Researchers developing new architectures and algorithms for data center networks (DCNs) face the challenge of producing meaningful evaluations of their contributions. Traditional evaluation methods like traffic traces and parametric models can fail to reveal weak spots in DCNs. The concept of adversarial inputs shapes traffic data, making it challenging for a DCN to serve it. Adversarial traffic can provide insight into performance issues of a DCN that might go unnoticed with traces and models. This paper presents ADFAT, a genetic algorithm-based system for automated adversarial input generation for DCNs. While previous work focuses on reordering flow volumes or individual packets, our system uses flow arrival times as the adversarial traffic dimension. By creating adversarial flow arrivals for a demand-oblivious RotorNet topology, we show that ADFAT not only finds traffic that causes 22.64% higher mean flow completion times than traffic with uniform random arrival times but is also sensitive to the inherent periodicities and connection patterns of RotorNet. The results indicate ADFAT can find and exploit temporal and structural properties of dynamic and demand-oblivious topologies in an automated way.     «

Researchers developing new architectures and algorithms for data center networks (DCNs) face the challenge of producing meaningful evaluations of their contributions. Traditional evaluation methods like traffic traces and parametric models can fail to reveal weak spots in DCNs. The concept of adversarial inputs shapes traffic data, making it challenging for a DCN to serve it. Adversarial traffic can provide insight into performance issues of a DCN that might go unnoticed with traces and models....     »