Throughput Unfairness in Dragonfly Networks under Realistic Traffic Patterns

Abstract

Dragonfly networks have a two-level hierarchical arrangement of the network routers, and allow for a competitive cost-performance solution in large systems. Nonminimal adaptive routing is employed to fully exploit the path diversity and increase the performance under adversarial traffic patterns. Throughput unfairness prevents a balanced use of the resources across the network nodes and degrades severely the performance of any application running on an affected node. Previous works have demonstrated the presence of throughput unfairness in Dragonflies under certain adversarial traffic patterns, and proposed different alternatives to effectively combat such effect. In this paper we introduce a new traffic pattern denoted adversarial consecutive (ADVc), which portrays a real use case, and evaluate its impact on network performance and throughput fairness. This traffic pattern is the most adversarial in terms of network fairness. Our evaluations, both with or without transit-over-injection priority, show that global misrouting policies do not properly alleviate this problem. Therefore, explicit fairness mechanisms are required for these networks.