Yuming Jiang

Bio: Yuming Jiang is an academic researcher from Norwegian University of Science and Technology. The author has contributed to research in topics: Network calculus & Quality of service. The author has an hindex of 32, co-authored 247 publications receiving 3877 citations. Previous affiliations of Yuming Jiang include Singapore Science Park & Agency for Science, Technology and Research.

Stochastic Network Calculus

Abstract: Network calculus, a theory dealing with queuing systems found in computer networks, focuses on performance guarantees. The development of an information theory for stochastic service-guarantee analysis has been identified as a grand challenge for future networking research. Towards that end, stochastic network calculus, the probabilistic version or generalization of the (deterministic) Network Calculus, has been recognized by researchers as a crucial step. Stochastic Network Calculus presents a comprehensive treatment for the state-of-the-art in stochastic service-guarantee analysis research and provides basic introductory material on the subject, as well as discusses the most recent research in the area. This helpful volume summarizes results for stochastic network calculus, which can be employed when designing computer networks to provide stochastic service guarantees. Features and Topics: Provides a solid introductory chapter, providing useful background knowledge Reviews fundamental concepts and results of deterministic network calculus Includes end-of-chapter problems, as well as summaries and bibliographic comments Defines traffic models and server models for stochastic network calculus Summarizes the basic properties of stochastic network calculus under different combinations of traffic and server models Highlights independent case analysis Discusses stochastic service guarantees under different scheduling disciplines Presents applications to admission control and traffic conformance study using the analysis results Offers an overall summary and some open research challenges for further study of the topic Key Topics: Queuing systems Performance analysis and guarantees Independent case analysis Traffic and server models Analysis of scheduling disciplines Generalized processor sharing Open research challenges Researchers and graduates in the area of performance evaluation of computer communication networks will benefit substantially from this comprehensive and easy-to-follow volume. Professionals will also find it a worthwhile reference text. Professor Yuming Jiang at the Norwegian University of Science and Technology (NTNU) has lectured using the material presented in this text since 2006. Dr Yong Liu works at the Optical Network Laboratory, National University of Singapore, where he researches QoS for optical communication networks and Metro Ethernet networks.

A basic stochastic network calculus

Abstract: A basic calculus is presented for stochastic service guarantee analysis in communication networks. Central to the calculus are two definitions, maximum-(virtual)-backlog-centric (m. b. c) stochastic arrival curve and stochastic service curve, which respectively generalize arrival curve and service curve in the deterministic network calculus framework. With m. b. c stochastic arrival curve and stochastic service curve, various basic results are derived under the (min, +)algebra for the general case analysis, which are crucial to the development of stochastic network calculus. These results include (i)superposition of flows, (ii)concatenation of servers, (iii) output characterization, (iv)per-flow service under aggregation, and (v)stochastic backlog and delay guarantees. In addition, to perform independent case analysis, stochastic strict server is defined, which uses an ideal service process and an impairment process to characterize a server. The concept of stochastic strict server not only allows us to improve the basic results (i)-(v)under the independent case, but also provides a convenient way to find the stochastic service curve of a serve. Moreover, an approach is introduced to find the m.b.c stochastic arrival curve of a flow and the stochastic service curve of a server.

Characterization of three infectious bronchitis virus isolates from China associated with proventriculus in vaccinated chickens.

Abstract: Outbreaks of an avian disease in infectious bronchitis-vaccinated chickens in China have led to the characterization of coronaviral isolates Q1, J2, and T3, which were isolated from proventricular tissues of the affected young layer flocks. Serologic analysis revealed that they could induce high titers of infectious bronchitis virus (IBV) antibodies in inoculated specific-pathogen-free (SPF) chickens in indirect enzyme-linked immunosorbent assay but were not neutralized by antisera specific to the IBV serotype M41 and the Australian T strain. In a pathogenicity experiment, the clinical signs and related gross lesions resembling those of field outbreaks were reproduced in SPF chickens, and viruses were reisolated from the damaged tissues, including trachea, proventriculus, duodenum, and cecal tonsil. Sequence data demonstrated the complete S1 amino acid sequences of these isolates were almost identical despite recovery from geographically different areas in China and had 47.3%-82.3% similarity in comparison with the 47 published S1 sequences. On the basis of genotyping and limited serology, the three isolates, which were responsible for field outbreaks of the disease, might be a new IBV variant.

Stochastic Performance Analysis of a Wireless Finite-State Markov Channel

Abstract: Wireless networks are expected to support a diverse range of quality of service requirements and traffic characteristics. This paper undertakes stochastic performance analysis of a wireless finite-state Markov channel (FSMC) by using stochastic network calculus. Particularly, delay and backlog upper bounds are derived directly based on the analytical principle behind stochastic network calculus. Both the single user and multi-user cases are considered. For the multi-user case, two channel sharing methods among eligible users are studied, i.e., the even sharing and exclusive use methods. In the former, the channel service rate is evenly divided among eligible users, whereas in the latter, it is exclusively used by a user randomly selected from the eligible users. When studying the exclusive use method, the problem that the state space increases exponentially with the user number is addressed using a novel approach. The essential idea of this approach is to construct a new Markov modulation process from the channel state process. In the new process, the multi-user effect is equivalently manifested by its transition and steady-state probabilities, and the state space size remains unchanged even with the increase of the user number. This significantly reduces the complexity in computing the derived backlog and delay bounds. The presented analysis is validated through comparison between analytical and simulation results.

Approximate Consensus in Stochastic Networks With Application to Load Balancing

Abstract: This paper is devoted to the approximate consensus problem for stochastic networks of nonlinear agents with switching topology, noisy, and delayed information about agent states A local voting protocol with nonvanishing (eg, constant) step size is examined under time-varying environments of agents To analyze dynamics of the closed-loop system, the so-called method of averaged models is used It allows us to reduce analysis complexity of the closed-loop stochastic system We derive the upper bounds for mean square distance between states of the initial stochastic system and its approximate averaged model These upper bounds are used to obtain conditions for approximate consensus achievement An application of general theoretical results to the load balancing problem in stochastic dynamic networks with incomplete information about the current states of agents and with changing set of communication links is considered The conditions to achieve the optimal level of load balancing are established The performance of the system is evaluated both analytically and by simulation

Convergence of Probability Measures

Abstract: Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Table Of Integrals Series And Products

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Convergence of probability measures

Abstract: The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

Stochastic Differential Equations

Abstract: We explore in this chapter questions of existence and uniqueness for solutions to stochastic differential equations and offer a study of their properties. This endeavor is really a study of diffusion processes. Loosely speaking, the term diffusion is attributed to a Markov process which has continuous sample paths and can be characterized in terms of its infinitesimal generator.