Super-resolution on network telemetry time series
arxiv(2024)
摘要
Fine-grained monitoring is crucial for multiple data-driven tasks such as
debugging, provisioning, and securing networks. Yet, practical constraints in
collecting, extracting, and storing data often force operators to use
coarse-grained sampled monitoring, degrading the performance of the various
tasks. In this work, we explore the feasibility of leveraging the correlations
among coarse-grained time series to impute their fine-grained counterparts in
software. We present Zoom2Net, a transformer-based model for network imputation
that incorporates domain knowledge through operational and measurement
constraints, ensuring that the imputed network telemetry time series are not
only realistic but also align with existing measurements and are plausible.
This approach enhances the capabilities of current monitoring infrastructures,
allowing operators to gain more insights into system behaviors without the need
for hardware upgrades. We evaluate Zoom2Net on four diverse datasets (e.g.
cloud telemetry and Internet data transfer) and use cases (such as bursts
analysis and traffic classification). We demonstrate that Zoom2Net consistently
achieves high imputation accuracy with a zoom-in factor of up to 100 and
performs better on downstream tasks compared to baselines by an average of 38
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