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Optimized Cloud Resource Management and Scheduling

2014-10-15 Computers
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Release : 2014-10-15
Genre : Computers
Kind : eBook
Book Rating : 450/5 ( reviews)

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Book Synopsis Optimized Cloud Resource Management and Scheduling by : Wenhong Dr. Tian

Download or read book Optimized Cloud Resource Management and Scheduling written by Wenhong Dr. Tian. This book was released on 2014-10-15. Available in PDF, EPUB and Kindle. Book excerpt: Optimized Cloud Resource Management and Scheduling identifies research directions and technologies that will facilitate efficient management and scheduling of computing resources in cloud data centers supporting scientific, industrial, business, and consumer applications. It serves as a valuable reference for systems architects, practitioners, developers, researchers and graduate level students. Explains how to optimally model and schedule computing resources in cloud computing Provides in depth quality analysis of different load-balance and energy-efficient scheduling algorithms for cloud data centers and Hadoop clusters Introduces real-world applications, including business, scientific and related case studies Discusses different cloud platforms with real test-bed and simulation tools

Resource Management in Utility and Cloud Computing

2013-10-17 Computers
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Release : 2013-10-17
Genre : Computers
Kind : eBook
Book Rating : 709/5 ( reviews)

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Book Synopsis Resource Management in Utility and Cloud Computing by : Han Zhao

Download or read book Resource Management in Utility and Cloud Computing written by Han Zhao. This book was released on 2013-10-17. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief reviews the existing market-oriented strategies for economically managing resource allocation in distributed systems. It describes three new schemes that address cost-efficiency, user incentives, and allocation fairness with regard to different scheduling contexts. The first scheme, taking the Amazon EC2TM market as a case of study, investigates the optimal resource rental planning models based on linear integer programming and stochastic optimization techniques. This model is useful to explore the interaction between the cloud infrastructure provider and the cloud resource customers. The second scheme targets a free-trade resource market, studying the interactions amongst multiple rational resource traders. Leveraging an optimization framework from AI, this scheme examines the spontaneous exchange of resources among multiple resource owners. Finally, the third scheme describes an experimental market-oriented resource sharing platform inspired by eBay's transaction model. The study presented in this book sheds light on economic models and their implication to the utility-oriented scheduling problems.

Autonomic Computing in Cloud Resource Management in Industry 4.0

2021-09-05 Technology & Engineering
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Release : 2021-09-05
Genre : Technology & Engineering
Kind : eBook
Book Rating : 569/5 ( reviews)

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Book Synopsis Autonomic Computing in Cloud Resource Management in Industry 4.0 by : Tanupriya Choudhury

Download or read book Autonomic Computing in Cloud Resource Management in Industry 4.0 written by Tanupriya Choudhury. This book was released on 2021-09-05. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the next generation of industry—Industry 4.0—and how it holds the promise of increased flexibility in manufacturing, along with automation, better quality, and improved productivity. The authors discuss how it thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. The authors posit that intelligent cloud services and resource sharing play an important role in Industry 4.0 anticipated Fourth Industrial Revolution. This book serves the different issues and challenges in cloud resource management CRM techniques with proper propped solution for IT organizations. The book features chapters based on the characteristics of autonomic computing with its applicability in CRM. Each chapter features the techniques and analysis of each mechanism to make better resource management in cloud.

Adaptive Resource Management and Scheduling for Cloud Computing

2016-01-07 Computers
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Release : 2016-01-07
Genre : Computers
Kind : eBook
Book Rating : 487/5 ( reviews)

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Book Synopsis Adaptive Resource Management and Scheduling for Cloud Computing by : Florin Pop

Download or read book Adaptive Resource Management and Scheduling for Cloud Computing written by Florin Pop. This book was released on 2016-01-07. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the Second International Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, ARMS-CC 2015, held in Conjunction with ACM Symposium on Principles of Distributed Computing, PODC 2015, in Donostia-San Sebastián, Spain, in July 2015. The 12 revised full papers, including 1 invited paper, were carefully reviewed and selected from 24 submissions. The papers have identified several important aspects of the problem addressed by ARMS-CC: self-* and autonomous cloud systems, cloud quality management and service level agreement (SLA), scalable computing, mobile cloud computing, cloud computing techniques for big data, high performance cloud computing, resource management in big data platforms, scheduling algorithms for big data processing, cloud composition, federation, bridging, and bursting, cloud resource virtualization and composition, load-balancing and co-allocation, fault tolerance, reliability, and availability of cloud systems.

Optimized Cloud Based Scheduling

2018-02-24 Technology & Engineering
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Release : 2018-02-24
Genre : Technology & Engineering
Kind : eBook
Book Rating : 145/5 ( reviews)

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Book Synopsis Optimized Cloud Based Scheduling by : Rong Kun Jason Tan

Download or read book Optimized Cloud Based Scheduling written by Rong Kun Jason Tan. This book was released on 2018-02-24. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics.

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