Application of an automatically designed fuzzy logic decision support system to connection admission control in ATM networks

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Application of learning algorithms to traffic management in integrated services networks.

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CLOUD SERVICE REVENUE MANAGEMENT

Successful Internet service offerings can only thrive if customers are satisfied with service performance. While large service providers can usually cope with fluctuations of customer visits retaining acceptable Quality of Service, small and medium-sizes enterprises face a big challenge due to limited resources in the IT infrastructure. Popular services, such as justin.tv and SmugMug, rely on external resources provided by cloud computing providers in order to satisfy their customers demands at all times. The paradigm of cloud computing refers to the delivery model of computing services as a utility in a pay-as-you-go manner. In this paper, we provide and computationally evaluate decision models and policies that can help cloud computing providers increase their revenue under the realistic assumption of scarce resources and under both informational certainty and uncertainty of customers? resource requirement predictions. Our results show that in both cases under certainty and under uncertainty applying the dynamic pricing policy significantly increases revenue while using the client classification policy substantially reduces revenue. We also show that, for all policies, the presence of uncertainty causes losses in revenue; when the client classification policy is applied, losses can even amount to more than 8%

Learning algorithms for the control of routing in integrated service communication networks

There is a high degree of uncertainty regarding the nature of traffic on future integrated service networks. This uncertainty motivates the use of adaptive resource allocation policies that can take advantage of the statistical fluctuations in the traffic demands. The adaptive control mechanisms must be 'lightweight', in terms of their overheads, and scale to potentially large networks with many traffic flows. Adaptive routing is one form of adaptive resource allocation, and this thesis considers the application of Stochastic Learning Automata (SLA) for distributed, lightweight adaptive routing in future integrated service communication networks. The thesis begins with a broad critical review of the use of Artificial Intelligence (AI) techniques applied to the control of communication networks. Detailed simulation models of integrated service networks are then constructed, and learning automata based routing is compared with traditional techniques on large scale networks. Learning automata are examined for the 'Quality-of-Service' (QoS) routing problem in realistic network topologies, where flows may be routed in the network subject to multiple QoS metrics, such as bandwidth and delay. It is found that learning automata based routing gives considerable blocking probability improvements over shortest path routing, despite only using local connectivity information and a simple probabilistic updating strategy. Furthermore, automata are considered for routing in more complex environments spanning issues such as multi-rate traffic, trunk reservation, routing over multiple domains, routing in high bandwidth-delay product networks and the use of learning automata as a background learning process. Automata are also examined for routing of both 'real-time' and 'non-real-time' traffics in an integrated traffic environment, where the non-real-time traffic has access to the bandwidth 'left over' by the real-time traffic. It is found that adopting learning automata for the routing of the real-time traffic may improve the performance to both real and non-real-time traffics under certain conditions. In addition, it is found that one set of learning automata may route both traffic types satisfactorily. Automata are considered for the routing of multicast connections in receiver-oriented, dynamic environments, where receivers may join and leave the multicast sessions dynamically. Automata are shown to be able to minimise the average delay or the total cost of the resulting trees using the appropriate feedback from the environment. Automata provide a distributed solution to the dynamic multicast problem, requiring purely local connectivity information and a simple updating strategy. Finally, automata are considered for the routing of multicast connections that require QoS guarantees, again in receiver-oriented dynamic environments. It is found that the distributed application of learning automata leads to considerably lower blocking probabilities than a shortest path tree approach, due to a combination of load balancing and minimum cost behaviour

A review of connection admission control algorithms for ATM networks

The emergence of high-speed networks such as those with ATM integrates large numbers of services with a wide range of characteristics. Admission control is a prime instrument for controlling congestion in the network. As part of connection services to an ATM system, the Connection Admission Control (CAC) algorithm decides if another call or connection can be admitted to the Broadband Network. The main task of the CAC is to ensure that the broadband resources will not saturate or overflow within a very small probability. It limits the connections and guarantees Quality of Service for the new connection. The algorithm for connection admission is crucial in determining bandwidth utilisation efficiency. With statistical multiplexing more calls can be allocated on a network link, while still maintaining the Quality of Service specified by the connection with traffic parameters and type of service. A number of algorithms for admission control for Broadband Services with ATM Networks are described and compared for performance under different traffic loads. There is a general description of the ATM Network as an introduction. Issues to do with source distributions and traffic models are explored in Chapter 2. Chapter 3 provides an extensive presentation of the CAC algorithms for ATM Broadband Networks. The ideas about the Effective Bandwidth are reviewed in Chapter 4, and a different approach to admission control using online measurement is presented in Chapter 5. Chapter 6 has the numerical evaluation of four of the key algorithms, with simulations. Finally Chapter 7 has conclusions of the findings and explores some possibilities for further work

Methods for designing and optimizing fuzzy controllers

We start by discussing fuzzy sets and the algebra of fuzzy sets. We consider some properties of fuzzy modeling tools. This is followed by considering the Mamdani and Sugeno models for designing fuzzy controllers. Various methods for using sets of data for desining controllers are discussed. This is followed by a chapter illustrating the use of genetic algorithms in designing and optimizing fuzzy controllers.Finally we look at some previous applications of fuzzy control in telecommunication networks, and illustrate a simple application that was developed as part of the present work

Congestion and admission control in WDM optical networks

The demand for more communication bandwidth and network resources, has pushed researchers to find faster and more reliable data communication networks. Wavelength division multiplexing (WDM) is a promising technology to meet such increasing demands. To make use of the WDM networks, some issues need to be dealt with. This thesis discusses three problems, constraint-based path selection. Congestion Control and Admission Control. When selecting a path between the source and destination, in which some constraints are present, the choice of the path can have dramatic effects on the Quality of Service (QoS). Three path selection algorithms are compared in order to achieve optimum path selection. These algorithms are presented and analyzed in this thesis. The algorithms do not just deal with one path selection constraint but k-constraints. Two controllers are presented: A proposed congestion controller and the second is a call admission controller in circuit switched networks. The proposed congestion control algorithm is based on the fuzzy logic technique and aims to control the congestion in a WDM network through an adequate adjustment of the delay on the calls that are in the queue of the server. The adaptive admission controller for circuit switched networks is based on the optimization of resources in the network. Numerous Simulation results are presented which show the performance of each controller

Global Research Performance on the Design and Applications of Type-2 Fuzzy Logic Systems: A Bibliometric Analysis

There has been a significant contribution to scientific literature in the design and applications of Type-2 fuzzy logic systems (T2FLS). The T2FLSs found applications in many aspects of our daily lives, such as engineering, pure science, medicine and social sciences. The online web of science was searched to identify the 100 most frequently cited papers published on the design and application of T2FLS from 1980 to 2016. The articles were analyzed based on authorship, source title, country of origin, institution, document type, web of science category, and year of publication. The correlation between the average citation per year (ACY) and the total citation (TC) was analyzed. It was found that there is a strong relationship between the ACY and TC (r = 0.91643, P<0.01), based on the papers consider in this research.  The “Type -2 fuzzy sets made simple” authored by Mendel and John (2002), published in IEEE Transactions on Fuzzy Systems received the highest TC as well as the ACY. The future trend in this research domain was also analyzed. The present analysis may serve as a guide for selecting qualitative literature especially to the beginners in the field of T2FLS

Fuzzy Controller Based QoS Routing Algorithm with a Multiclass Scheme for MANET

A mobile ad hoc network (MANET) consists of a set of mobile hosts that can communicate with each other without the assistance of base stations. Due to the dynamic nature of the network topology and restricted resources, quality of service (QoS) and multicast routing in MANET are challenging tasks which attract the interests of many people. In this paper, we present a fuzzy controller based QoS routing algorithm with a multiclass scheme (FQRA) in mobile ad hoc networks. The performance of this scheduler is studied using NS2 (Network Simulator version 2) and evaluated in terms of quantitative measures such as packet delivery ratio, path success ratio and average end-to-end delay. Simulations show that the approach is efficient, promising and applicable in ad hoc networks