The Open Flow implementations(SDNs) have been deployed increasingly on varieties of networks in research institutions as well as commercial institutions. To develop an Open Flow implementation, it is required to understand the performance of the network. A few benchmark tools(e.g., Cbench and OFlops) can be used to measure the network performance, while these tools take considerable time to simulate traffic behaviors and generate the required results,therefore extending the development time. In this paper, we present an analytical model, which is based on stochastic network calculus theory, for evaluating the performance of switch to controller.The previous studies show that stochastic network calculus can provide realistic emulation of real network traffic behaviors. Our model is evaluated by using both simulation tool and realistic testbed.The results show the stochastic network calculus based analysis model can realistically measure the network performance of the end-to-end properties between controller and switch.
LIN ChangtingWU ChunmingHUANG MinWEN ZhenyuZHENG Qiuhua
In a data center network （DCN）, load balancing is required when servers transfer data on the same path. This is necessary to avoid congestion. Load balancing is challenged by the dynamic transferral of demands and complex routing control. Because of the distributed nature of a traditional network, previous research on load balancing has mostly focused on improving the performance of the local network; thus, the load has not been optimally balanced across the entire network. In this paper, we propose a novel dynamic load-balancing algorithm for fat-tree. This algorithm avoids congestions to the great possible extent by searching for non-conflicting paths in a centralized way. We implement the algorithm in the popular software-defined networking architecture and evaluate the algorithm＇ s performance on the Mininet platform. The results show that our algorithm has higher bisection band- width than the traditional equal-cost multi-path load-balancing algorithm and thus more effectively avoids congestion.
When applying Software-Defined Networks(SDN) to WANs,the SDN flexibility enables the cross-domain control to achieve a better control scalability.However,the control consistence is required by all the cross-domain services,to ensure the data plane configured in consensus for different domains.Such consistence process is complicated by potential failure and errors of WANs.In this paper,we propose a consistence layer to actively and passively snapshot the cross-domain control states,to reduce the complexities of service realizations.We implement the layer and evaluate performance in the PlanetLab testbed for the WAN emulation.The testbed conditions are extremely enlarged comparing to the real network.The results show its scalability,reliability and responsiveness in dealing with the control dynamics.In the normalized results,the active and passive snapshots are executed with the mean times of 1.873 s and 105 ms in135 controllers,indicating its readiness to be used in the real network.
Providing services on demand is a major contributing factor to drive the increasingly development of the software defined network. However, it should supply all the current popular applications before it really attains widespread development. Multiple Description Coding(MDC) video applications, as a popular application in the current network, should be reasonably supported in this novel network virtualization environment. In this paper, we address this issue to assign MDC video application into virtual networks with an efficient centralized algorithm(CAMDV). Since this problem is an NP-hard problem, we design an algorithm that can effectively balance the user satisfaction and network resource cost. Previous work just builds a global multicast tree for each description to connect all the destination nodes by breadth-first search strategy or shortest path tree algorithm. But those methods could not achieve an optimal balance or a high-level user satisfaction. By introducing the hierarchical clustering scheme, our algorithm decomposes the whole mapping procedure into multicast tree construction and multipath description distribution. A serial of simulation experiments show that our centralized algorithm could achieve a better performance in balancing the user satisfaction and average mapping cost in comparison with its rivals.
