WOSPF: A Traffic Engineering Solution for OSPF Networks

Wide spreading of computer networks along with increasing traffic demand throughout the Internet caused a dramatic increase in energy consumption by networking devices and Internet infrastructure. Energy-aware traffic engineering is a promising approach towards green networking to achieve a trade-off between energy saving and network utilisation in backbone networks. In this paper, we propose to use non-dominated sorting genetic algorithm (NSGA-II) for energy-aware intra-domain traffic engineering. This algorithm tries to make a trade-off between maximum link utilisation (MLU) and energy reservation. For each pair of network topology and traffic matrix, NSGA-II computes the optimal set of links to put to sleep so that the resulting topology would be able to carry the traffic demand. We developed a simulator to evaluate the performance of our mechanism. The results of comprehensive evaluations show that our energy-aware TE approach increases the network performance in terms of energy conservation by 50% at the cost of slight increase in maximum link utilisation.

Multicast transmission corresponds to send data to several destinations often involving requirements of Quality of Service (QoS) and Traffic Engineering (TE). This work investigates new evolutionary models to tackle Multicast Flow Routing in a Pareto multiobjective perspective. Two multiobjective evolutionary algorithms (SPEA and SPEA2) were applied as the underlying search of such models. QoS and TE requirements are also considered in the route calculus by optimizing four objectives - maximum link utilization, total cost, maximum end-to-end delay and hops count - attending a link capacity constraint. Besides, three heuristics for subtrees reconnection to be used on crossover and mutation operators are investigated. The first heuristic uses a shortest path algorithm to improve the convergence; the second one employs a random search to reconnect subtrees into a valid route and the third one mixes the other two combining the good skills of each one. The resultant evolutionary environments were evaluated using four multiobjective metrics: two for convergence and two for diversity. SPEA2 reached better results than SPEA on the vast majority cases. The design of crossover and mutation operators that provide more diversity lead to very good improvements on both multiobjective goals - convergence and diversity - being that the heuristic which combines random and shortest path reconnections and returned the best results.

Objectives: Nowadays the Internet has become vital to each and every one. It is sensitive to link failures and node failures due to many reasons in the network connectivity. Any change in the node or link may change the routing table of many nodes. Due to this change, the routing table of many nodes may be unstable. These failures lead to increase the convergence time of network. The paper focused on reducing the convergence time of network. Methods -In this research paper, we have proposed a novel approach that keeps the value of Minimum Route Advertisement Interval (MRAI) timer fickle. In this approach, depending on the position of receiver, the value of the MRAI timer is varying. In this research, if more than one route is available to reach the destination then we have used the soft computing approach so that the load is balanced over all the paths that are reaching to the destination to avoid the congestion. Result: The approach used in this paper focuses on fast network convergence and maximum link utilization. The Fickle MRAI used in the network for reducing the convergence time up to 5 second, also the use of load balancing approach to improve link utilization of the network. Conclusion: The proposed work improves fast network convergence and utilization of bandwidth as the traffic flows from the entire shortest paths in the network.

Existing traffic engineering (TE) solutions performs well for software defined network (SDN) in average cases. However, during peak hours, bursty traffic spikes are challenging to handle, because it is difficult to react in time and guarantee high performance even after failures with limited flow entries. Instead of leaving some capacity empty to guarantee no congestion happens due to traffic rerouting after failures or path updating after demand or topology changes, we decide to make full use of the network capacity to satisfy the demands for heavily-loaded peak hours. The TE system also needs to react to failures quickly and utilize the priority queue to guarantee the transmission of loss and delay sensitive traffic. We propose TED, a scalable TE system that can guarantee high throughput in peak hours. TED can quickly compute a group of maximum number of edge-disjoint paths for each ingress-egress switch pair. We design two methods to select paths under the flow entry limit. We then input the selected paths to our TE to minimize the maximum link utilization. In case of large traffic matrix making the maximum link utilization larger than 1, we input the utilization and the traffic matrix to the optimization of maximizing overall throughput under a new constrain. Thus we obtain a realistic traffic matrix, which has the maximum overall throughput and guarantees no traffic starvation for each switch pair. Experiments show that TED has much better performance for heavily-loaded SDN and has 10% higher probability to satisfy all (> 99.99%) the traffic after a single link failure for G-Scale topology than Smore under the same flow entry limit.

We consider the problem of packet scheduling in a network with small router buffers. The objective is to provide a statistical bound on the worst-case packet loss rate for a traffic aggregate (connection) routed along any network path, given maximum permissible link utilization (load). This problem is argued to be of interest in networks providing statistical loss-rate guarantees to ingress-egress connections with fixed bandwidth demands. We introduce a scheduling algorithm for networks using per packet transmission reservation. Reservations allow loss guarantees at the aggregate level to hold for individual flows within the aggregate. The algorithm employs randomization and traffic regulation at the ingress, and batch local scheduling at the links. It ensures that a large fraction of packets from each connection are consistently subject to small loss probability at every link. These packets are therefore likely to survive long paths. To obtain the desired loss-rate bound, we analyze the performance of the algorithm under global routing and bandwidth allocation scenarios that maximize the loss rate of a connection routed along an arbitrary network path. We compare the bound to that obtained using the scheduling algorithm that combines the FCFS service discipline and the drop-tail policy. We find that the proposed algorithm significantly improves the constraints on link utilization and path length necessary to achieve strong loss-rate guarantees

Traffic Engineering (TE) is important and necessary in order to fully utilize the existing network resources and reduce capital expenditure. Given its importance, many algorithms have been proposed in the literature. Unfortunately, there is still not a consistent methodology to evaluate these algorithms. Worse yet, some variants of the traffic engineering problem are known to be NP-hard. Thus, given a particular TE problem, in general, it is not possible to know the optimal solution; hence, it is difficult to assess how a particular heuristic algorithm performs. Even though several heuristic algorithms could be used to validate each other, the best solution from these algorithms could still be very far away from the optimal solution. We propose a novel methodology to evaluate TE algorithms. In this methodology, we construct TE problems with known optimal solutions and we then use these TE problem instances to test the performance of TE algorithms. We found that some TE algorithms perform poorly, and the result deviates from the optimum further as the problem size gets bigger. Our results suggest that there is large room for algorithm improvements and further research is required. Even though we only demonstrate the power of the methodology in the context of traffic engineering algorithms, the methodology is general enough that it could be applied in many other areas as well.

In this study, we develop a novel adaptive Multi-Objective Optimization (MOO) algorithm to jointly minimize the Power Consumption (PC) and Maximum Link Utilization (MLU) of wired computer networks. This novel algorithm, based on Non-dominated Sorting Genetic Algorithm II (NSGA-II), is able to discover the optimal link weights configuration for wired networks based on live network traffic data. The goal is to minimize both PC and MLU when this optimal link weights configuration is applied to the network. The impact of the Internet and wired networks’ growing power consumption on costs and environment continues to be a major concern of network operators. Our adaptive MOO scheme can be applied to any wired network to reduce its power consumption with acceptable load balancing to help achieve greener energy-efficient networks. In order to validate our solution, we run experiments on two different network topologies with various network traffic data to compare the performance of three different methods of updating the link weights: random, delta-weight, and hybrid. The experiment results show that all three methods can find optimal solutions to achieve both reduced PC and MLU. Compared to the default link weights configuration, the optimal solution found by hybrid approach is the best since it can reduce the PC by 35.24%, while reducing MLU by 42.86% for specific traffic pattern on Abilene network topology.

In this paper, we propose a multi-objective traffic engineering scheme using different distribution trees to multicast several flows. The aim is to combine into a single aggregated metric, the following weighting objectives: the maximum link utilization, the hop count, the total bandwidth consumption, and the total end-to-end delay. Moreover, our proposal solves the traffic split ratio for multiple trees. We formulate this multi-objective function as one with Non Linear programming with discontinuous derivatives (DNLP). Results obtained using SNOPT solver show that several weighting objectives are decreased and the maximum link utilization is minimized. The problem is NP-hard, therefore, a novel SPT algorithm is proposed for optimizing the different objectives. The behavior we get using this algorithm is similar to what we get with SNOPT solver. The proposed approach can be applied in MPLS networks by allowing the establishment of explicit routes in multicast events. The main contributions of this paper are the optimization model and the formulation of the multi-objective function; and that the algorithm proposed shows polynomial complexity.

Open Shortest Path First (OSPF) is one of the most widely used intra-domain routing protocol. It is well known that OSPF protocol does not provide flexibility in terms of packet forwarding to achieve any network optimization objective. Because of the high cost of network assets and commercial and competitive nature of Internet service provisioning, service providers are interested in performance optimization of their networks. This helps in reducing congestion hotspots and improving resource utilization across the network, which, in turn, results in an increased revenue collection. One way of achieving this is through Traffic Engineering. Currently traffic engineering is mostly done by using MPLS. But legacy networks running OSPF would need to be upgraded to MPLS. To achieve better resource utilization without upgrading OSPF network to MPLS is a challenge. In this paper we present a simple but effective algorithm, called Smart OSPF (S-OSPF) to provide traffic engineering solution in an OSPF based best effort network. We formulate an optimization problem based on the traffic demand to minimize the maximum link utilization in the network. Routing of the traffic demand is achieved using OSPF. We have simulated S-OSPF on real networks of two service providers. Simulation results show that S- OSPF based traffic engineering solution performance very closely follows the optimal solution.

The rapid growth and increasing requirements for service quality, reliability had made everyone to choose the traffic engineering essential for the large public networks. The multi Protocol label switching is a protocol used when there is interaction between the traffic engineering and the overlay networks. This MultiProtocol Labeled Switching (MPLS) with the help of the nash equilibrium algorithm has given optimized performance better by reducing the latency and increasing the link utilization. But increase in link utilization will gradually increase the congestion in the network as the link is being used by all the nodes. So, to overcome this specific problem we have performed a formulation called dual programming formulation which has set of constraints that have to be satisfied, along with Open Shortest Path First(OSPF) protocol so as to reduce the maximum link utilization. We have simulated the results in five queuing models which includes BPR,PD1,PM1,MM1,MD1 using source, compliant, optimal overlay routing. The results are simulated using Ns2 with linux as platform.Link utilization is reduced based on the parameters such as traffic demands and the distance between the nodes with their respective demands.

This paper focus on analyzing traffic characterization of a large-scale access network based on active IP address count and link utilization. We find out that the behavior of internal and external hosts is asymmetric, the utilization of internal IP addresses per day is about 18.64% and the maximum link utilization is about 53%. Based on these, we proposed a scheme for detecting traffic anomalies which is proved to be powerful than another work.

This paper considers explicit routing algorithms for Internet traffic engineering. Explicit routing is seen to be a much more capable solution for improving network utilization than the current destination-based routing and the multi-protocol label switching (MPLS) standard has made explicit routes implementable. ISP can now have fine granularity control over the traffic distribution across their backbones by carefully overlaying explicit routes over the physical network. The basic traffic engineering problem is how to set up explicit routes to meet bandwidth demands between the edge nodes of the network and at the same time to optimize the network performance. We model the traffic engineering problem as an optimization problem with the objective of minimizing congestion and maximizing potential for traffic growth. We present two mathematical formulations, one linear programming for the case of allowing demand bifurcation and one integer programming for the case of disallowing demand bifurcation. While the bifurcation case can be solved to optimality, we show that the non-bifurcation case is NP-hard. Four heuristic schemes are proposed for the non-bifurcation case, with the most sophisticated one being based on re-routing of split demands in the optimal solution of the bifurcation case. The performance of these heuristic schemes are tested in a large backbone topology. Our results show that shortest-path and minimum hop algorithms, although widely used in current routing protocols, perform poorly, white the re-routing approach performs best.

Software Defined Networking (SDN) is an emerging network architecture that separates the control plane from the data plane to simplify and improve network management with a high degree of flexibility. Network optimization under traffic uncertainties is one of the most challenging topics in communication networks to optimize network performance and traffic delivery. Although the traffic optimization technology has been extensively studied in the industry, a traffic optimization solution different from the traditional network is needed in the SDN network, which can utilize global network information and traffic characteristics to control and manage traffic in a better way. In this paper, a Mixed Linear Geometric Programming Traffic Optimization Algorithm (MLGP-TOA) is proposed for the problem of traffic uncertainties in SDN. Aiming at minimizing the maximum link utilization (MLU), the initial problem is transformed into a convex optimization problem by monomial approximation and variable substitution. Then, the inner point method is used to find the global optimal solution, and the optimal split ratio at each node is obtained. Finally, the configuration information is sent to the data plane. The simulation results show that the algorithm can reduce MLU, so that the traffic can fully utilize network resources and avoid congestion.

In Wireless Sensor Network (WSN), resource optimization is one of the challenging issues. It improves the network performance. This can be achieved with the help of a new networking technology, called Software Defined Networking (SDN). The concept of SDN is to decouple the control plane from the forwarding plane of a network to improve the performance of the networking applications. Traffic Engineering (TE) is a domain of network applications that manages the network traffic, and try to optimize the network resources. SDN offers multiple features to support TE applications. SDN combined with WSN forms a new networking domain called Software Defined WSN (SDWSN). Since WSN has resource-constrained nodes, resource optimization is a need in WSN. This paper proposes an optimized link utilization based approach for TE in SDWSN. The proposed approach discusses about a traffic optimization algorithm, called Optimized Link Allocation based TE (OLA-TE). OLA-TE is implemented at the controller node and helps to improve the overall network utilization. The SDN sensor nodes find the optimized path for traffic routing with the help of controller node. The proposed approach is compared with the traditional WSN approach. IBM CPLEX Optimization Studio and MATLAB are used for validation purposes. The CPLEX results assure a network flow-cost optimization of about 10%. The results of MATLAB show that despite having extra control traffic on network, the network performance gets improved over the period of time.

In this paper, we propose a multi-objective traffic engineering scheme using different distribution trees to dynamic multicast groups (in which egress nodes can change during the connection's lifetime). If a multicast tree is recomputed from scratch, it may consume a considerable amount of CPU time and all communication using the multicast tree will be temporarily interrupted. To alleviate these drawbacks we propose a dynamic optimization model that makes use of a previously computed multicast tree (static model) in order to add new egress nodes. Using these two models, our aim is to combine into a single aggregated metric, the following weighting objectives: maximum link utilization, hop count, total bandwidth consumption and total end-to-end delay. Moreover, our proposal solves the traffic split ratio for multiple trees. We compare the dynamic and static multicast routing models using SNOPT solver. The proposed approach can be applied in MPLS networks by allowing explicit routes to be established in multicast events.