About the Book:
Cloud computing offers subscription-based on-demand services, and it has emerged as the backbone of the computing industry. It has enabled us to share resources among multiple users through virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. Unlike early distributed computing models, it offers virtually limitless computing resources through its large scale cloud data centers. It has gained wide popularity over the past few years, with an ever-increasing infrastructure, a number of users, and the amount of hosted data. The large and complex workloads hosted on these data centers introduce many challenges, including resource utilization, power consumption, scalability, and operational cost. Therefore, an effective resource management scheme is essential to achieve operational efficiency with improved elasticity. Machine learning enabled solutions are the best fit to address these issues as they can analyze and learn from the data. Moreover, it brings automation to the solutions, which is an essential factor in dealing with large distributed systems in the cloud paradigm.
Machine Learning for Cloud Management explores cloud resource management through predictive modelling and virtual machine placement. The predictive approaches are developed using regression-based time series analysis and neural network models. The neural network-based models are primarily trained using evolutionary algorithms, and efficient virtual machine placement schemes are developed using multi-objective genetic algorithms.
Key Features:
The first book to set out a range of machine learning methods for efficient resource management in a large distributed network of clouds.
Predictive analytics is an integral part of efficient cloud resource management, and this book gives a future research direction to researchers in this domain.
It is written by leading international researchers.
Table of Contents
1. Introduction
1.1: Cloud Computing
1.2: Cloud Management
1.3: Machine Learning
1.4: Workload Traces
1.5: Experimental Setup & Evaluation Metrics
2. Time Series Models
2.1: Autoregression
2.2: Moving Average
2.3: Autoregressive Moving Average
2.4: Autoregressive Integrated Moving Average
3. Error Preventive Time Series Models
3.1: Error Prevention Scheme
3.2: Predictions In Error Range
3.3: Magnitude Of Predictions
3.4: Error Preventive Time Series Models
4. Metaheuristic Optimization Algorithms
4.1: Swarm Intelligence Algorithms In Predictive Model
4.2: Evolutionary Algorithms In Predictive Model
4.3: Nature Inspired Algorithms In Predictive Model
4.4: Physics Inspired Algorithms In Predictive Model
5. Evolutionary Neural Networks
5.1: Neural Network Prediction Framework Design
5.2: Network Learning
5.3: Recombination Operator Strategy Learning
5.4: Algorithms And Analysis
6. Self Directed Learning
6.1: Non-Directed Learning Based Framework
6.1: Non-Directed Learning
6.2: Self-Directed Learning Based Framework
6.3: Forecast Assessment
6.4: Long Term Forecast
7. Ensemble Learning
7.1: Extreme Learning Machine
7.2: Workload Decomposition Predictive Framework
7.2: Framework Design
7.3: Elm Ensemble Predictive Framework
7.4: Short Term Forecast Evaluation
8. Load Balancing
8.1: Multi-Objective Optimization
8.2: Resource Efficient Load Balancing Framework
8.3: Secure And Energy Aware Load Balancing Framework
8.3: Side Channel Attacks
8.3: Ternary Objective Vm Placement
8.4: Simulation Setup
8.5: Homogeneous Vm Placement Analysis
8.6: Heterogeneous Vm Placement Analysis
