云数据中心网络多用户多业务并发性算法
Cloud Data Center Network Multi-User Multi-Service Concurrency Algorithm
DOI: 10.12677/CSA.2020.107147, PDF,    科研立项经费支持
作者: 任海科*:福建江夏学院电子信息科学学院,福建 福州;羊富贵:福建江夏学院数理部,福建 福州;李 辉, 陈小龙:锐捷网络股份有限公司,福建 福州
关键词: 云数据中心RDMA多用户多业务并发性Cloud Data Center RDMA Multi-User Multi-Service Concurrency
摘要: 在云数据中心网络中,海量的业务流在数据传输中往往会出现延迟、丢包等现象,但单用户性能测试无法实现多用户多业务流并发性的检测,为了能实时了解数据中心网络多用户多业务并发性数据的传输过程,发现数据传输过程中影响带宽、产生延迟、丢包现象的形成因素,解决了当前无法仿真多用户多条业务流测试瓶颈,解决不需增设服务器的情况下多用户并发性问题。文中采用Parallel Matrix Multiplication算法实现RDMA Ib_write源端口变化,解决了当前无法仿真多用户多条业务流测试瓶颈;采用基于RDMA技术的分布式和多线程组功能实现了并发测试,达到不增设服务器却能实现多用户并发的仿真效果,同时也降低了资源消耗及时间消耗。
Abstract: In the cloud data center network, massive service flows often have delays and packet loss in data transmission, but single-user performance testing cannot achieve multi-user multi-service flow concurrency detection. The transmission process of multi-user multi-service concurrent data on the network has discovered the factors that affect the bandwidth, delay, and packet loss during data transmission, in the case of multi-user concurrency issues. In this paper, the Parallel Matrix Multi-plication algorithm is used to implement the RDMA Ib_write source port change, which solves the current test bottleneck of multi-user multi-service flow testing; the distributed and multi-threaded group function based on RDMA technology is used to achieve concurrent testing, and achieve the simulation effect of multi-user concurrency without adding a server, while also reducing resource consumption and time consumption.
文章引用:任海科, 羊富贵, 李辉, 陈小龙. 云数据中心网络多用户多业务并发性算法[J]. 计算机科学与应用, 2020, 10(7): 1422-1430. https://doi.org/10.12677/CSA.2020.107147

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