Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

Network security is a complex and challenging problem. The area of network defense mechanism design is receiving immense attention from the research community for more than two decades. However, the network security problem is far from completely solved. Researchers have been exploring the applicability of game theoretic approaches to address the network security issues and some of these approaches look promising. This paper surveys the existing game theoretic solutions which are designed to enhance network security and presents a taxonomy for classifying the proposed solutions. This taxonomy should provide the reader with a better understanding of game theoretic solutions to a variety of cyber security problems.

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A Survey of Game Theory as Applied to Network Security

Being able to identify the groups of clients that are responsible for a significant portion of a Web site's requests can be helpful to both the Web site and the clients. In a Web application, it is beneficial to move content closer to groups of clients that are responsible for large subsets of requests to an origin server. We introduce clusters---a grouping of clients that are close together topologically and likely to be under common administrative control. We identify clusters using a ``network-aware" method, based on information available from BGP routing table snapshots.

On Network-Aware Clustering of Web Clients

As networks become ubiquitous in people's lives, users depend on networks a lot for sufficient communication and convenient information access. However, networks suffer from security issues. Network security becomes a challenging topic since numerous new network attacks have appeared increasingly sophisticated and caused vast loss to network resources. Game theoretic approaches have been introduced as a useful tool to handle those tricky network attacks. In this paper, we review the existing game-theory based solutions for network security problems, classifying their application scenarios under two categories, attack-defense analysis and security measurement. Moreover, we present a brief view of the game models in those solutions and summarize them into two categories, cooperative game models and non-cooperative game models with the latter category consisting of subcategories. In addition to the introduction to the state of the art, we discuss the limitations of those game theoretic approaches and propose future research directions.

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Game Theory for Network Security

The explosive development of ICT (information and communication technology) industry has emerged as one of the major sources of world energy consumption. Therefore, this paper concerns about the BS (base station) energy saving issue, for most energy consumption of the communication network comes from the BSs and the core network. Particularly, we consider dynamically turning off certain BSs when the network traffic is low. Centralized and decentralized implementations are investigated. Simulations demonstrate the energy efficiency of the proposed algorithms and the tradeoff between energy saving and coverage guarantee.

Green mobile access network with dynamic base station energy saving (インターネットアーキテクチャ)

Risk assessment is a very important tool to acquire a present and future security status of the network information system. Many risk assessment approaches consider the present system security status, while the future security status, which also has an impact on assessing the system risk, is not taken into consideration. In this paper we propose a novel risk assessment model based on Markov game theory. In this model, all of the possible risk in the future will impact on the present risk assessment. The farther away from now, the smaller impact on the risk assessment it has. After acquiring the system security status, we proposed an automatic generated reinforcement scheme which will provide a great convenience to the system administrator. A software tool is developed to demonstrate the performance of the risk assessment of a network information system and a simulation example shows the effectiveness of the proposed model.

A Markov Game Theory-Based Risk Assessment Model for Network Information System

Describing the security situation and its trend is the research hotspot of network security. As a new research field, Network security situation awareness (NSSA) includes three phases: situation perception, situation evaluation and situation prediction. This paper proposes a novel approach to NSSA model. The situation evaluation model adopts a multi-perspective analysis. It uses the description of security attacks, vulnerabilities and security services to evaluate current network security situation. The situation prediction model adopts time series analysis. It uses past and current situation map to forecast future network security situation. Simulation results show that the model is suitable and efficient.

A Novel Approach to Network Security Situation Awareness Based on Multi-Perspective Analysis

Malware detection is a crucial aspect of software security. Traditional signature-based detection method cannot detect zero-day attacks and some malware adopting some circumvention techniques such as polymorphic, metamorphic, obfuscation and packer. So some anomaly-based detection techniques are introduced to overcome this drawback, but these techniques have high false alarm rate and the complexity involved in determining what features should be learned in the training phase. In order to overcome these shortcomings, we propose a malware detection system based on expert systems in this paper. This system integrates signature-based analysis and anomaly-detection technique together. The signature is anomaly behavioral signatures. Accord to expertise about malware?s major suspicious behaviors, we build the knowledge base of the expert system. And we design a behavior gathering component to intercept anomaly behaviors happened in the operating system and get significant traces leaved by malware, then present these behaviors and traces as facts. The expert system uses the knowledge base and behaviors facts to infer and give the results. This system can detect not only known malware, but some zero-day attacks using known techniques and also malware adopting low-level techniques, such as polymorphic and packer.

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Application of CLIPS Expert System to Malware Detection System

Average-cost optimization problems for a class of continuous-time Markov control processes with a compact action set have been studied The definition of a generalized average-cost Poisson equation, which can be viewed as an extension to the standard one is first given Markov performance potentials are defined as its unique solution Based on the formula of performance potentials, an average-cost optimality equation is derived and the existence of its solution is established Then, policy iteration and value iteration algorithms are proposed and their convergence discussed A numerical example for controlled closed queuing networks illustrates the application of the proposed value iteration algorithm

Performance optimization of continuous-time Markov control processes based on performance potentials

Cao's work shows that, by defining an α-dependent equivalent infinitesimal generator Aα, a semi-Markov decision process (SMDP) with both average- and discounted-cost criteria can be treated as an α-equivalent Markov decision process (MDP), and the performance potential theory can also be developed for SMDPs. In this work, we focus on establishing error bounds for potential and Aα-based iterative optimization methods. First, we introduce an α-uniformized Markov chain (UMC) for a SMDP via Aα and a uniformized parameter, and show their relations. Especially, we obtain that their performance potentials, as solutions of corresponding Poisson equations, are proportional, so that the studies of a SMDP and the α-UMC based on potentials are unified. Using these relations, we derive the error bounds for a potential-based policy-iteration algorithm and a value-iteration algorithm, respectively, when there exist various calculation errors. The obtained results can be applied directly to the special models, i.e., continuous-time MDPs and Markov chains, and can be extended to some simulation-based optimization methods such as reinforcement learning and neurodynamic programming, where estimation errors or approximation errors are common cases. Finally, we give an application example on the look-ahead control of a conveyor-serviced production station (CSPS), and show the corresponding error bounds.

Xi Hongsheng

Papers

A Markov Game Theory-Based Risk Assessment Model for Network Information System

A Novel Approach to Network Security Situation Awareness Based on Multi-Perspective Analysis

Application of CLIPS Expert System to Malware Detection System

Performance optimization of continuous-time Markov control processes based on performance potentials

Error bounds of optimization algorithms for semi-Markov decision processes