Journal
PROCEEDINGS OF THE 2018 CONFERENCE OF THE ACM SPECIAL INTEREST GROUP ON DATA COMMUNICATION (SIGCOMM '18)
Volume -, Issue -, Pages 576-590Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3230543.3230559
Keywords
Sketch; Network measurement
Categories
Funding
- CAS Pioneer Hundred Talents Program
- National Key R&D Program of China [2016YFB1000200]
- Research Grants Council of Hong Kong [GRF 14204017]
- National Natural Science Foundation of China [61420106013]
- Outstanding Member Award of Youth Innovation Promotion Association of CAS
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Network measurement is challenged to fulfill stringent resource requirements in the face of massive network traffic. While approximate measurement can trade accuracy for resource savings, it demands intensive manual efforts to configure the right resource-accuracy trade-offs in real deployment. Such user burdens are caused by how existing approximate measurement approaches inherently deal with resource conflicts when tracking massive network traffic with limited resources. In particular, they tightly couple resource configurations with accuracy parameters, so as to provision sufficient resources to bound the measurement errors. We design SketchLearn, a novel sketch-based measurement framework that resolves resource conflicts by learning their statistical properties to eliminate conflicting traffic components. We prototype SketchLearn on OpenVSwitch and P4, and our testbed experiments and stress-test simulation show that SketchLearn accurately and automatically monitors various traffic statistics and effectively supports network-wide measurement with limited resources.
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