Flow Optimization at Inter-Datacenter Networks for Application Run-time Acceleration

TL;DR

This paper presents a flow prioritization method using eBPF in SD-WAN to reduce flow completion times for short flows, enhancing distributed application performance across inter-datacenter networks.

Contribution

It introduces a novel eBPF-based flow segregation technique that prioritizes short flows over long flows in inter-datacenter networks, improving application run-time without modifying applications.

Findings

Achieves 1.5x reduction in flow completion time for short flows

Works with encrypted traffic and is application-agnostic

Effective in accelerating distributed application performance

Abstract

In the present-day, distributed applications are commonly spread across multiple datacenters, reaching out to edge and fog computing locations. The transition away from single datacenter hosting is driven by capacity constraints in datacenters and the adoption of hybrid deployment strategies, combining on-premise and public cloud facilities. However, the performance of such applications is often limited by extended Flow Completion Times (FCT) for short flows due to queuing behind bursts of packets from concurrent long flows. To address this challenge, we propose a solution to prioritize short flows over long flows in the Software-Defined Wide-Area Network (SD-WAN) interconnecting the distributed computing platforms. Our solution utilizes eBPF to segregate short and long flows, transmitting them over separate tunnels with the same properties. By effectively mitigating queuing delays, we consistently achieve a 1.5 times reduction in FCT for short flows, resulting in improved application response times. The proposed solution works with encrypted traffic and is application-agnostic, making it deployable in diverse distributed environments without modifying the applications themselves. Our testbed evaluation demonstrates the effectiveness of our approach in accelerating the run-time of distributed applications, providing valuable insights for optimizing multi-datacenter and edge deployments.