Load Balancing Using Time Series Analysis for Soft Real Time Systems With Statistically Periodic Loads - Rilegato
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Load Balancing Using Time Series Analysis for Soft Real Time Systems With Statistically Periodic Loads
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Creative Media Partners, LLC, 2025
ISBN 10: 1025154274
ISBN 13: 9781025154275
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HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L1-9781025154275
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Load Balancing Using Time Series Analysis for Soft Real Time Systems With Statistically Periodic Loads
Editore:
Creative Media Partners, LLC, 2025
ISBN 10: 1025154274
ISBN 13: 9781025154275
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HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L1-9781025154275
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Load Balancing Using Time Series Analysis for Soft Real Time Systems With Statistically Periodic Loads
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Load Balancing Using Time Series Analysis for Soft Real Time Systems With Statistically Periodic Loads
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Condizione: New. Print on Demand. Codice articolo 409474818
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Load Balancing Using Time Series Analysis for Soft Real Time Systems With Statistically Periodic Loads
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Load Balancing Using Time Series Analysis for Soft Real Time Systems With Statistically Periodic Loads (Hardcover)
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Hardcover. Condizione: new. Hardcover. This thesis provides design and analysis of techniques for global load balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic loads. It focuses on estimating the instantaneous average load over all the processing elements. The major contribution is the use of explicit stochastic process models for both the loading and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average load estimates, and thus to facilitate global load balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that our techniques allow more accurate estimation of the global system load ing, resulting in fewer object migration than local methods. Our method is shown to provide superior performance, relative not only to static load-balancing schemes but also to many adaptive methods.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9781025154275
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Load Balancing Using Time Series Analysis for Soft Real Time Systems With Statistically Periodic Loads
Editore:
Creative Media Partners, LLC Mai 2025, 2025
ISBN 10: 1025154274
ISBN 13: 9781025154275
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Buch. Condizione: Neu. Neuware - This thesis provides design and analysis of techniques for global load balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic loads. It focuses on estimating the instantaneous average load over all the processing elements. The major contribution is the use of explicit stochastic process models for both the loading and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average load estimates, and thus to facilitate global load balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that our techniques allow more accurate estimation of the global system load ing, resulting in fewer object migration than local methods. Our method is shown to provide superior performance, relative not only to static load-balancing schemes but also to many adaptive methods. Codice articolo 9781025154275
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