芮兰兰,
邱雪松
北京邮电大学 网络与交换技术国家重点实验室 ??北京 ??100876
基金项目:国家自然科学基金(61702048, 61302078)
详细信息
作者简介:于天放：男，1980年生，博士生，研究方向为软件定义网络
芮兰兰：女，1979年生，博士，副教授，研究方向为网络和业务质量管理、泛在网络、大数据等
邱雪松：男，1973年生，博士，教授，研究方向为网络管理与通信软件
通讯作者:于天放 m2015010@foxmail.com
中图分类号:TP393计量
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被引次数:0
出版历程
收稿日期:2018-02-28
修回日期:2018-08-13
网络出版日期:2018-08-22
刊出日期:2018-12-01
Research on SDN-based Load Balancing Technology of Server Cluster
Tianfang YU,,Lanlan RUI,
Xuesong QIU
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Funds:The National Natural Science Foundation of China (61702048, 61302078)
摘要
摘要:在当前的网络体系结构下，采用硬件系统实现服务器集群负载均衡存在着获取负载节点状态困难、流量导向方式复杂等制约因素，不利于提升服务器集群的伸缩性和服务性能。针对此问题，该文提出一种基于软件定义网络(SDN)的负载均衡机制(SDNLB)。该机制借助SDN具有的集中式控制和流量灵活调度优势，利用SNMP协议和OpenFlow协议对服务器的运行状态和全局网络负载信息进行实时监测，并通过权值计算的方式选择出权重最高的服务器作为流处理的目标服务器，在此基础上，采用最优转发路径算法进行流量调度，从而达到提高服务器集群的利用率与处理性能的目的。搭建了实验平台对SDNLB的性能进行仿真测试，实验结果表明：在相同的网络负载条件下，SDNLB与其他负载均衡算法相比，能够有效地降低服务器集群的负载，并能够显著提高网络吞吐量和带宽利用率，缩短流的完成时间和平均时延。
关键词:软件定义网络/
OpenFlow/
服务器集群/
负载均衡/
流量工程
Abstract:Under the present network architecture, it is disadvantageous for scalability and service performance of server cluster to adopt hardware systems to realize load balancing of server cluster, because there are some restriction factors in such a method, including the difficulty of acquiring load nodes status and the complexity of redirecting traffic, etc. To solve the problem, a Load Balancing mechanism based on Software-Defined Networking (SDNLB) is proposed. With superiorities of SDN such as centralized control and flexible traffic scheduling, SDNLB monitors run states of servers and overall network load information by means of SNMP protocol and OpenFlow protocol in real time, and chooses the highest weight server as target server aiming for processing coming flows through the way of weight value calculation. On this basis, SDNLB takes full advantage of the optimal forwarding path algorithm to carry on traffic scheduling, and achieves the goal that raises utilization rate and processing performance of server cluster. An experiment platform is built to carry out simulation tests for overall performance of SDNLB, and the experiment results show that under the same network load conditions, SDNLB lowers effectively loads of server cluster, noticeably raises network throughput and bandwidth utilization, and reduces finish time and average latency of flows, compared with other load balancing algorithms.
Key words:Software-Defined Networking (SDN)/
OpenFlow/
Server cluster/
Load balancing/
Traffic engineering
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