MAGNet: Machine Learning Guided Application-Aware Networking for Data Centers

Abstract

Modern data centers are witnessing fast-growing east-west traffic on their network infrastructure due to the highly distributed data center applications. Motivated by the heterogeneity of such application workloads, we propose in this paper an extensible network management architecture called MAGNET which enables application-aware intra-data center networking. The crux of MAGNET is the smart endpoint residing within end-hosts, which is empowered by machine learning combined with lightweight workload tracing to detect workload identities and enable workload-dependent packet tagging. The centralized management plane interface of MAGNET allows network functions to interpret packet tags and perform application-aware packet processing. We demonstrate the feasibility of the architecture via prototype implementation and extensive use case evaluation. Our experiments show that the smart endpoint can fingerprint many real-world applications with 99% accuracy only at 12% additional CPU, and that application-aware data plane can potentially bring substantial benefits in terms of security (e.g., via identity-based microsegmentation), CPU usage (e.g., for intrusion detection) and network latency (e.g., via TCP stack customization).