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A Framework For Scalable Distributed Job Processing With Dynamic Load Balancing Using Decentralized Approach
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Book Synopsis A FRAMEWORK FOR SCALABLE DISTRIBUTED JOB PROCESSING WITH DYNAMIC LOAD BALANCING USING DECENTRALIZED APPROACH by : Dr P. SrinivasaRao
Download or read book A FRAMEWORK FOR SCALABLE DISTRIBUTED JOB PROCESSING WITH DYNAMIC LOAD BALANCING USING DECENTRALIZED APPROACH written by Dr P. SrinivasaRao and published by Lulu.com. This book was released on 2017-12-30 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: A distributed system consists of many heterogeneous processors with different processing power and all processors are interconnected with a communication channel. In such a system, if some processors are less loaded or idle and others are heavily loaded, the system performance will be reduced drastically. System performance can be improved by using proper load balancing [1, 4]. The aim of load balancing is to improve the performance measures and reduce the overall completion time and cost
Book Synopsis Theory and Applications of Satisfiability Testing – SAT 2021 by : Chu-Min Li
Download or read book Theory and Applications of Satisfiability Testing – SAT 2021 written by Chu-Min Li and published by Springer Nature. This book was released on 2021-07-01 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 24th International Conference on Theory and Applications of Satisfiability Testing, SAT 2021, which took place in Barcelona, Spain, in July 2021. The 37 full papers presented in this volume were carefully reviewed and selected from 73 submissions. They deal with theory and applications of the propositional satisfiability problem, broadly construed. Aside from plain propositional satisfiability, the scope of the meeting includes Boolean optimization, including MaxSAT and pseudo-Boolean (PB) constraints, quantified Boolean formulas (QBF), satisfiability modulo theories (SMT), and constraint programming (CP) for problems with clear connections to Boolean reasoning.
Book Synopsis Scheduling in Distributed Computing Environment Using Dynamic Load Balancing by : Priyesh Kanungo
Download or read book Scheduling in Distributed Computing Environment Using Dynamic Load Balancing written by Priyesh Kanungo and published by Anchor Academic Publishing. This book was released on 2016-08 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book illustrates various components of Distributed Computing Environment and the importance of distributed scheduling using Dynamic Load Balancing. It describes load balancing algorithms for better resource utilization, increasing throughput and improving user’s response time. Various theoretical concepts, experiments, and examples enable students to understand the process of load balancing in computing cluster and server cluster. The book is suitable for students of Advance Operating Systems, High Performance Computing, Distributed Computing in B.E., M.C.A., M. Tech. and Ph.D courses.
Book Synopsis Applications of Machine Learning in UAV Networks by : Hassan, Jahan
Download or read book Applications of Machine Learning in UAV Networks written by Hassan, Jahan and published by IGI Global. This book was released on 2024-01-17 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applications of Machine Learning in UAV Networks presents a pioneering exploration into the symbiotic relationship between machine learning techniques and UAVs. In an age where UAVs are revolutionizing sectors as diverse as agriculture, environmental preservation, security, and disaster response, this meticulously crafted volume offers an analysis of the manifold ways machine learning drives advancements in UAV network efficiency and efficacy. This book navigates through an expansive array of domains, each demarcating a pivotal application of machine learning in UAV networks. From the precision realm of agriculture and its dynamic role in yield prediction to the ecological sensitivity of biodiversity monitoring and habitat restoration, the contours of each domain are vividly etched. These explorations are not limited to the terrestrial sphere; rather, they extend to the pivotal aerial missions of wildlife conservation, forest fire monitoring, and security enhancement, where UAVs adorned with machine learning algorithms wield an instrumental role. Scholars and practitioners from fields as diverse as machine learning, UAV technology, robotics, and IoT networks will find themselves immersed in a confluence of interdisciplinary expertise. The book's pages cater equally to professionals entrenched in agriculture, environmental studies, disaster management, and beyond.
Book Synopsis Proceedings, 2000 International Workshop on Autonomous Decentralized System by :
Download or read book Proceedings, 2000 International Workshop on Autonomous Decentralized System written by and published by Institute of Electrical & Electronics Engineers(IEEE). This book was released on 2000 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text constitutes the proceedings from the International Workshop on Autonomous Decentralized Systems (IWADS2000) that took place in 2000. Topics covered include flexible and autonomous service replication technique, and information searching in autonomous mobile agent groups.
Book Synopsis A Global Plan Policy for Coherent Cooperation in Distributed Dynamic Load Balancing Algorithms by : M. Kara
Download or read book A Global Plan Policy for Coherent Cooperation in Distributed Dynamic Load Balancing Algorithms written by M. Kara and published by . This book was released on 1994 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Distributed-controlled dynamic load balancing algorithms are known to have several advantages over centralised algorithms such as scalability, and fault tolerance. Distributed implies that the control is decentralised and that a copy of the algorithm (called a scheduler) is replicated on each host of the network. However, distributed control also contributes to the lack of global goals and lack of coherence. This paper presents a new algorithm called DGP (Decentralised Global Plans) that addresses the problem of coherence and coordination in distributed dynamic load balancing algorithms. The DGP algorithm is based on a strategy called Global Plans (GP), and aims at maintaining all computational loads of a distributed system within a band called [delta]. The rationale for the design of DGP is to allow each scheduler to consider the actions of its peer schedulers. With this level of coordination, the schedulers can act more as a coherent team. This new approach first explicitly specifies a global goal and then design [sic] a strategy around this global goal such that each scheduler (1) takes into account local decisions made by other schedulers; (2) takes into account the effect of its local decisions on the overall system and (3) ensures load balancing. An experimental evaluation of DGP with two other well-known dynamic load balancing algorithms published in the literature shows that DGP performs consistently better. More significantly, the results indicate that the global plan approach provides a better frameowrk for the design of distributed dynamic load balancing algorithms."
Book Synopsis Job Scheduling Strategies for Parallel Processing by : Narayan Desai
Download or read book Job Scheduling Strategies for Parallel Processing written by Narayan Desai and published by Springer. This book was released on 2014-06-10 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 17th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2013, held Boston, MA, USA, in May 2013. The 10 revised papers presented were carefully reviewed and selected from 20 submissions. The papers cover the following topics parallel scheduling for commercial environments, scientific computing, supercomputing and cluster platforms.
Book Synopsis Load-balanced Structures for Decentralized Overlays by :
Download or read book Load-balanced Structures for Decentralized Overlays written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In large scale distributed environments, huge amounts of information are exchanged and accessed by a large and dynamic number of users. The information is not only stored in servers, but also users store and share the information. Due to these characteristics, the client/server communication model is not well adapted for certain types of applications. As an alternative to the client/server model, new paradigms such as peer-to-peer and publish/subscribe have been proposed providing mechanisms to locate the information stored and shared between the users and to disseminate the information to interested users. In this thesis, we focus on developing efficient lookup mechanisms avoiding bottlenecks on large scale peer-to-peer systems and efficient information dissemination taking into account the system resources. In the first part of this thesis, we propose an adaptive load-balancing solution in structure peer-to-peer systems. The approach aims to balance the request and routing load of the peers, under biased request workloads, distributing the traffic among the peers in the overlay. We achieve the routing load balancing through a dynamic reorganization of the routing tables and the request load balancing by caching the most popular keys. We significantly improve the load balancing, and consequently their scalability and performance. In the second part of this thesis, we focus on achieving scalable and efficient information dissemination. We propose Distributed R-tree overlays and Distributed Hilbert R-trees, which use R-tree-based spatial filters to construct a peer-to-peer overlay optimized for selective dissemination of information. We adapt well-known variants of R-trees to build content-based publish/subscribe where publishers and subscribers are organized in a peer-to-peer network in order to minimize the occurrences of false positives while avoiding false negatives. In addition, we implement self-stabilizing algorithms that guarantee consistency despite failures.
Book Synopsis The Study of Load Balancing Algorithms for Decentralized Distributed Processing Systems by : University of Wisconsin--Madison. Computer Sciences Dept
Download or read book The Study of Load Balancing Algorithms for Decentralized Distributed Processing Systems written by University of Wisconsin--Madison. Computer Sciences Dept and published by . This book was released on 1984 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Dynamic Load Balancing and Autoscaling in Distributed Stream Processing Systems by : Xing Wu
Download or read book Dynamic Load Balancing and Autoscaling in Distributed Stream Processing Systems written by Xing Wu and published by . This book was released on 2015 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: In big data world, Hadoop and other batch-processing tools are widely used to analyze data and get results in minutes. However, minutes of latency still cannot satisfy the proliferated needs for real-time decision in many fields such as live stock and trading feeds in financial services, telecommunications, sensor networks, online advertisement, etc. Distributed stream processing (DSP) systems aim to process, analyze and make decisions on-the-fly based on immense quantities of data streams being dynamically generated at high rates. As the rates of data streams may vary over time, DSP systems require an architecture that is elastic to handle dynamic load. Although many dynamic load balancing and autoscaling techniques for general pull-based distributed systems have been well studied, these solutions cannot be directly applied to DSP systems because DSP systems are push-based, they process data streams with different types of operators, each running on a cluster node. One research problem is to allocate data processing operators on nodes of clusters and balance the workload dynamically. Since the data volume and rate can be unpredictable, static mapping between operators and cluster resources often results in unbalanced operator load distribution. Furthermore, the problem of making DSP system scalable requires autoscaling at runtime. In this context, the operators need to be relocated among newly provisioned nodes. The contribution of this thesis is three folds. First, we proposes a software layer that is load-adaptive between a DSP engine and clusters. The architecture allows dynamic transferring of an operator to different cluster nodes at runtime and keeps the process transparent to developers. Second, an optimization method that combines correlation of resource utilization of nodes and capacity of clusters is proposed to balance load dynamically. Lastly, we design the autoscaling mechanism and algorithm to detect overload and provision nodes at runtime. We implement our design on S4, an open-source DSP engine first developed by Yahoo!. The implementation is evaluated by a top-N topic list application on Twitter streams using clusters on Amazon Web Services. The results demonstrate a 75.79% improvement on stream processing throughputs, and a 294.47% improvement on cluster resource utilization.
Book Synopsis Artificial Intelligence Abstracts by :
Download or read book Artificial Intelligence Abstracts written by and published by . This book was released on 1991 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Concise Introduction to Decentralized POMDPs by : Frans A. Oliehoek
Download or read book A Concise Introduction to Decentralized POMDPs written by Frans A. Oliehoek and published by Springer. This book was released on 2016-06-03 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs). The intended audience is researchers and graduate students working in the fields of artificial intelligence related to sequential decision making: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research.
Book Synopsis Distributed and Cloud Computing by : Kai Hwang
Download or read book Distributed and Cloud Computing written by Kai Hwang and published by Morgan Kaufmann. This book was released on 2013-12-18 with total page 671 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed and Cloud Computing: From Parallel Processing to the Internet of Things offers complete coverage of modern distributed computing technology including clusters, the grid, service-oriented architecture, massively parallel processors, peer-to-peer networking, and cloud computing. It is the first modern, up-to-date distributed systems textbook; it explains how to create high-performance, scalable, reliable systems, exposing the design principles, architecture, and innovative applications of parallel, distributed, and cloud computing systems. Topics covered by this book include: facilitating management, debugging, migration, and disaster recovery through virtualization; clustered systems for research or ecommerce applications; designing systems as web services; and social networking systems using peer-to-peer computing. The principles of cloud computing are discussed using examples from open-source and commercial applications, along with case studies from the leading distributed computing vendors such as Amazon, Microsoft, and Google. Each chapter includes exercises and further reading, with lecture slides and more available online. This book will be ideal for students taking a distributed systems or distributed computing class, as well as for professional system designers and engineers looking for a reference to the latest distributed technologies including cloud, P2P and grid computing. - Complete coverage of modern distributed computing technology including clusters, the grid, service-oriented architecture, massively parallel processors, peer-to-peer networking, and cloud computing - Includes case studies from the leading distributed computing vendors: Amazon, Microsoft, Google, and more - Explains how to use virtualization to facilitate management, debugging, migration, and disaster recovery - Designed for undergraduate or graduate students taking a distributed systems course—each chapter includes exercises and further reading, with lecture slides and more available online
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 1997 with total page 806 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book 并行程序设计 written by Foster and published by . This book was released on 2002 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: 国外著名高等院校信息科学与技术优秀教材
Book Synopsis Algorithmic Aspects of Cloud Computing by : Gianlorenzo D’Angelo
Download or read book Algorithmic Aspects of Cloud Computing written by Gianlorenzo D’Angelo and published by Springer Nature. This book was released on 2021-12-09 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings of the 6th International Symposium on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2021, held in Lisbon, Portugal, in September 2021. Due to the COVID-19 pandemic the conference was held online. The 4 revised full papers and 1 short paper were carefully reviewed and selected from 11 submissions. The aim of the symposium is to present research activities and results on topics related to algorithmic, design, and development aspects of modern cloud-based systems.
Download or read book Federated Learning written by Qiang Yang and published by Springer Nature. This book was released on 2020-11-25 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
