Using Self-Similarity to Increase Network Testing Effectiveness
Abstract
AI
AI
The effectiveness of network testing is often hindered by the variety in network topologies and configurations, which limits the scope of testing to a small subset of networks. This paper addresses the challenge of selecting a representative network that can reflect the behavior of a broader class by leveraging the concept of self-similarity. By identifying subnetworks that demonstrate common characteristics shared across the class, the study proposes a method to enhance the validity of network testing. Insights into protocol conformance testing are discussed, emphasizing the shortcomings of current models and the necessity for formal validations. Advanced mechanisms using self-similar structures are explored to improve network testing effectiveness.
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