Two Algorithms for Network Size Estimation for Master/Slave Ad Hoc Networks

This paper proposes an adaptation of two network size estimation methods: random tour and gossip-based aggregation to suit master/slave mobile ad hoc networks. We show that it is feasible to accurately estimate the size of ad hoc networks when topology changes due to mobility using both methods. The algorithms were modified to account for the specific constraints of master/slave ad hoc networks and the results show that the proposed modifications perform better on these networks than the original protocols. Each of the two algorithms presents strengths and weaknesses and these are outlined in this paper.Comment: 3 pages, 2 figures, submitted to ANTS'09 - Corrected typos and definition

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)

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

An evolutionary algorithm for unicast/multicast traffic engineering

A number of Traffic Engineering (TE) approaches have been recently proposed to improve the performance of network routing protocols, both developed over MPLS and intra-domain protocols such as OSPF. In this work, a TE approach is proposed for routing optimization in scenarios where unicast and multicast demands are simultaneously present. Evolutionary Algorithms are used as the optimization engine with overall network congestion as the objective function. The optimization aim is to reach a set of (near-)optimal weights to configure the OSPF protocol, both in its standard version and also considering the possibility of using multitopology variants. The results show that the proposed optimization approach is able to obtain networks with low congestion, even under scenarios with heavy unicast/multicast demands.This work was supported by the Portuguese Foundation for Science and Technology under project POSC/EIA/59899/2004, partially funded by FEDER

Automatic provisioning of QoS aware OSPF configurations

This paper presents a contribution for the development of network management tools able to automatically provide QoS aware routing configurations. In this perspective, a traffic engineering framework able to provide near-optimal OSPF configurations is presented. The devised solution takes into account the multiconstrained QoS demands of a given network domain in order to improve the quality of the OSPF weight setting process. To pursue this objective, this work resorts to Evolutionary Algorithms, which provide OSPF configurations based on a bi-objective function. The proof of concept of the proposed optimization framework resorts to a wide range of simulation studies, clearly showing the effectiveness of the devised mechanisms.Fundação para a Ciência e a Tecnologia (FCT) - Project SeARCH (Services and Advanced Research Computing with HTC/HPC clusters).Nuffield Foundation - Grant NAL/001136/A.Portuguese National Conference of Rectors (CRUP)/British Council Portugal - Grant B-53/05 grant.Engineering and Physical Sciences Research Council - Grant EP/522885 grant

Qos constrained internet routing with evolutionary algorithms

OSPFOSPF is the most common intra-domain routing protocol in Wide Area Networb. Thus, optimiaing OSPF weighb will produce tools for traflc engineering with Quality of Sewice constraints, without changing the network management madel. Evolutionary Algorithms (EAs) provide a valuable tool to face this NF-hard problem, allowing Jexibb cost functions with sweml mtrics of the network behavior: A novel framework is proposed that enriches current models for network congestion with delay constraints, setting the basis for EAs that allocate OSPF weights, guided by a bi-objective cost function. The results show that EAs make an eflcient method, outperfoming common heuristics and achieving gfective network behavior under nplfavornble scenarios.Engineering and Physical Sciences Research Council - EP/522885 grant.Portuguese National Conference of Rectors (CRUP)/British Council Portugal - B-53/05 grant.the Nufield Foundation - NAW001136/A grant.Fundação para a Ciência e a Tecnologia (FCT) - Project SeARCH (Services and Advanced Research Computing with HTC/HPC clusters), contract CONC-REEQ/443/2001

Multiscale Internet traffic forecasting using neural networks and time series methods

This article presents three methods to forecast accurately the amount of traffic in TCP=IP based networks: a novel neural network ensemble approach and two important adapted time series methods (ARIMA and Holt-Winters). In order to assess their accuracy, several experiments were held using real-world data from two large Internet service providers. In addition, different time scales (5min, 1h and 1 day) and distinct forecasting lookaheads were analysed. The experiments with the neural ensemble achieved the best results for 5 min and hourly data, while the Holt-Winters is the best option for the daily forecasts. This research opens possibilities for the development of more efficient traffic engineering and anomaly detection tools, which will result in financial gains from better network resource management.This work is supported by the FCT (Portuguese science foundation) project PTDC=EIA=64541= 2006. We would also like to thank Steve Williams from UKERNA for providing us with part of the data used in this work

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

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