Service Function Chain Optimization via Live Testing
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Solution Overview
Problem
In virtual network environments, optimizing service chain performance is challenging due to the complexity of diagnosing and automating traffic flow issues, which often requires manual trial-and-error and cross-domain knowledge, making it time-consuming and inefficient.
Innovation Solution
The solution involves cloning a production service chain to create simulated clones with varying parameters, allowing for the simulation of traffic flows to identify and measure the impact of configuration changes on performance, and automatically applying changes that improve flow performance to the original chain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trial-and-error methods are used to optimize service chain parameters, then configuration changes can be tested, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent creates virtual clones of the production service chain that replicate the original configuration and behavior. These clones serve as testbeds for evaluating configuration changes without affecting production systems. By copying the service chain multiple times with different parameter variations, the system can systematically test and measure performance impacts, thereby improving optimization accuracy while reducing the time required compared to manual trial-and-error methods.
Solution Approach 2:
The patent performs preliminary testing and evaluation of configuration changes on cloned service chains before applying changes to the production system. This advance testing allows performance metrics to be measured and validated in advance, ensuring that only optimized configurations are deployed to production, thus reducing the overall optimization time and improving precision.
2Productivity
If multiple service chain parameters are modified simultaneously to optimize performance, then comprehensive improvements can be achieved, but diagnosing the impact of individual changes becomes complex
Solution Approach 1:
The patent segments the service chain into multiple independent virtual clones, each representing a specific configuration variation. By dividing the optimization process into separate clone instances, each testing a specific parameter or combination of parameters, the system can isolate and diagnose the impact of individual changes. This segmentation reduces diagnosis complexity while maintaining comprehensive performance optimization across multiple service chain parameters.
3Reliability
If virtual clones of service chains are created for testing, then configuration changes can be evaluated safely, but system resource consumption increases
Solution Approach 1:
The patent creates virtual clones with modified configuration parameters to test performance variations. By systematically changing parameters in cloned instances rather than modifying the production system, safe evaluation of configuration changes is enabled. The system manages resource consumption by creating clones only when needed for specific optimization scenarios and terminating them after evaluation, thereby maintaining reliability while controlling computational resource usage.
Data Source
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AI summary
Aspects of the disclosed technology address the problems of manually identifying and optimizing service chain (SC) performance bottlenecks by providing solutions for automatically identifying and tuning various SC parameters. In some aspects, a SC optimization process of the disclosed technology includes the replication or cloning of a SC for which traffic flow is to be optimized. Traffic flows for the production chain can then be simulated over one or more SC clones to identify and tune individual system parameters, for example, to determine if the simulated changes produce a positive, negative, or neutral change in flow performance. Systems and machine-readable media are also provided.