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Massachusetts Institute of Technology

A methodology for using eBPF to efficiently monitor network behavior in Linux Kubernetes clusters

Abstract

dc:description.abstract

With the rise of container orchestration systems, such as Kubernetes and microservice based application architectures there has been a corresponding growth in tools aimed at monitoring these systems. As monitoring approaches have evolved the implementation of instrumentation has shifted from the application level to the platform level. The extended Berkeley Packet Filter (eBPF) can enable high performance and low overhead collection for platform level monitoring. Existing commercial eBPF monitoring systems are often tightly integrated systems with large dependencies and little flexibility in integration into alternative monitoring systems. This thesis presents a methodology for developing modular self-contained eBPF monitoring systems which are portable across various kernel versions, Container Network Interface (CNI) plugins, and cluster configurations. The choice of stable hook points and the BPF CO-RE approach to development using the libbpf or Cilium/ebpf loaders is recommended in this methodology. A proof of concept monitor was developed which captures network traffic on a cluster using the stable Traffic Control direct-action hook point. Packet capture at pod virtual ethernet network interfaces was selected to allow for CNI independent correlation of packets to cluster workloads. The prototype developed provides a suitable platform for implementing additional monitoring functionality on top of and was integrated with an existing NetApp cloud monitoring system.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2022

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Zavarella, Timothy D.
Advisors dc:contributor.advisor
  • Palacios, Tomas
  • Ryabin, Aleks

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright MIT

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/145083
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/145083

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Zavarella, Timothy D.. A methodology for using eBPF to efficiently monitor network behavior in Linux Kubernetes clusters. Massachusetts Institute of Technology, 2022. https://hdl.handle.net/1721.1/145083