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AQ-CAR : adaptive-queueing congestion-aware routing for datacenter traffic forwarding

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https://ir.library.oregonstate.edu/concern/articles/wh247160j

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  • Modern datacenters are constructed with multirooted tree topologies and support multiple service queues per switch port. They support a wide variety of applications and services with stringent performance needs and conflicting requirements. To meet these requirements, recent works focus on load balancing or ECN marking approaches. Though existing load-balancing approaches can deliver good performance for both short and long flows, they do not consider integrating active queue management with load balancing to further improve the network performance and meet applications' requirements. In this paper, we propose AQ-CAR, a novel framework that combines active queue management with an efficient loadbalancing algorithm to deliver high throughput and low latency simultaneously. More specifically, it classifies the queues in each switch port into small, medium, and large classes, each serving a specific flow type. Then it performs adaptive queueing where a flow initially is enqueued at the small service queue and then gets migrated adaptively to the other queues based on the number of bytes it has sent. To achieve congestion- and traffic-aware routing/rerouting, the load balancer first detects the flow type and then piggybacks the congestion information of the queues that serve this flow type. Then it routes/reroutes the flow to the path with minimum congestion. Large-scale ns-2 simulations show that AQ-CAR outperforms the state-of-theart load balancing and ECN marking schemes for different performance metrics under a variety of workloads.
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file details AQ-CAR.pdf 2023-04-02 Public Télécharger