Machine Learning for Cloud Management

Cloud computing has become the backbone of the computing industry, offering subscription-based on-demand services and virtually limitless computing resources. However, managing these resources efficiently introduces challenges such as resource utilization, power consumption, scalability, and operational cost. Machine learning enabled solutions are the best fit to address these issues, as they can analyze and learn from the data, bringing automation to the solutions. "Machine Learning for Cloud Management" explores cloud resource management through predictive modeling and virtual machine placement, using regression-based time series analysis and neural network models. It is the first book to set out a range of machine learning methods for efficient resource management in a large distributed network of clouds and is written by leading international researchers.

Format: Paperback / softback
Length: 182 pages
Publication date: 26 November 2021
Publisher: Taylor & Francis Ltd

Cloud computing has revolutionized the computing industry by offering subscription-based on-demand services. It enables users to share resources among multiple users through virtualization, creating a virtual instance of a computer system running in an abstracted hardware layer. Unlike early distributed computing models, cloud computing offers virtually limitless computing resources through its large-scale cloud data centers. This has led to its widespread popularity in recent years, with an ever-increasing infrastructure, a growing number of users, and the amount of hosted data. However, the large and complex workloads hosted on these data centers present several challenges, including resource utilization, power consumption, scalability, and operational cost.

To address these challenges, effective resource management schemes are essential. Machine learning enabled solutions are particularly well-suited for cloud management as they can analyze and learn from the data. Moreover, they bring automation to the solutions, which is crucial in dealing with large distributed systems in the cloud paradigm.

Machine Learning for Cloud Management explores cloud resource management through predictive modeling and virtual machine placement. Predictive approaches are developed using regression-based time series analysis and neural network models. Neural network-based models are primarily trained using evolutionary algorithms, while efficient virtual machine placement schemes are developed using multi-objective genetic algorithms.

This book is the first to set out a range of machine learning methods for efficient resource management in a large distributed network of clouds. Predictive analytics plays a vital role in efficient cloud resource management, enabling organizations to make informed decisions about resource allocation and utilization. By leveraging machine learning algorithms, cloud providers can optimize resource allocation, reduce power consumption, and improve scalability.

In conclusion, cloud computing has transformed the computing industry by offering subscription-based on-demand services and enabling users to share resources among multiple users. Machine learning enabled solutions are crucial for cloud management as they can analyze and learn from the data, bringing automation to the solutions and optimizing resource allocation, power consumption, and scalability. This book provides a comprehensive overview of machine learning methods for efficient resource management in a large distributed network of clouds, making it an essential resource for cloud professionals and researchers.

Weight: 420g
Dimension: 177 x 251 x 18 (mm)
ISBN-13: 9780367622565