A framework for improving routing configurations using multi-objective optimization mechanisms

IP networks are nowadays well established technolo- gies being used to support a myriad of applications and services, thus assuming a crucial role in todays telecommunication sys- tems. Nevertheless, such infrastructures usually require network administrators to perform a wide set of complex planning and management tasks trying to attain adequate network configura- tions. Many of such management tasks can be mathematically for- mulated as NP-hard optimization problems, sometimes involving several objective functions. In this context, this work explores and demonstrates the potential of using computational intelligence methods as optimization engines to tackle complex network op- timization problems. In particular, Multi-objective Evolutionary Algorithms (MOEAs) are used to attain near-optimal link state routing configurations robust to distinct operational conditions. As result, network administrators will be provided with a set of alternative routing configurations representing distinct tradeoffs between the considered optimization goals. The robustness of the proposed methods is illustrated by presenting several multi-objective optimization examples able to improve the performance and resilience levels of a network infrastructure. Moreover, the devised methods are integrated in a freely available Traffic Engineering optimization framework able to be used by network administrators interested in this particular research field.This work has been supported by COMPETE: POCI-010145-FEDER-007043 and FCT Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013

Traffic engineering in ambient networks: challenges and approaches

The focus of this paper is on traffic engineering in ambient networks. We describe and categorize different alternatives for making the routing more adaptive to the current traffic situation and discuss the challenges that ambient networks pose on traffic engineering methods. One of the main objectives of traffic engineering is to avoid congestion by controlling and optimising the routing function, or in short, to put the traffic where the capacity is. The main challenge for traffic engineering in ambient networks is to cope with the dynamics of both topology and traffic demands. Mechanisms are needed that can handle traffic load dynamics in scenarios with sudden changes in traffic demand and dynamically distribute traffic to benefit from available resources. Trade-offs between optimality, stability and signaling overhead that are important for traffic engineering methods in the fixed Internet becomes even more critical in a dynamic ambient environment

A Survey of Network Optimization Techniques for Traffic Engineering

TCP/IP represents the reference standard for the implementation of interoperable communication networks. Nevertheless, the layering principle at the basis of interoperability severely limits the performance of data communication networks, thus requiring proper configuration and management in order to provide effective management of traffic flows. This paper presents a brief survey related to network optimization using Traffic Engineering algorithms, aiming at providing additional insight to the different alternatives available in the scientific literature

Моделювання балансування навантаження у мережах MPLS

Обґрунтована необхідність реалізації методів балансування навантаження в мережах MPLS з метою перенаправлення маршрутів і перерозподілу трафіку на резервні в разі виходу з ладу однієї або кількох ліній зв'язку. Наведені приклади, реалізовані за допомогою імітаційного моделювання в програмному середовищі Opnet Modeler, підтверджують теоретичні результати авторівThe necessity of implementing the methods of load balancing in MPLS networks to route diversion and redistribution of traffic to a backup in case of failure of one or more lines. The examples are implemented using the simulation software in the vehicle Opnet Modeler, our results confirm the theoretica

Load Balancing and Congestion Control using Congestion Aware Multipath Routing Protocol (CAMRP) in Wireless Networks

This paper deals with a network-transparent wireless Here,we propose a congestion aware multipath routing protocol called CAMRP (Congetstion Aware Multipath Routing Protocol). The protocol computes multiple paths using proposed congestion aware metric and performs load balancing by a pooling scheme with proper queue utilization of variable interfaces of a node. However, the effective load balancing technique constantly maintains optimal data transmission using optimal path by managing traffic in all the way through congested area. Present Wireless Communications have got the popularity due to its randomness in the deployment and immense support and compatibility for different applications. Due to these applications, the problem of network congestion arises and in turn it results lower throughput and longer delay. In many recent research works, routing protocols dealing with these problems are designed but they are not sufficent to adapt congestion and optimal link quality. Our proposed woirk deals with this problem and the simulation results using ns2 proves that our proposed work on load balancing shows better performance than the existing in terms of throughput, end-to-end delay with heavy traffic load

On the Benefit of Information Centric Networks for Traffic Engineering

Current Internet performs traffic engineering (TE) by estimating traffic matrices on a regular schedule, and allocating flows based upon weights computed from these matrices. This means the allocation is based upon a guess of the traffic in the network based on its history. Information-Centric Networks on the other hand provide a finer-grained description of the traffic: a content between a client and a server is uniquely identified by its name, and the network can therefore learn the size of different content items, and perform traffic engineering and resource allocation accordingly. We claim that Information-Centric Networks can therefore provide a better handle to perform traffic engineering, resulting in significant performance gain. We present a mechanism to perform such resource allocation. We see that our traffic engineering method only requires knowledge of the flow size (which, in ICN, can be learned from previous data transfers) and outperforms a min-MLU allocation in terms of response time. We also see that our method identifies the traffic allocation patterns similar to that of min-MLU without having access to the traffic matrix ahead of time. We show a very significant gain in response time where min MLU is almost 50% slower than our ICN-based TE method

Enhancing network transmission capacity by efficiently allocating node capability

A network s transmission capacity is the maximal rate of traffic inflow that the network can handle without causing congestion Here we study how to enhance this quantity by allocating resource to individual nodes while preserving the total amount of the resource available We propose a practical and effective scheme which redistributes node capability based on the local knowledge of node connectivity We show that our scheme enhances the transmission capacity of networks with heterogeneous structures by up to two orders of magnitude

Cautious Weight Tuning for Link State Routing Protocols

Link state routing protocols are widely used for intradomain routing in the Internet. These protocols are simple to administer and automatically update paths between sources and destinations when the topology changes. However, finding link weights that optimize network performance for a given traffic scenario is computationally hard. The situation is even more complex when the traffic is uncertain or time-varying. We present an efficient heuristic for finding link settings that give uniformly good performance also under large changes in the traffic. The heuristic combines efficient search techniques with a novel objective function. The objective function combines network performance with a cost of deviating from desirable features of robust link weight settings. Furthermore, we discuss why link weight optimization is insensitive to errors in estimated traffic data from link load measurements. We assess performance of our method using traffic data from an operational IP backbone

Online Load Balancing for Network Functions Virtualization

Network Functions Virtualization (NFV) aims to support service providers to deploy various services in a more agile and cost-effective way. However, the softwarization and cloudification of network functions can result in severe congestion and low network performance. In this paper, we propose a solution to address this issue. We analyze and solve the online load balancing problem using multipath routing in NFV to optimize network performance in response to the dynamic changes of user demands. In particular, we first formulate the optimization problem of load balancing as a mixed integer linear program for achieving the optimal solution. We then develop the ORBIT algorithm that solves the online load balancing problem. The performance guarantee of ORBIT is analytically proved in comparison with the optimal offline solution. The experiment results on real-world datasets show that ORBIT performs very well for distributing traffic of each service demand across multipaths without knowledge of future demands, especially under high-load conditions