CACC: A Congestion-Aware Control Mechanism to Reduce INT Overhead and PFC Pause Delay

Nowadays, Remote Direct Memory Access (RDMA) is gaining popularity in data centers for low CPU overhead, high throughput, and ultra-low latency. As one of the state-of-the-art RDMA Congestion Control (CC) mechanisms, HPCC leverages the In-band Network Telemetry (INT) features to achieve accurate control and significantly shortens the Flow Completion Time (FCT) for short flows. However, there exists redundant INT information increasing the processing latency at switches and affecting flows’ throughput. Besides, its end-to-end feedback mechanism is not timely enough to help senders cope well with bursty traffic, and there still exists a high probability of triggering Priority-based Flow Control (PFC) pauses under large-scale incast. In this paper, we propose a Congestion-Aware (CA) control mechanism called CACC, which attempts to push CC to the theoretical low INT overhead and PFC pause delay. CACC introduces two CA algorithms to quantize switch buffer and egress port congestion, separately, along with a fine-grained window size adjustment algorithm at the sender. Specifically, the buffer CA algorithm perceives large-scale congestion that may trigger PFC pauses and provides early feedback, significantly reducing the PFC pause delay. The egress port CA algorithm perceives the link state and selectively inserts useful INT data, achieving lower queue sizes and reducing the average overhead per packet from 42 bytes to 2 bits. In our evaluation, compared with HPCC, PINT, and Bolt, CACC shortens the average and tail FCT by up to 27% and 60.1%, respectively.

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Publication Details

Journal
IEEE Transactions on Network and Service Management
Published
2024-08-26
DOI
https://doi.org/10.1109/tnsm.2024.3449699
Citations
2
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.42

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article

CACC: A Congestion-Aware Control Mechanism to Reduce INT Overhead and PFC Pause Delay

Peilin Hong, Jiangping Han, Guanglei Chen, Hang Wang et al.
2 citations
IEEE Transactions on Network and Service Management
Smart Grid Energy Management
0.42
article

CACC: A Congestion-Aware Control Mechanism to Reduce INT Overhead and PFC Pause Delay

Peilin Hong, Jiangping Han, Guanglei Chen, Hang Wang, Kaiping Xue, Xiwen Jie
article en
2 citations

Abstract

Nowadays, Remote Direct Memory Access (RDMA) is gaining popularity in data centers for low CPU overhead, high throughput, and ultra-low latency. As one of the state-of-the-art RDMA Congestion Control (CC) mechanisms, HPCC leverages the In-band Network Telemetry (INT) features to achieve accurate control and significantly shortens the Flow Completion Time (FCT) for short flows. However, there exists redundant INT information increasing the processing latency at switches and affecting flows’ throughput. Besides, its end-to-end feedback mechanism is not timely enough to help senders cope well with bursty traffic, and there still exists a high probability of triggering Priority-based Flow Control (PFC) pauses under large-scale incast. In this paper, we propose a Congestion-Aware (CA) control mechanism called CACC, which attempts to push CC to the theoretical low INT overhead and PFC pause delay. CACC introduces two CA algorithms to quantize switch buffer and egress port congestion, separately, along with a fine-grained window size adjustment algorithm at the sender. Specifically, the buffer CA algorithm perceives large-scale congestion that may trigger PFC pauses and provides early feedback, significantly reducing the PFC pause delay. The egress port CA algorithm perceives the link state and selectively inserts useful INT data, achieving lower queue sizes and reducing the average overhead per packet from 42 bytes to 2 bits. In our evaluation, compared with HPCC, PINT, and Bolt, CACC shortens the average and tail FCT by up to 27% and 60.1%, respectively.

IEEE Transactions on Network and Service ManagementVol. 21(6)
University of Science and Technology of China (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Climate action
Openalex Percentile: Top 39%
Smart Grid Energy Management
0.42
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