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Item #:
236156406
Cloud Computing: Data-Intensive Computing and Scheduling, (Paperback)
Item #:
236156406
IQD 161170
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This book presents various technologies that demonstrate how cloud computing can meet business requirements and serve as the infrastructure of multidimensional data analysis applications.
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What Stands Out
In-Depth Analysis
Provides comprehensive insights into data-intensive computing, equipping readers with knowledge to tackle complex scheduling challenges in cloud environments.
Practical Examples
Includes real-world case studies and examples, enhancing understanding and application of theoretical concepts in practical scenarios.
Accessible Format
Designed as an easy-to-read paperback, making advanced topics in cloud computing accessible for students and professionals alike.
Product Details
- This book presents various technologies that demonstrate how cloud computing can meet business requirements and serve as the infrastructure of multidimensional data analysis applications. Addressing performance issues, it explores the evolution of classical techniques and describes completely new methods and innovative algorithms. The text covers scheduling algorithms for real-time tasks and practical algorithms for user bidding and auctioneer pricing. It explains approaches to data analytical query processing, including pre-computing, data indexing, and data partitioning. Applications of MapReduce, a new parallel programming model, are also presented.
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Textbooks |
| Publication date | June, 2019 |
| Pages | 232 |
| Reading level | Tertiary Education (US: College) |
| Subgenre | Number Systems |
| Series title | No Series |
| Edition | 1 |
| Publisher | Taylor & Francis Ltd (Sales) |
| Original languages | English |
| Language | English |
| Edu focus | Technology |
| Is collectible | N |
| Binding type | Perfect Binding |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 6.00 x 0.50 x 9.10 in (15.2 x 1.3 x 23.1 cm) |
| Assembled product weight | 0.75 lb (340 grams) |
| Bisac subject heading | Mathematics |
| Country of Origin | This item will be imported from US |
| What is in the box | Cloud Computing: Data-Intensive Computing... For more details, please check description/product details |
Product Description
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Customer Questions & Answers
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Question:
Who is the author of this book?
Answer: The book is authored by Frederic Magoules. -
Question:
What is the publication date of this book?
Answer: The book was published on June 19, 2019. -
Question:
How many pages does the book contain?
Answer: The book has a total of 232 pages.
Frederic Magoules All Books Editorial Review
Cloud Computing: Data-Intensive Computing and Scheduling, (Paperback) offers an insightful exploration into the realms of data management and resource allocation within cloud environments. This non-fiction textbook, published in June 2019 by Taylor & Francis Ltd, is structured to aid tertiary education students in grasping complex concepts in technology, primarily focusing on number systems pertinent to data-intensive computing. With 232 well-organized pages, it effectively serves as a comprehensive guide, enhancing the understanding of cloud computing principles. Many readers appreciate its clear explanations and practical approach, making it a valuable resource for college-level studies.
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Pros
- Comprehensive overview of data-intensive computing
- Engaging writing style facilitates understanding
- Well-structured for tertiary education
- Suitable for courses in cloud technology
- Clear explanations of complex concepts
Cons
- Might be too technical for beginners
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IQD 161170
Order now and get it around Monday, October 19
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Features & Benefits
- Focuses on addressing performance issues in data-intensive computing.
- Covers innovative algorithms and scheduling methods for cloud computing.
- Includes practical algorithms for resource management and bidding strategies.
- Explains data analytical query processing techniques such as indexing and partitioning.
- Presents applications of MapReduce for effective parallel programming.
- Serves as a comprehensive reference for cloud computing technologies.
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