GRSA: Service-aware flow scheduling for cloud storage datacenter networks

Abstract

The proliferation of the global datasphere has forced cloud storage systems to evolve more complex architectures for different applications. The emergence of these application session requests and system daemon services has created large persistent flows with diverse performance requirements that need to coexist with other types of traffic. Current routing methods such as equal-cost multipath (ECMP) and Hedera do not take into consideration specific traffic characteristics nor performance requirements, which make these methods difficult to meet the quality of service (QoS) for high-priority flows. In this paper, we tailored the best routing for different kinds of cloud storage flows as an integer programming problem and utilized grey relational analysis (GRA) to solve this optimization problem. The resulting method is a GRAbased service-aware flow scheduling (GRSA) framework that considers requested flow types and network status to select appropriate routing paths for flows in cloud storage datacenter networks. The results from experiments carried out on a real traffic trace show that the proposed GRSA method can better balance traffic loads, conserve table space and reduce the average transmission delay for high-priority flows compared to ECMP and Hedera.