Fejer Type Inequalities for (s,m)-Convex Functions in Second Sense

In this paper, we consider (s,m)-convex functions in second sense which were introduced and studied by N. Eftekhari . We prove several F´ejer-Hermite-Hadamard type integral inequalities for (s,m)-convex functions in second sense. Our results include several new and known results as special cases.We expect that the ideas and techniques used in this paper may inspire interested readers to explore some new applications of these newly introduced functions in various fields of pure and applied sciences

High-Rate, Reliable Communications with Hybrid Space-Time Codes

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Internet traffic forecasting using neural networks

The forecast of Internet traffic is an important issue that has received few attention from the computer networks field. By improving this task, efficient traffic engineering and anomaly detection tools can be created, resulting in economic gains from better resource management. This paper presents a Neural Network Ensemble (NNE) for the prediction of TCP/IP traffic using a Time Series Forecasting (TSF) point of view. Several experiments were devised by considering real-world data from two large Internet Service Providers. In addition, different time scales (e.g. every five minutes and hourly) and forecasting horizons were analyzed. Overall, the NNE approach is competitive when compared with other TSF methods (e.g. Holt-Winters and ARIMA).Engineering and Physical Sciences Research Council (EP/522885 grant).Portuguese National Conference of Rectors (CRUP)/British Council Portugal (B-53/05 grant).Nuffield Foundation (NAL/001136/A grant)

Class-based OSPF traffic engineering inspired on evolutionary computation

This paper proposes a novel traffic engineering framework able to automatically provide near-optimal OSPF routing configurations for QoS constrained scenarios. Within this purpose, this work defines a mathematical model able to measure the QoS compliance in a class-based networking domain. Based on such model, the NP-hard optimization problem of OSPF weight setting is faced resorting to Evolutionary Algorithms. The presented results show that, independently of other QoS aware mechanisms that might be in place, the proposed framework is able to improve the QoS level of a given domain only taking into account the direct influence of the routing component of the network. The devised optimization tool is able to optimize OSPF weight configurations in scenarios either considering a single level of link weights or using multiple levels of weights (one for each class) in multi-topology routing scenarios

Multiconstrained optimization of networks with multicast and unicast traffic

This paper presents an OSPF routing optimization framework taking into account a set of multiconstrained QoS requirements of the networking domain. The proposed optimization approach, based on Evolutionary Computation, is able to handle network scenarios with both unicast and multicast traffic, providing high quality configurations for single-topology or multi-topology routing approaches. The results clearly show the effectiveness of the devisedoptimization methods, allowing for the development of management tools automatically providing enhanced configurations to improve the QoS performance of the network.Fundação para a Ciência e a Tecnologia (FCT) - PTDC/EIA/64541/2006

Topology aware Internet traffic forecasting using neural networks

Forecasting Internet traffic is receiving an increasing attention from the computer networks domain. Indeed, by improving this task efficient traffic engineering and anomaly detection tools can be developed, leading to economic gains due to better resource management. This paper presents a Neural Network (NN) approach to predict TCP/IP traffic for all links of a backbone network, using both univariate and multivariate strategies. The former uses only past values of the forecasted link, while the latter is based on the neighbor links of the backbone topology. Several experiments were held by considering real-world data from the UK education and research network. Also, different time scales (e.g. every ten minutes and hourly) were analyzed. Overall, the proposed NN approach outperformed other forecasting methods (e.g. Holt-Winters).R&D Algoritmi centr

Evolutionary computation for quality of service internet routing optimization

In this work, the main goal is to develop and evaluate a number of optimization algorithms in the task of improving Quality of Service levels in TCP/IP based networks, by configuring the routing weights of link-state protocols such as OSPF. Since this is a complex problem, some meta-heuristics from the Evolutionary Computation arena were considered, working over a mathematical model that allows for flexible cost functions, taking into account several measures of the network behavior such as network congestion and end-to-end delays. A number of experiments were performed, resorting to a large set of network topologies, where Evolutionary Algorithms (EAs), Differential Evolution and some common heuristic methods including local search were compared. EAs make the most promising alternative leading to solutions with an effective network performance even under unfavorable scenarios

Efficient OSPF weight allocation for intra-domain QoS optimization

This paper presents a traffic engineering framework able to optimize OSPF weight setting administrative procedures. Using the proposed framework, enhanced OSPF configurations are now provided to network administrators in order to effectively improve the QoS performance of the corresponding network domain. The envisaged NP-hard optimization problem is faced resorting to Evolutionary Algorithms, which allocate OSPF weights guided by a bi-objective function. The results presented in this work show that the proposed optimization tool clearly outperforms common weight setting heuristics and, even under unfavorable scenarios, effective QoS improvement is achieved in the network domain.(undefined

Quality of service constrained routing optimization using evolutionary computation

In this work, a novel optimization framework is proposed that allows the im- provement of Quality of Service levels in TCP/IP based networks, by configuring the routing weights of link-state protocols such as OSPF. Since this is a NP-hard problem, some algorithms from Evolutionary Computation were considered, work- ing over a mathematical model that allows the definition of flexible cost functions that can take into account several measures of the network behaviour, such as net- work congestion and end-to-end delays. A number of experiments were performed, over a large set of network topologies, where Evolutionary Algorithms (EAs), Dif- ferential Evolution, local search methods and common heuristics were compared. EAs make the most promising alternative leading to solutions with an effective net- work performance, even under unfavourable scenarios. A number of state of the art multiobjective optimization algorithms were also tested, but the proposed EAs still hold as the most consistent method for network optimization.Fundação para a Ciência e a Tecnologia (FCT) - Contract CONC-REEQ/443/2001British Council Portugal - B-53/05 grantPortuguese National Conference of Rectors (CRUP)Nuffield Foundation - NAL/001136/A grantEngineering and Physical Sciences Research Council - EP/522885 grantProject SeARCH (Services and Advanced Research Computing with HTC/HPC clusters