9783330001404 - Automatic Tuning of Data-Intensive Analytical Workloads de Herodotou, Herodotos (9 resultados)

Condición: New.

Paperback. Condición: Brand New. 328 pages. 8.66x5.91x0.74 inches. In Stock.

Taschenbuch. Condición: Neu. Neuware -Modern industrial, government, and academic organizations are collecting massive amounts of data at an unprecedented scale and pace. The ability to perform timely and cost-effective analytical processing of such large datasets in order to extract deep insights is now a key ingredient for success. Existing database systems are adapting to the new status quo while large-scale dataflow systems like MapReduce are becoming popular for executing analytical workloads on Big Data. In order to ensure good and robust performance automatically on such systems, a novel dynamic optimization approach has been developed that works across different tuning scenarios and systems. The solution is based on (i) collecting monitoring information in order to learn the run-time behavior of workloads, (ii) deploying appropriate models to predict the impact of hypothetical tuning choices on workload behavior, and (iii) using efficient search strategies to find tuning choices that give good workload performance. The dynamic nature enables this solution to overcome the new challenges posed by Big Data, and also makes it applicable to both MapReduce and Database systems.Books on Demand GmbH, Überseering 33, 22297 Hamburg 328 pp. Englisch.

Taschenbuch. Condición: Neu. Automatic Tuning of Data-Intensive Analytical Workloads | Herodotos Herodotou | Taschenbuch | 328 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783330001404 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Modern industrial, government, and academic organizations are collecting massive amounts of data at an unprecedented scale and pace. The ability to perform timely and cost-effective analytical processing of such large datasets in order to extract deep insights is now a key ingredient for success. Existing database systems are adapting to the new status quo while large-scale dataflow systems like MapReduce are becoming popular for executing analytical workloads on Big Data. In order to ensure good and robust performance automatically on such systems, a novel dynamic optimization approach has been developed that works across different tuning scenarios and systems. The solution is based on (i) collecting monitoring information in order to learn the run-time behavior of workloads, (ii) deploying appropriate models to predict the impact of hypothetical tuning choices on workload behavior, and (iii) using efficient search strategies to find tuning choices that give good workload performance. The dynamic nature enables this solution to overcome the new challenges posed by Big Data, and also makes it applicable to both MapReduce and Database systems. 328 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Herodotou HerodotosDr. Herodotos Herodotou is a tenure-track Lecturer at the Cyprus University of Technology. He received his Ph.D. in Computer Science from Duke University in 2012. His research interests are in large-scale Data Proc.

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Modern industrial, government, and academic organizations are collecting massive amounts of data at an unprecedented scale and pace. The ability to perform timely and cost-effective analytical processing of such large datasets in order to extract deep insights is now a key ingredient for success. Existing database systems are adapting to the new status quo while large-scale dataflow systems like MapReduce are becoming popular for executing analytical workloads on Big Data. In order to ensure good and robust performance automatically on such systems, a novel dynamic optimization approach has been developed that works across different tuning scenarios and systems. The solution is based on (i) collecting monitoring information in order to learn the run-time behavior of workloads, (ii) deploying appropriate models to predict the impact of hypothetical tuning choices on workload behavior, and (iii) using efficient search strategies to find tuning choices that give good workload performance. The dynamic nature enables this solution to overcome the new challenges posed by Big Data, and also makes it applicable to both MapReduce and Database systems.