| 摘要(英) |
In the current Internet environment, all the geographic redundancy and load balancing, including Internet, media, corporate...etc, can be achieved by establishing servers that are able to provide service content. However, in view of the independence of server loading status, it is difficult to instantly determine whether it is a server malfunction from the content provider, or it is a circuit congestion occurs in the transmitting route, which result in the time uncertainty of trouble shooting under the traditional Internet structure.
To efficiently address the problems that happen when determining the quality of existing network, the programmable management of SDN(Software Defined Network) is the solution. Different from the traditional decentralized network, the network of SDN consist of Control Plane and Data Plane, operating the Controller in the Control Plane by programming it. SDN also centralizes the management of all rule tables from each independent network devices — Switch, which is only responsible for the package transmission.
The study takes the programmable SDN as the direction of the experiment. It monitors the end- to-end packet response status through the Controller, and send the data back to the central data collector to process the consensus decision-making within all controllers. Then, the result of the process will be sent to the controllers, further allowing them to modify the routing table of Open vSwitch and to transfer the flow from the determined heavy loading route to the one with lower loading. The performance analysis is conducted with using KVM simulator under three different testing environments, including Traditional Base, Longest Delay Reroute, and Consensus Base suggested from the study.
The result of the experiment shows that the Consensus base suggested by the study can correctly determine the loading status, modifying the route in Open vSwitch from heavy loading path to lowest loading path. It significantly reduced the overall responding time of the packet, and further utilized the resources from the low loading servers efficiently. |
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