LightFD: A Lightweight Flow Detection Mechanism for Traffic Grooming in Optical Wireless DCNs

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Wireless data centers (DCs) are enablers of re-configurable data center network (DCN) topologies by augmenting the cabling complexity and inflexibility of traditional wired DCs. In this paper, we propose an optical traffic grooming (TG) for mice flows (MFs) and elephant flows (EFs) in a wireless DCN which is interconnected with free-space optical (FSO) links operating on wavelength division multiplexing (WDM). Since handling the bandwidth-hungry EFs along with delay-sensitive MFs over the same network resources have undesirable consequences, proposed TG policy treat MFs and EFs separately. MFs/EFs destined to the same rack are groomed into larger rack-to-rack MF/EF flows over dedicated lightpaths whose routes and capacities are jointly determined taking the load balancing into account. Performance evaluations of proposed TG policy show a significant throughput improvement thanks to bandwidth efficient utilization of the wireless links. Therefore, proposed TG requires expeditious flow detection mechanisms which can immediately classify EFs with very high accuracy. Since these demands cannot be met by existing sampling and port-mirroring based solutions, we propose a lightweight and fast in-network flow detection (LightFD) mechanism. LightFD is designed as a module on the Virtual-Switch/Hypervisor, which detects EFs based on acknowledgment sequence number of flow packets. Emulation results show that LightFD can provide up to 110 times faster detection speeds than sampling-based methods with %100 detection accuracy. We also demonstrate that the EF detection speed has a considerable impact on achievable EF throughput.
Original languageEnglish (US)
Title of host publication2018 IEEE Global Communications Conference (GLOBECOM)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)9781538647271
StatePublished - Feb 21 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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