Although the use of software-defined networking (SDN) enables routes of packets to be controlled with finer granularity (down to the individual flow level) by using traffic engineering (TE) and thereby enables better balancing of the link loads, the corresponding increase in the number of states that need to be managed at routers and controller is problematic in large-scale networks. Aggregating flows into macro flows and assigning routes by macro flow should be an effective approach to solving this problem. However, when macro flows are constructed as TE targets, variations of traffic rates in each macro flow should be minimized to improve route stability. We propose two methods for generating macro flows: one is based on a greedy algorithm that minimizes the variation in rates, and the other clusters micro flows with similar traffic variation patterns into groups and optimizes the traffic ratio of extracted from each cluster to aggregate into each macro flow. Evaluation using traffic demand matrixes for 48 hours of Internet2 traffic demonstrated that the proposed methods can reduce the number of TE targets to about 1/50 ∼ 1/400 without degrading the link-load balancing effect of TE.

In recent years, the time variation of Internet traffic has increased due to the growth of streaming and cloud services. Backbone networks must accommodate such traffic without congestion. Traffic engineering with traffic prediction is one approach to stably accommodating time-varying traffic. In this approach, routes are calculated from predicted traffic to avoid congestion, but predictions may include errors that cause congestion. We propose prediction-based traffic engineering that is robust against prediction errors. To achieve robust control, our method uses model predictive control, a process control method based on prediction of system dynamics. Routes are calculated so that future congestion is avoided without sudden route changes. We apply calculated routes for the next time slot, and observe traffic. Using the newly observed traffic, we again predict traffic and re-calculate the routes. Repeating these steps mitigates the impact of prediction errors, because traffic predictions are corrected in each time slot. Through simulations using backbone network traffic traces, we demonstrate that our method can avoid the congestion that the other methods cannot.

Traffic engineering refers to techniques to accommodate traffic efficiently by dynamically configuring traffic routes so as to adjust to changes in traffic. If traffic changes frequently and drastically, the interval of route reconfiguration should be short. However, with shorter intervals, obtaining traffic information is problematic. To calculate a suitable route, accurate traffic information of the whole network must be gathered. This is difficult in short intervals, owing to the overhead incurred to monitor and collect traffic information. In this paper, we propose a framework for traffic engineering in cases where only partial traffic information can be obtained in each time slot. The proposed framework is inspired by the human brain, and uses conditional probability to make decisions. In this framework, a controller is deployed to (1) obtain a limited amount of traffic information, (2) estimate and predict the probability distribution of the traffic, (3) configure routes considering the probability distribution of future predicted traffic, and (4) select traffic that should be monitored during the next period considering the system performance yielded by route reconfiguration. We evaluate our framework with a simulation. The results demonstrate that our framework improves the efficiency of traffic accommodation even when only partial traffic information is monitored during each time slot.

To efficiently use network resources, internet service providers need to conduct traffic engineering that dynamically controls traffic routes to accommodate traffic change with limited network resources. The performance of traffic engineering (TE) depends on the accuracy of traffic prediction. However, the size of traffic change has been drastically increasing in recent years due to the growth in various types of network services, which has made traffic prediction difficult. Our approach to tackle this issue is to separate traffic into predictable and unpredictable parts and to apply different control policies. However, there are two challenges to achieving this: dynamically separating traffic according to predictability and dynamically controlling routes for each separated traffic part. In this paper, we propose a macroflow-based TE scheme that uses different routing policies in accordance with traffic predictability. We also propose a traffic-separation algorithm based on real-time traffic analysis and a framework for controlling separated traffic with software-defined networking technology, particularly OpenFlow. An evaluation of actual traffic measured in an Internet2 network shows that compared with current TE schemes the proposed scheme can reduce the maximum link load by 34% (at the most congested time) and the average link load by an average of 11%.