chain based, at least in part, on the measurement information. In some cases, the method can include determining end-to-end measurement information for the service chain using the recorded measurement information. In some cases, managing capacity for the service chain can further include identifying a particular service function as a bottleneck service function for the service chain; and increasing capacity for the bottleneck service. In various instances, increasing capacity for the bottleneck service can include at least one of: instantiating additional instances of the bottleneck service; and instantiating additional instances of the service chain.