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eZtunnel: An eBPF-based Traffic Acceleration Mechanism for Cloud-Native Infrastructures

eZtunnel: An eBPF-based Traffic Acceleration Mechanism for Cloud-Native Infrastructures

Offloading Strategies for Next-Generation Programmable Infrastructures

GitHub: arthursimas1/mesh-fastpath (opens in a new tab)

Abstract

Cloud-native applications, characterized by their scalability, resilience, and flexibility, adopt a microservices architecture to decompose applications into smaller, independently manageable services. This architecture, while offering significant benefits, introduces challenges in service-to-service communication, requiring the use of advanced orchestration and communication frameworks such as Kubernetes and Istio, respectively. However, the complexity of these technologies impose substantial overhead on the underlying infrastructure, introducing longer packet processing paths.

This work identifies the performance bottlenecks arising from the extensive use of service meshes, highlighting the critical issue of CPU overload due to networking-related tasks. To address these challenges, we propose eZtunnel, a transparent offloading technique to allow efficient communications in service meshes. This proposal leverages extended Berkeley Packet Filter (eBPF) as the enabler technology to address and mitigate the problem.

Experiments show that the proposed solution can improve intra-node networking metrics, such as reduction in average Flow Completion Time (FCT) by 41.2%, latency by 42.0%, and increase in throughput by 68.5%. Memory footprint was small, reaching at most 60.5 MB. CPU usage was variable between +29.2% and -23.2%. Through this approach, the research aims to unlock the full potential of cloud-native applications, ensuring that the architectural advancements translate into relevant benefits.

Objectives

The main objective of this work is to present an overview of how service meshes work, their inherent overhead problem, and the implementation of a transparent offloading mechanism to enable efficient networking in service meshes.

Context

This project is part of my Master's Dissertation at University of Campinas (UNICAMP). The proposed framework is concluded and the resulting publications are presented below.

Publications

Funding

  • Innovation Center, Ericsson S.A.
  • São Paulo Research Foundation (FAPESP), grants 2023/05222-3 and 2021/00199-8 (CPE SMARTNESS)
  • Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)- Finance Code 001