SoftFG: A Dynamic Load Balancer for Soft Reconfiguration of Wireless Data Centers

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

3 Scopus citations


In this paper, we investigate the soft-reconfiguration of optical wireless data centers (WDCs). In the considered physical topology, edge top-of-rack (ToR) switches in the leaf layer are inter-connected with core switches in the spine layer via wavelength division multiplexing (WDM) based free-space optical (FSO) links. We propose an agile load balancing (LB) solution, namely SoftFG, to cope with the dynamically changing link load variations and the low-utilization time intervals within the wireless data centers (DCs). SoftFG executes flow grooming (FG) and soft reconfigurations on the virtual topology depending upon the fine-grain network statistics. Unlike the long-term LBs, SoftFG offloads large flows of congested paths onto underutilized links without making any hardware reconfiguration on path capacity and routes. Flows can be offloaded to other wavelengths within the same FSO link (i.e., intra-link), to other FSO links (i.e., inter-link), or within/across topologies (i.e., intra/inter topology). To do so, SoftFG ensures clear visibility on network paths, early congestion detection, and fast-accurate reaction to reroute offloaded flows onto underutilized wavelengths or links. Therefore, SoftFG is designed as a kernel module installed on the virtual switches/hypervisor. The module collects flow statistics based on a source-destination collaborative scheme and records them in flow and path information tables. SoftFG accordingly makes quick decisions on offloading and reroutes flows with high accuracy. Emulation results show that SoftFG delivers about 12 and 17 faster flow completion time (FCT) than LetFlow and CONGA LBs, respectively.
Original languageEnglish (US)
Title of host publication2020 IEEE Wireless Communications and Networking Conference (WCNC)
ISBN (Print)978-1-7281-3107-8
StatePublished - 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


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