Software-Defined Networking (SDN) can be applied for managing application flows dynamically by a logically centralized SDN controller and SDN switches. Because one SDN switch can support just a few thousand forwarding rule installations per second, it is a barrier to dynamic and scalable application flow management. For this reason, it is essential to reduce the number of application flows if they are to be successfully managed. Nowadays, since much attention has been paid to developing a network service that reduces application delay, the allowable delay of application flows has become an important factor. However, there has been no work on minimizing the number of flows while satisfying end-to-end delay of flows. In this paper, we propose a method that can aggregate flows and minimize the number flows in a network while ensuring all flows satisfy their allowable delay in accordance with QoS or SLA. Since the problem is classified as NP-hard, we propose a heuristic algorithm. We compared the aggregation effect of the proposed method, simple aggregation method and optimal solution by simulation. In addition, we clarify the characteristics of the proposed method by performing simulations with various parameter settings. The results show that the proposed method decreases the number of rules than comparative aggregation method and has very shorter computational time than optimal solution.

One drawback of Integrated service architecture is the scaling problem. Therefore, flow aggregation is an important solution for supporting quality of service in large-scale network. In advance reservation, priori information of advance-reserved requests can be used for flow aggregation before their initiation time. However, an impolitic aggregation can lead to violate admission control. In this paper, we propose an effective algorithm to aggregate advance-reserved requests with guaranteed delay. The proposed algorithm not only can reduce the amount of state in core network but also minimize the bandwidth consumption. The simulation result indicates that the state in the core network can be reduced as low as 17.3% even in the worst case.

A rapid growth of the Internet and proliferation of new multimedia applications lead to demands of high speed and broadband network technologies. Routers are also necessary to follow up the growth of link bandwidths. From this reason, there have been many researches on high speed routers having switching capabilities. To have an expected effect, however, a control parameters set based on traffic characteristics are necessary. In this paper, we analyze the network traffic using the network traffic monitor and investigate the Internet traffic characteristics through a statistical analysis. We next show the application of our analytical results to parameter settings of high speed switching routers. Simulation results show that our approach makes highly utilized VC space and high performance in packet processing delay. We also show the effect of flow aggregation on MPLS. From our results, the flow aggregation has a great impact on the performance of MPLS.

In this paper, we propose flow aggregation algorithms for multicast video transport. Because of heterogeneities of network/client environments and users' preference on the perceived video quality, various QoS requirements must be simultaneously guaranteed even for the single video source in the multicast connection. It is easy but ineffective to provide many video streams according to each user's request. Our flow aggregation algorithm arranges similar QoS requirements of clients into a single QoS requirement, by which the required number of video streams that the video server prepares can be decreased. Then the total amount of the required bandwidth can be reduced by sharing the same video stream among a number of clients. Our flow aggregation algorithm has two variants, which are suitable to sender-initiated and receiver-initiated multicast connections, respectively. Proposed algorithms are evaluated and compared through simulation. Then we show that the server-initiated flow aggregation (an ideal case in our approach) is most effective, but the receiver-initiated flow aggregation can also offer a reasonably effective mechanism.