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FlowDyn: Towards a Dynamic Flowlet Gap Detection using Programmable Data Planes
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO, Computer Networking)ORCID iD: 0000-0001-7734-1653
Karlstad University, Faculty of Economic Sciences, Communication and IT, Department of Computer Science. Karlstad University, Faculty of Economic Sciences, Communication and IT, Centre for HumanIT. Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO, Computer Networking)ORCID iD: 0000-0002-9446-8143
2019 (English)Conference paper (Refereed)
Abstract [en]

Data center networks offer multiple disjoint paths between Top-of-Rack (ToR) switches to connect server racks providing large bisection bandwidth. An effective load-balancing mechanism is required in order to fully utilize the available capacity of the multiple paths. While packet-based loadbalancing can achieve high utilization, it suffers from reordering. Flow-based load-balancing such as equal-cost multipath routing (ECMP) spreads traffic uniformly across multiple paths leading to frequent hash collisions and suboptimal performance. Finally, flowlet based load-balancing such as CONGA or HULA splits flows into smaller units, which are sent on different paths. Most flowlet based load-balancing schemes depend on a proper static setting of the flowlet gap, which decides when new flowlets are detected. While a too small gap may lead to reordering, a too large gap results in missed load-balancing opportunities. In this paper,weproposeFlowDyn,whichdynamicallyadaptstheflowlet gap to increase the efficiency of the load-balancing schemes while avoiding the reordering problem. Using programmable data planes, FlowDyn uses active probes together with telemetry informationtotrackpathlatencybetweendifferentToRswitches. FlowDyn calculates dynamically a suitable flowlet gap that can be used for flowlet based load-balancing mechanism. We evaluate FlowDyn extensively in simulation, showing that it achieves 3.19 times smaller flow completion time at 10% load and 1.16x at 90% load.

Place, publisher, year, edition, pages
2019.
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-76829OAI: oai:DiVA.org:kau-76829DiVA, id: diva2:1394251
Conference
IEEE International Conference on Cloud Networking, 4-6 november 2019, Coimbra, Portugal
Projects
HITS, 4707
Funder
Knowledge FoundationAvailable from: 2020-02-18 Created: 2020-02-18 Last updated: 2020-02-18

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Other links

https://arxiv.org/pdf/1910.03324.pdf

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Hernandez Benet, CristianKassler, Andreas

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