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Author: Eduardo Jose Ramírez Llanos Publisher: ISBN: Category : Languages : en Pages : 212
Book Description
The proliferation of large-scale networks like social networks, transportation networks, or smartgrids imposes new demands and challenges on the design of learning algorithms for optimal resource allocation. In a typical scenario, a group of agents decides how to coordinate the use of shared resources to solve a common goal while satisfying operational and communication constraints. The challenge is how to increase the network resilience given myopic agents with access to partial information. Under these settings, there is an emergence for the design of algorithms that are scalable, robust against adversarial or unknown environments, preserve privacy, and that allow the agents to take autonomous decisions on the resource utilization. A real-world problem leading to such a scenarios arises in computer networks, epidemiology, and viral marketing, where a viral outbreak can be a threat to the security of interconnected infrastructure and the well-being of general population. The implementation of strategies to stop epidemics can be specially challenging when networks are managed by multiple operators who need to preserve the privacy and interests of their constituents. Motivated by this situation, we consider a resource allocation problem for virus spread minimization. Based on a general contagion dynamics model, we characterize the optimal solution to the problem. We pose the problem objective as the minimization of the spectral radius of the contagion-dynamics matrix subject to operational constraints. We propose four algorithms to find the solution with provable convergence guarantees under different settings. The first algorithm, inspired by the Replicator Dynamics, implements the desired resource allocation for time-varying symmetric matrices. The second algorithm, designed in continuous-time, uses local and anonymous interactions, does not require knowledge of the total resource available to agents in order to converge to the solution, is robust to agents joining or departing the network, and to sporadic changes in the network topology, computation errors, and communication faults. The third algorithm, which is a discrete version of the second one, conserves the robustness properties of the previous one. Finally, we propose a stochastic algorithm, which extends the previous algorithms to scenarios where the closed-form expression of the cost functions is unknown to the agents.
Author: Eduardo Jose Ramírez Llanos Publisher: ISBN: Category : Languages : en Pages : 212
Book Description
The proliferation of large-scale networks like social networks, transportation networks, or smartgrids imposes new demands and challenges on the design of learning algorithms for optimal resource allocation. In a typical scenario, a group of agents decides how to coordinate the use of shared resources to solve a common goal while satisfying operational and communication constraints. The challenge is how to increase the network resilience given myopic agents with access to partial information. Under these settings, there is an emergence for the design of algorithms that are scalable, robust against adversarial or unknown environments, preserve privacy, and that allow the agents to take autonomous decisions on the resource utilization. A real-world problem leading to such a scenarios arises in computer networks, epidemiology, and viral marketing, where a viral outbreak can be a threat to the security of interconnected infrastructure and the well-being of general population. The implementation of strategies to stop epidemics can be specially challenging when networks are managed by multiple operators who need to preserve the privacy and interests of their constituents. Motivated by this situation, we consider a resource allocation problem for virus spread minimization. Based on a general contagion dynamics model, we characterize the optimal solution to the problem. We pose the problem objective as the minimization of the spectral radius of the contagion-dynamics matrix subject to operational constraints. We propose four algorithms to find the solution with provable convergence guarantees under different settings. The first algorithm, inspired by the Replicator Dynamics, implements the desired resource allocation for time-varying symmetric matrices. The second algorithm, designed in continuous-time, uses local and anonymous interactions, does not require knowledge of the total resource available to agents in order to converge to the solution, is robust to agents joining or departing the network, and to sporadic changes in the network topology, computation errors, and communication faults. The third algorithm, which is a discrete version of the second one, conserves the robustness properties of the previous one. Finally, we propose a stochastic algorithm, which extends the previous algorithms to scenarios where the closed-form expression of the cost functions is unknown to the agents.
Author: Thuan Hong Duong-Ba Publisher: ISBN: Category : Cloud computing Languages : en Pages : 200
Book Description
We studied the problem of resource allocation in large scale distributed applications such as Online Social Networks (OSN) and Cloud Computing. In such settings, resource allocation schemes need to efficient as well as adaptive to the time-varying environments. The abstract resource allocation problem concerns with how to optimally use resources for different tasks. In the context of this dissertation, the resources are servers and the tasks are (a) the virtual machines in the cloud computing setting, and or users for on-line social network applications. It is well-known that the general resource allocation problem is NP-hard. Therefore, in this dissertation, we study a number of heuristic algorithms designed for two primary objectives: 1) achieve reliability via load balancing among resource providers and 2) minimizing the energy consumption by reducing unnecessary intercommunication loads among the servers. Specifically, the dissertation has three main components. The first component deals with optimal assignment of user data to servers to maximize load balance and minimize power consumption. In this component, we propose a novel Distributed Perturbed Greedy Search (DPGS) algorithm which combine both deterministic search and random search to speed the convergence while avoiding local optimum. The empirical shows that the DPGS has a fast convergence rate to the near optimal solution even when the environment changes. The second component deals with the analysis on the convergence rates of a general simulated annealing algorithm via the notion of adiabatic time. We then apply the results to characterize the convergence rates for simulated annealing algorithm when applied to the optimal assignment in the component one. Finally, the third component of the dissertation is concerned with optimal assignment of virtual machines to servers in the context of cloud computing, in order to minimize the energy subject to a given performance requirement. We show that the problem can be approximated well as a convex problem, and propose convex relaxation technique to find the optimal solution.
Author: Sandip Roy Publisher: Cambridge University Press ISBN: 1108916074 Category : Technology & Engineering Languages : en Pages : 463
Book Description
This unique introduction to the foundational concepts of cyber-physical systems (CPS) describes key design principles and emerging research trends in detail. Several interdisciplinary applications are covered, with a focus on the wide-area management of infrastructures including electric power systems, air transportation networks, and health care systems. Design, control and optimization of cyber-physical infrastructures are discussed, addressing security and privacy issues of networked CPS, presenting graph-theoretic and numerical approaches to CPS evaluation and monitoring, and providing readers with the knowledge needed to operate CPS in a reliable, efficient, and secure manner. Exercises are included. This is an ideal resource for researchers and graduate students in electrical engineering and computer science, as well as for practitioners using cyber-physical systems in aerospace and automotive engineering, medical technology, and large-scale infrastructure operations.
Author: Dimitri Bertsekas Publisher: Athena Scientific ISBN: 1886529310 Category : Mathematics Languages : en Pages : 256
Book Description
An insightful, concise, and rigorous treatment of the basic theory of convex sets and functions in finite dimensions, and the analytical/geometrical foundations of convex optimization and duality theory. Convexity theory is first developed in a simple accessible manner, using easily visualized proofs. Then the focus shifts to a transparent geometrical line of analysis to develop the fundamental duality between descriptions of convex functions in terms of points, and in terms of hyperplanes. Finally, convexity theory and abstract duality are applied to problems of constrained optimization, Fenchel and conic duality, and game theory to develop the sharpest possible duality results within a highly visual geometric framework. This on-line version of the book, includes an extensive set of theoretical problems with detailed high-quality solutions, which significantly extend the range and value of the book. The book may be used as a text for a theoretical convex optimization course; the author has taught several variants of such a course at MIT and elsewhere over the last ten years. It may also be used as a supplementary source for nonlinear programming classes, and as a theoretical foundation for classes focused on convex optimization models (rather than theory). It is an excellent supplement to several of our books: Convex Optimization Algorithms (Athena Scientific, 2015), Nonlinear Programming (Athena Scientific, 2017), Network Optimization(Athena Scientific, 1998), Introduction to Linear Optimization (Athena Scientific, 1997), and Network Flows and Monotropic Optimization (Athena Scientific, 1998).
Author: Mykel J. Kochenderfer Publisher: MIT Press ISBN: 0262039427 Category : Computers Languages : en Pages : 521
Book Description
A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.
Author: Luis M. Camarinha-Matos Publisher: Springer ISBN: 3642547346 Category : Computers Languages : en Pages : 614
Book Description
This book constitutes the refereed proceedings of the 5th IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2014, held in Costa de Caparica, Portugal, in April 2014. The 68 revised full papers were carefully reviewed and selected from numerous submissions. They cover a wide spectrum of topics ranging from collaborative enterprise networks to microelectronics. The papers are organized in the following topical sections: collaborative networks; computational systems; self-organizing manufacturing systems; monitoring and supervision systems; advances in manufacturing; human-computer interfaces; robotics and mechatronics, Petri nets; multi-energy systems; monitoring and control in energy; modelling and simulation in energy; optimization issues in energy; operation issues in energy; power conversion; telecommunications; electronics: design; electronics: RF applications; and electronics: devices.
Author: Sukumar Mishra Publisher: Springer ISBN: 9811367728 Category : Technology & Engineering Languages : en Pages : 1296
Book Description
This book discusses key concepts, challenges and potential solutions in connection with established and emerging topics in advanced computing, renewable energy and network communications. Gathering edited papers presented at MARC 2018 on July 19, 2018, it will help researchers pursue and promote advanced research in the fields of electrical engineering, communication, computing and manufacturing.
Author: Xin-She Yang Publisher: Elsevier ISBN: 0124167454 Category : Computers Languages : en Pages : 277
Book Description
Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. - Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature - Provides a theoretical understanding as well as practical implementation hints - Provides a step-by-step introduction to each algorithm
Author: Ch. Satyanarayana Publisher: Springer ISBN: 9811305447 Category : Technology & Engineering Languages : en Pages : 139
Book Description
This book highlights major issues related to big data analysis using computational intelligence techniques, mostly interdisciplinary in nature. It comprises chapters on computational intelligence technologies, such as neural networks and learning algorithms, evolutionary computation, fuzzy systems and other emerging techniques in data science and big data, ranging from methodologies, theory and algorithms for handling big data, to their applications in bioinformatics and related disciplines. The book describes the latest solutions, scientific results and methods in solving intriguing problems in the fields of big data analytics, intelligent agents and computational intelligence. It reflects the state of the art research in the field and novel applications of new processing techniques in computer science. This book is useful to both doctoral students and researchers from computer science and engineering fields and bioinformatics related domains.
Author: Eduardo Jose Ramírez Llanos
Author: Eduardo Jose Ramírez Llanos
Author: Thuan Hong Duong-Ba
Author: Sandip Roy
Author: Dimitri Bertsekas
Author: Mykel J. Kochenderfer
Author: Luis M. Camarinha-Matos
Author: Sukumar Mishra
Author: Devavrat Shah
Author: Xin-She Yang
Author: Ch. Satyanarayana