Randomized trial evaluates TCP variants' performance across different workloads in cloud environments, suggesting optimal congestion control policies.
Choosing the right Transmission Control Protocol (TCP) congestion control algorithm matters more in shared cloud environments than is often appreciated, yet head-to-head comparisons across heterogeneous, concurrently running workloads remain rare in the literature. We evaluated three widely used variants Cubic, Reno, and Bottleneck Bandwidth and Round-trip propagation time (BBR) under four workload types: synthetic throughput testing with iperf3, real-time stream processing using Apache Kafka and Apache Flink, distributed I/O, and compute-intensive sorting via Hadoop TeraSort. All experiments ran inside a dumbbell network topology built on four Amazon Web Services (AWS) m7i-flex.large Elastic Compute Cloud (EC2) instances, with a dedicated forwarding node and static routing forcing every flow through that single contention point. Across every metric we measured throughput, end-to-end latency, retransmission count, and job completion time the three variants behaved quite differently depending on the workload. The results give concrete, workload-specific guidance for operators choosing a congestion control policy in multitenant cloud deployments.
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D. et al. (2026) studied this question.
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