Heuristic Algorithm for Robust Approximate Hurst Parameter Estimation with Wavelet Analysis and Neural Networks

Fast and robust Hurst parameter estimation of traffic data traces tops the bill of nowadays problems of the field of traffic engineering. Almost every existent approach fits up the goal of as far as possible precise H parameter estimation; however this option is not as necessary as approximate estimation of boundaries of H parameter if traffic demonstrates long range dependence. Often this is the satisfactory condition for defining adequate traffic engineering operations. In this paper we verify a possibility of heuristic H parameter estimation algorithm which is based on wavelet transform of fractional Brownian motion synthesized data traces, and forthcoming operating with neural network learning capabilities. In this paper algorithm is described. Experimental data are depicted and future research subjects are pointed

Heuristic Algorithm for Optimal LSP Set up Policy in MPLS Networks

In present time there is very active research in the field of multiprotocol label switching (MPLS), and lots of networks are supporting MPLS. In this paper, we verify a possibility of a heuristic LSP setup policy algorithm, which uses optimization procedure based on multi-objective model with Pareto ranking and Genetic Algorithm. Adaptation to variable and bursty traffic is achieved by using Neural Network learning capabilities, and decision making under uncertainty is made up with Fuzzy Logical approach. This algorithm functions in two phases – learning and operating, which are accomplished consecutive. In this paper algorithm is described and depicted. Experimental data are depicted and future research subjects are pointed

Faziloģikas balstīta piekļuves vadība MPLS-TE/GMPLS tīklos

Šī promocijas darba mērķis bija izveidot jaunu fazi-CAC projektēšanas metodi un novērtēt konstruētā fazi-CAC risinājuma pielietošanas iespējas MPLS-TE (GMPLS) trafika pārraides/vadības sistēmā, modificējot RSVP-TE protokola darbību, šādā veidā nodrošinot dinamisko un selektīvo LSP iestatīšanu. Balstoties uz darbā definēto fazi-CAC fazi izvedumu sistēmas (FIS) modeli un MPLS-TE tīkla modeli, kā arī vairāku lielu starptautisko sakaru pakalpojumu sniedzēju SLA, ar heiristisku pētījuma metodi tiek atrasta fazi izvedumu sistēma, kas nodrošina vēlamo fazi-CAC veiktspēju. FIS heiristiskai izvēlei tiek izmantots MPLS-TE modeļa simulācijas rīks. Darba izstrādes gaitā tika izveidots MPLS-TE eksperimentāls tīkls uz Cisco 2800 sērijas maršrutētāju bāzes, maršrutētāja saskarnes un vadības modulis, fazi-CAC kontroles modulis RSVP-TE protokola vadībai, kā arī trafika ģenerācijas un analīzes moduļi. Rezultātā ir pierādīta fazi-CAC efektivitāte salīdzinājumā ar klasisko sliekšņa-CAC algoritmu, kas tiek pielietots RSVP-TE vadībai MPLS-TE tīklos. Promocijas darbs balstās uz eksperimentāliem rezultātiem fazi-CAC pielietošanā RSVP-TE protokola vadībai MPLS-TE tīklā. Darba apjoms – 174 lpp, 90 attēli, 20 tabulas, 2 pielikumi un 148 literatūras avoti

Mobile Application Based Traffic Advisory System for General Aviation – Is It Possible?

Abstract — In this paper we present our solution of application based traffic advisory system for general aviation. Our system uses online telemetry data from an Android device which is located in a light aircraft and a server side fuzzy-logic based decision making software which is located on the ground. Data communication is provided by the means of existing mobile networks. This paper contains research results which examines the possibility of online data communication between a light aircraft and GSM or CDMA mobile networks. We also provide extensive our traffic advisory system solution technical description

Fuzzy Approach for QoS Aware Application Driven Traffic Control in GMPLS Networks

The ITU-T defined next generation network (NGN) architecture designates the resource admission control function to perform the application-driven Quality of Service (QoS) control across access and core networks. The Generalized Multiprotocol Label Switching (GMPLS) was introduced by the Internet Engineering Task Force (IETF) to cope with new traffic engineering challenges in fast optical networks and provide them with reliable end-to-end QoS mechanisms. However, an actual Connection Admission Control (CAC) implementation inside the resource reserva-tion protocol – traffic engineering extension (RSVP-TE) in GMPLS networks does not provide the ability of effective decision making, since the applied threshold CAC lacks of the capability to consider QoS policies on GMPLS network nodes. This prevents an effective end-to-end QoS control in a fully dynamic, application driven Label Switched Path (LSP) setup scenario. This work presents a specific imple-mentation of fuzzy-CAC operating over an RSVP-TE agent in GMPLS network domain. This fuzzy-CAC implementa-tion is applied to a testbed where a client application re-quests a real-time data transfer through a GMPLS network, which results in dynamic LSP setup and exclusion. The ad-mission control is performed upon service request based on QoS class requirements and network resource availability. The differentiated traffic treatment on per-flow basis is rea-lized through employment of IF-THEN rule based expert knowledge. Effective traffic differentiation is achieved in a multi-service network scenario and thus it validates fuzzy-CAC as candidate for RSVP-TE protocol enhancement for application driven QoS provisioning in GMPLS networks

Analysis of effective fuzzy-CAC solution for proactive traffic engineering

Actual Connection Admission Control (CAC) implementation inside the RSVP-TE protocol in MPLS-TE networks is highly limited, since the applied threshold CAC cannot provide Quality of Service (QoS) aware decision making on MPLS-TE network nodes. This prevents an effective end-to-end QoS control in a fully dynamic, application driven Label Switched Path (LSP) setup scenario. Authors of this paper have developed effective and flexible Fuzzy logic driven CAC solution (Fuzzy-CAC) which is capable of sustaining the parameters of QoS within the accepted limits and provide the dynamic setup of an application-controlled LSP. In this paper we present basic results as well as more in details discuss several adaptation approaches that help Fuzzy-CAC to operate under rapidly changing traffic characteristics

Metamodelling of Queuing Systems Using Fuzzy Graphs

The dynamic behavior of queuing systems under sophisticated traffic can be analyzed using simulation models. Unfortunately, often the results are only available in a form of large datasets, which makes it hard to extract the underlying regularities. One of the interesting applications is the approximation of the behavior of simulation models, called metamodelling. The goal of this paper is to approximate the behavior of queuing systems as well as to extract some understandable knowledge about the simulation model. In this paper we present the knowledge extraction from trained Neural Networks. The underlying knowledge can be extracted from the Network in form of a Fuzzy Graph. The Fuzzy Graphs are generated using Rectangular Basis Functions of Neural Networks. The research results are illustrated with a range of experiments performed. Ill. 1, bibl.

Approximation of Internet Traffic Using Robust Wavelet Neural Networks

Robust Hurst parameter estimation of traffic data traces tops the bill of nowadays problems of the field of traffic engineering. Almost every going approach fits up the goal of as far as possible precise H parameter estimation; however this option is not as indispensable as approximate estimation of boundaries of H parameter if traffic demonstrates long range dependence. Constantly this is satisfactory condition for defining adequate traffic engineering operations. In this paper we verify a possibility of robust wavelet based H parameter estimation algorithm with ulterior traffic classification, which is based on wavelet transform of fractional Brownian motion synthesized data traces, and forthcoming wavelet coefficient clustering and operating with neural network learning capabilities. In this paper algorithm is described. Experimental data are depicted and future research subjects are pointed. Ill. 5, bibl. 1

Data Mining for Managing Intrinsic Quality of Service in MPLS

LSP set up admission control policy is one of the notable problems that have to be solved to fulfill the requirements for effective resource allocation and network utilization for appropriate QoS level. In this paper, we verify a possibility of a new LSP setup admission algorithm, which uses optimization procedure based on multi-objective model with Pareto ranking and Genetic Algorithm. Decision rules are generated with Data Mining approach by performing classification operation to the selected data. This algorithm functions in two phases – classification and operating, which are accomplished consecutive. Algorithm is described and depicted. Experimental data are depicted and future research subjects are pointed. Ill. 4, bibl. 1

Attractor Selection Algorithm’s Key Parameters Synchronization Protocol among All Routers in the Domain for Network Dynamic Resource Management

One of the key challenges in today’s Service Provider’s networks is to provide the quality of service agreed with customers not investing too much in network modernization. The key moment here is effective use of the existing resources. As traffic flow changes over time, there is a new for a method or algorithm, which will dynamically re-share resources among all devices based on changing flow. Attractor selection mechanism is one of such algorithms, which could dynamically re-share network resources even if traffic flow changes very rapidly and unpredictably. Before this algorithm is operational, some parameters should be set-up on all routers in the network domain. In this paper we will investigate how these parameters could be set up in automatic mode without network personal being involve