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

Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving clients’ private data from being exposed to adversaries. Nevertheless, private information can still be divulged by analyzing uploaded parameters from clients, e.g., weights trained in deep neural networks. In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noise is added to parameters at the clients’ side before aggregating, namely, noising before model aggregation FL (NbAFL). First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noise. Then we develop a theoretical convergence bound on the loss function of the trained FL model in the NbAFL. Specifically, the theoretical bound reveals the following three key properties: 1) there is a tradeoff between convergence performance and privacy protection levels, i.e., better convergence performance leads to a lower protection level; 2) given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; and 3) there is an optimal number aggregation times (communication rounds) in terms of convergence performance for a given protection level. Furthermore, we propose a $K$ -client random scheduling strategy, where $K$ ( $1\leq K ) clients are randomly selected from the $N$ overall clients to participate in each aggregation. We also develop a corresponding convergence bound for the loss function in this case and the $K$ -client random scheduling strategy also retains the above three properties. Moreover, we find that there is an optimal $K$ that achieves the best convergence performance at a fixed privacy level. Evaluations demonstrate that our theoretical results are consistent with simulations, thereby facilitating the design of various privacy-preserving FL algorithms with different tradeoff requirements on convergence performance and privacy levels.

Federated Learning With Differential Privacy: Algorithms and Performance Analysis

Sensor networks, which consist of sensor nodes each capable of sensing environment and transmitting data, have lots of applications in battlefield surveillance, environmental monitoring, industrial diagnostics, etc. Coverage which is one of the most important performance metrics for sensor networks reflects how well a sensor field is monitored. Individual sensor coverage models are dependent on the sensing functions of different types of sensors, while network-wide sensing coverage is a collective performance measure for geographically distributed sensor nodes. This article surveys research progress made to address various coverage problems in sensor networks. We first provide discussions on sensor coverage models and design issues. The coverage problems in sensor networks can be classified into three categories according to the subject to be covered. We state the basic coverage problems in each category, and review representative solution approaches in the literature. We also provide comments and discussions on some extensions and variants of these basic coverage problems.

Coverage problems in sensor networks: A survey

In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noises are added to the parameters at the clients side before aggregating, namely, noising before model aggregation FL (NbAFL). First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noises. Then we develop a theoretical convergence bound of the loss function of the trained FL model in the NbAFL. Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i.e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level. Furthermore, we propose a $K$-random scheduling strategy, where $K$ ($1<K<N$) clients are randomly selected from the $N$ overall clients to participate in each aggregation. We also develop the corresponding convergence bound of the loss function in this case and the $K$-random scheduling strategy can also retain the above three properties. Moreover, we find that there is an optimal $K$ that achieves the best convergence performance at a fixed privacy level. Evaluations demonstrate that our theoretical results are consistent with simulations, thereby facilitating the designs on various privacy-preserving FL algorithms with different tradeoff requirements on convergence performance and privacy levels.

Federated Learning with Differential Privacy: Algorithms and Performance Analysis

A survey of communication/networking in Smart Grids

To explore the advantages of software defined network (SDN), while preserving the legacy networking systems, a natural deployment strategy is to deploy a hybrid SDN incrementally to improve the network performance. In this paper, we address two technical challenges: an incremental deployment strategy and a throughput-maximization routing, for deploying a hybrid network incrementally. For incremental deployment, we propose a heuristic algorithm for deploying a hybrid SDN under the budget constraint, and prove the approximate factor of $ 1- \frac {1}{e} $ . For throughput-maximization routing, we apply a depth-first-search method and a randomized rounding mechanism to solve the multi-commodity $h$ -splittable flow routing problem in a hybrid SDN, where $h\ge 1$ . We also prove that our method has approximation ratio $O\left({\frac {1}{\log N}}\right)$ , where $ N $ is the number of links in a hybrid SDN. We then show, by both analysis and simulations, that our algorithms can obtain significant performance gains and perform better than the theoretical worst-case bound. For example, our incremental deployment scheme helps to enhance the throughout about 40% compared with the previous deployment scheme by deploying a small number of SDN devices, and the proposed routing algorithm can improve the throughput about 31% compared with ECMP in hybrid networks.

Incremental Deployment and Throughput Maximization Routing for a Hybrid SDN

Consider statistical learning (e.g. discrete distribution estimation) with local $\epsilon$-differential privacy, which preserves each data provider's privacy locally, we aim to optimize statistical data utility under the privacy constraints. Specifically, we study maximizing mutual information between a provider's data and its private view, and give the exact mutual information bound along with an attainable mechanism: $k$-subset mechanism as results. The mutual information optimal mechanism randomly outputs a size $k$ subset of the original data domain with delicate probability assignment, where $k$ varies with the privacy level $\epsilon$ and the data domain size $d$. After analysing the limitations of existing local private mechanisms from mutual information perspective, we propose an efficient implementation of the $k$-subset mechanism for discrete distribution estimation, and show its optimality guarantees over existing approaches.

Mutual Information Optimally Local Private Discrete Distribution Estimation.

In Mobile Edge Computing (MEC), many tasks require specific service support for execution and in addition, have a dependent order of execution among the tasks. However, previous works often ignore the impact of having limited services cached at the edge nodes on (dependent) task offloading, thus may lead to an infeasible offloading decision or a longer completion time. To bridge the gap, this article studies how to efficiently offload dependent tasks to edge nodes with limited (and predetermined) service caching. We formally define the problem of offloading dependent tasks with service caching (ODT-SC), and prove that there exists no algorithm with constant approximation for this hard problem. Then, we design an efficient convex programming based algorithm (CP) to solve this problem. Moreover, we study a special case with a homogeneous MEC and propose a favorite successor based algorithm (FS) to solve this special case with a competitive ratio of $O(1)$ O ( 1 ) . Extensive simulation results using Google data traces show that our proposed algorithms can significantly reduce applications’ completion time by about 21-47 percent compared with other alternatives.

Offloading Tasks With Dependency and Service Caching in Mobile Edge Computing

Federated learning (FL) has emerged in edge computing to address limited bandwidth and privacy concerns of traditional cloud-based centralized training. However, the existing FL mechanisms may lead to long training time and consume a tremendous amount of communication resources. In this paper, we propose an efficient FL mechanism, which divides the edge nodes into K clusters by balanced clustering. The edge nodes in one cluster forward their local updates to cluster header for aggregation by synchronous method, called cluster aggregation, while all cluster headers perform the asynchronous method for global aggregation. This processing procedure is called hierarchical aggregation. Our analysis shows that the convergence bound depends on the number of clusters and the training epochs. We formally define the resource-efficient federated learning with hierarchical aggregation (RFL-HA) problem. We propose an efficient algorithm to determine the optimal cluster structure (i.e., the optimal value of K) with resource constraints and extend it to deal with the dynamic network conditions. Extensive simulation results obtained from our study for different models and datasets show that the proposed algorithms can reduce completion time by 34.8%-70% and the communication resource by 33.8%-56.5% while achieving a similar accuracy, compared with the well-known FL mechanisms.

Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing

Wireless sensor nodes are usually powered by batteries and deployed in unmanned outdoors or dangerous regions. So, constrained energy is a prominent feature for wireless sensor networks. Since the radio transceiver typically consumes more energies than any other hardware component on a sensor node, it is of great importance to design energy optimized routing algorithm to prolong network lifetime. In this work, based on analysis of energy consumption for data transceiver, single-hop forwarding scheme is proved to consume less energy than multihop forwarding scheme within the communication range of the source sensor or a current forwarder, using free space energy consumption model. We adopt the social welfare function to predict inequality of residual energy of neighbors after selecting different next hop nodes. Based on energy inequality, the method is designed to compute the degree of energy balance. Parameters such as degree of closeness of node to the shortest path, degree of closeness of node to Sink, and degree of energy balance are put into fuzzy logic system. Fuzzy-logic-based energy optimized routing algorithm is proposed to achieve multiparameter, fuzzy routing decision. Simulation results show that the algorithm effectively extends the network lifetime and has achieved energy efficiency and energy balance together, compared with similar algorithms.

https://journals.sagepub.com/doi/pdf/10.1155/2013/216561

Hongli Xu

Papers

Incremental Deployment and Throughput Maximization Routing for a Hybrid SDN

Mutual Information Optimally Local Private Discrete Distribution Estimation.

Offloading Tasks With Dependency and Service Caching in Mobile Edge Computing

Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing

Fuzzy-Logic-Based Energy Optimized Routing for Wireless Sensor Networks