SketchLearn: Relieving User Burdens in Approximate Measurement with Automated Statistical Inference
Approximate network measurements trade accuracy to saves resources, but requires intensive manual effort to learn the appropriate tradeoff. Sketchlearn, a sketch-based network measurement framework, learns statistical properties from the resources to eliminates the resource traffic conflicts.
Problems behind: Due to conpetition of network traffics with limited resources (resource conflicts), measurement errors occurs. Sufficient resources must be guaranteed in approximate measurements. A tight binding of resource configurations (resource params) and accuracy parameters exists in approximate measurements.
Current approach: approximate measurements. Limitations: Hard to quantify stats (expected errors, threshholds), hard to find theoretical bounds, hard to examine correctness. Not understand: Hard to define flowkeys
Design requirements: fast (real-time, per-packet processing), resource saving (memory), generalization
Proposed approach: Sketch-based measurement framework. It draws the resource conflicts by building a multi-level sketches. The sketch tracks the frequencies of flow records at bit-level, so the multi-sketch forms a multi-level Gaussian.
Model learning (needs to look in detail): Theory about model inference. Large flow extraction replies on this.
Implementation: Software/hardware data plane and control plane. Flows are updated with each incoming packet.