Sieve: A flow scheduling framework in SDN based data center networks
Maiass Zaher, Aymen Hasan Alawadi, Sándor Molnár
Abstract
Today’s data centers act as the primary infrastructure for emerging technologies. QoS imposes requirements for more attentive techniques that can deal with different characteristics of traffic classes and patterns. In this context, network flows can be classified into large and long-lived flows called elephant flows and mice flows, which are small and short-lived flows. According to the characteristics of the emerging technologies, e.g., IoT and Big Data, mice flows are dominant; Hence, it is crucial to improve Flow Completion Time (FCT) for such delay-sensitive flows. This paper presents Sieve, a new distributed Software Defined Networks (SDN) based framework. Sieve initially schedules a portion of the flows based on the available bandwidth despite their classes. We propose a distributed sampling technique which sends a portion of the packets to the controller. Furthermore, Sieve polls the edge switches periodically to get the network information rather than polls all switches in the network, and it reschedules elephant flows only. Mininet emulator and mathematical analysis have been employed to validate the proposed solution in 4-ary Fat-Tree DCN. Sieve provides less FCT up to around 58% for mice flows and maintains throughput of elephant flows compared to Equal Cost MultiPath (ECMP) and Hedera.