Status-Byte-Assisted RDMA Transmission Mechanism for Optimizing Multi-Task Video Streaming in Edge Computing

In the context of the rapid development of edge computing, optimizing data transmission and reducing latency is crucial for efficient collaborative processing among edge servers. Traditional TCP/IP protocols are hindered by high latency and low throughput, while RDMA (Remote Direct Memory Access) technology addresses these challenges by enabling direct memory access and bypassing the operating system kernel. However, the RDMA data transmission mechanism based on sliding windows requires frequent memory status exchanges in the order of memory blocks, which can limit its ability to handle multiple concurrent tasks within a single Queue Pair (QP). To address the limitations of the traditional sliding window transmission mechanism in multi-task environments, we propose a novel RDMA data transmission mechanism that utilizes status bytes to indicate memory block utilization, which utilizes stateless server connections, and multi-task shared QP transmission strategies. In the proposed mechanism, fine-grained control over memory blocks is achieved through the status byte, thereby enabling effective multi-task real-time video stream transmission. Experimental results show that, compared to the sliding window method, the proposed status-byte-assisted RDMA transmission mechanism provides higher throughput, lower latency, and reduced resource consumption, thus enhancing system scalability and reducing CPU utilization. Moreover, this mechanism achieves more stable throughput than the sliding window method when transmitting multiple real-time video streams in edge computing scenarios, making it particularly suitable for data transmission in such environments.

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Publication Details

Journal
Applied Sciences
Published
2024-08-23
DOI
https://doi.org/10.3390/app14177437
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
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article

Status-Byte-Assisted RDMA Transmission Mechanism for Optimizing Multi-Task Video Streaming in Edge Computing

Huiyue Yi, Wuxiong Zhang, Shen Wenhui, Donglei Xiao
Applied Sciences
IoT and Edge/Fog Computing
article

Status-Byte-Assisted RDMA Transmission Mechanism for Optimizing Multi-Task Video Streaming in Edge Computing

Huiyue Yi, Wuxiong Zhang, Shen Wenhui, Donglei Xiao
article en

Abstract

In the context of the rapid development of edge computing, optimizing data transmission and reducing latency is crucial for efficient collaborative processing among edge servers. Traditional TCP/IP protocols are hindered by high latency and low throughput, while RDMA (Remote Direct Memory Access) technology addresses these challenges by enabling direct memory access and bypassing the operating system kernel. However, the RDMA data transmission mechanism based on sliding windows requires frequent memory status exchanges in the order of memory blocks, which can limit its ability to handle multiple concurrent tasks within a single Queue Pair (QP). To address the limitations of the traditional sliding window transmission mechanism in multi-task environments, we propose a novel RDMA data transmission mechanism that utilizes status bytes to indicate memory block utilization, which utilizes stateless server connections, and multi-task shared QP transmission strategies. In the proposed mechanism, fine-grained control over memory blocks is achieved through the status byte, thereby enabling effective multi-task real-time video stream transmission. Experimental results show that, compared to the sliding window method, the proposed status-byte-assisted RDMA transmission mechanism provides higher throughput, lower latency, and reduced resource consumption, thus enhancing system scalability and reducing CPU utilization. Moreover, this mechanism achieves more stable throughput than the sliding window method when transmitting multiple real-time video streams in edge computing scenarios, making it particularly suitable for data transmission in such environments.

Applied SciencesVol. 14(17)
Shanghai University (CN), Shanghai Institute of Microsystem and Information Technology (CN)
Decent work and economic growth
Openalex Percentile: Top 85%
IoT and Edge/Fog Computing
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