TRAINS : a Throughput-Efficient Uniform Total Order Broadcast Algorithm

Michel Simatic (1) , Arthur Foltz (1) , Damien Graux (1) , Nicolas Hascoet (1, 2) , Stéphanie Ouillon (1) , Nathan Reboud (1) , Tiezhen Wang (1)

1 TSP - INF - Département Informatique
2 TSP - ARTEMIS - Département Advanced Research And Techniques For Multidimensional Imaging Systems

Within data centers, many applications rely on a uniform total order broadcast algorithm to achieve load-balancing or fault-tolerance. In this context, achieving high throughput for uniform total order broadcast algorithms is an important issue: It contributes to optimize data center resources usage and to reduce its energy consumption. This paper presents TRAINS , a throughput-efficient uniform total order broadcast algorithm. The paper estimates T RAINS performance. It evaluates the prediction-oriented throughput efficiency (POTE) - i.e. the theoretical ratio between bytes delivered and bytes transmitted on the network. TRAINS POTE improves the POTE of the best algorithm of the literature. For 5 processes, the POTE improvement reaches a peak of 250% for 10 bytes messages. Experimental evaluation confirms T RAINS high throughput capabilities. The trade-off of this throughput improvement is the alteration of the latency. The worst alteration is in the case of 2 processes: 125%

Domaines: Algorithme et structure de données [cs.DS]; Réseaux et télécommunications [cs.NI]

Michel Simatic, Arthur Foltz, Damien Graux, Nicolas Hascoet, Stéphanie Ouillon, et al.. TRAINS : a Throughput-Efficient Uniform Total Order Broadcast Algorithm. NTDS - ICPE 2015 : International Conference on Protocol Engineering (ICPE) and International Conference on New Technologies of Distributed Systems (NTDS), Jul 2015, Paris, France. pp.1 - 8, ⟨10.1109/NOTERE.2015.7293477⟩. ⟨hal-01263231⟩