Performance Prediction of On-NIC Network Functions with Multi-Resource Contention and Traffic Awareness

TL;DR

Yala is a performance prediction system for on-NIC network functions that accounts for multi-resource contention and traffic variability, significantly improving accuracy and SLA compliance in data center environments.

Contribution

Yala introduces a SmartNIC-specific, traffic-aware prediction approach that models multi-resource contention for on-NIC network functions, addressing limitations of prior solutions.

Findings

Yala achieves 78.8% higher prediction accuracy.

Yala reduces SLA violations by 92.2%.

Enables practical use cases for on-NIC NF management.

Abstract

Network function (NF) offloading on SmartNICs has been widely used in modern data centers, offering benefits in host resource saving and programmability. Co-running NFs on the same SmartNICs can cause performance interference due to contention of onboard resources. To meet performance SLAs while ensuring efficient resource management, operators need mechanisms to predict NF performance under such contention. However, existing solutions lack SmartNIC-specific knowledge and exhibit limited traffic awareness, leading to poor accuracy for on-NIC NFs. This paper proposes Yala, a novel performance predictive system for on-NIC NFs. Yala builds upon the key observation that co-located NFs contend for multiple resources, including onboard accelerators and the memory subsystem. It also facilitates traffic awareness according to the behaviors of individual resources to maintain accuracy as the external traffic attributes vary. Evaluation using BlueField-2 SmartNICs shows that Yala improves the prediction accuracy by 78.8% and reduces SLA violations by 92.2% compared to state-of-the-art approaches, and enables new practical usecases.