Machine Learning for Cloud Management

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.

The book is ideal for researchers who are working in the domain of cloud computing.

List of Figures
List of Tables
Preface
Author Bios
Abbreviations

Introduction
1.1 CLOUD COMPUTING
1.2 CLOUD MANAGEMENT
1.2.1 Workload Forecasting
1.2.2 Load Balancing
1.3 MACHINE LEARNING
1.3.1 Artificial Neural Network
1.3.2 Metaheuristic Optimization Algorithms
1.3.3 Time Series Analysis
1.4 WORKLOAD TRACES
1.5 EXPERIMENTAL SETUP & EVALUATION METRICS
1.6 STATISTICAL TESTS
1.6.1 Wilcoxon Signed-Rank Test
1.6.2 Friedman Test
1.6.3 Finner Test

Time Series Models
2.1 AUTOREGRESSION
2.2 MOVING AVERAGE
2.3 AUTOREGRESSIVE MOVING AVERAGE
2.4 AUTOREGRESSIVE INTEGRATED MOVING AVERAGE
2.5 EXPONENTIAL SMOOTHING
2.6 EXPERIMENTAL ANALYSIS
2.6.1 Forecast Evaluation
2.6.2 Statistical Analysis

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
3.4.1 Error Preventive Autoregressive Moving Average
3.4.2 Error Preventive Auto Regressive Integrated Moving Average
3.4.3 Error Preventive Exponential Smoothing
3.5 PERFORMANCE EVALUATION
3.5.1 Comparative Analysis
3.5.2 Statistical Analysis

Metaheuristic Optimization Algorithms
4.1 SWARM INTELLIGENCE ALGORITHMS IN PREDICTIVE MODEL
4.1.1 Particle Swarm Optimization
4.1.2 Firefly Search Algorithm
4.2 EVOLUTIONARY ALGORITHMS IN PREDICTIVE MODEL
4.2.1 Genetic Algorithm
4.2.2 Differential Evolution
4.3 NATURE INSPIRED ALGORITHMS IN PREDICTIVE MODEL
4.3.1 Harmony Search
4.3.2 Teaching Learning Based Optimization
4.4 PHYSICS INSPIRED ALGORITHMS IN PREDICTIVE MODEL
4.4.1 Gravitational Search Algorithm
4.4.2 Blackhole Algorithm
4.5 STATISTICAL PERFORMANCE ASSESSMENT

Evolutionary Neural Networks
5.1 NEURAL NETWORK PREDICTION FRAMEWORK DESIGN
5.2 NETWORK LEARNING
5.3 RECOMBINATION OPERATOR STRATEGY LEARNING
5.3.1 Mutation Operator
5.3.1.1 DE/current to best/1
5.3.1.2 DE/best/1
5.3.1.3 DE/rand/1
5.3.2 Crossover Operator
5.3.2.1 Ring Crossover
5.3.2.2 Heuristic Crossover
5.3.2.3 Uniform Crossover
5.3.3 Operator Learning Process
5.4 ALGORITHMS AND ANALYSIS
5.5 FORECAST ASSESSMENT
5.5.1 Short Term Forecast
5.5.2 Long Term Forecast
5.6 COMPARATIVE ANALYSIS

Self Directed Learning
6.1 NON-DIRECTED LEARNING BASED FRAMEWORK
6.1.1 Non-Directed Learning
6.2 SELF-DIRECTED LEARNING BASED FRAMEWORK
6.2.1 Self Directed Learning
6.2.2 Cluster Based Learning
6.2.3 Complexity analysis
6.3 FORECAST ASSESSMENT
6.3.1 Short Term Forecast
6.3.1.1 Web Server Workloads
6.3.1.2 Cloud Workloads
6.4 LONG TERM FORECAST
6.4.0.1 Web Server Workloads
6.4.0.2 Cloud Workloads
6.5 COMPARATIVE & STATISTICAL ANALYSIS

Ensemble Learning
7.1 EXTREME LEARNING MACHINE
7.2 WORKLOAD DECOMPOSITION PREDICTIVE FRAMEWORK
7.2.1 Framework Design
7.3 ELM ENSEMBLE PREDICTIVE FRAMEWORK
7.3.1 Ensemble Learning
7.3.2 Expert Architecture Learning
7.3.3 Expert Weight Allocation
7.4 SHORT TERM FORECAST EVALUATION
7.5 LONG TERM FORECAST EVALUATION
7.6 COMPARATIVE ANALYSIS

Load Balancing
8.1 MULTI-OBJECTIVE OPTIMIZATION
8.2 RESOURCE EFFICIENT LOAD BALANCING FRAMEWORK
8.3 SECURE AND ENERGY AWARE LOAD BALANCING FRAMEWORK
8.3.1 Side Channel Attacks
8.3.2 Ternary Objective VM Placement
8.4 SIMULATION SETUP
8.5 HOMOGENEOUS VM PLACEMENT ANALYSIS
8.6 HETEROGENEOUS VM PLACEMENT ANALYSIS

Bibliography
Index

Jitendra Kumar is an assistant professor in machine learning at the National Institute of Technology Tiruchirappalli, Tamilnadu, India. He obtained his doctorate in 2019 from the National Institute of Technology Kurukshetra, Haryana, India. He is also a recipient of the Director’s medal for the first rank in the University examination at Dayalbagh Educational Institute, Agra, Uttar Pradesh in 2011. He has experience of three years in academia. He has published several research papers in international journals and conferences of high repute, including IEEE Transactions on Parallel and Distributed Systems, Information Sciences, Future Generation Computer Systems, Neurocomputing, Soft Computing, Cluster Computing, IEEE-FUZZ, etc. He has also obtained the best paper awards in two international conferences. His research interests are machine learning, cloud computing, healthcare, parallel algorithms, and optimization. He is also a review board member of several journals, including IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, IEEE Access, Journal and Parallel Distributed Computing, and more.

Ashutosh Kumar Singh is an esteemed researcher and academician in the domain of Electrical and Computer engineering. Currently, he is working as a Professor; Department of Computer Applications; National Institute of Technology; Kurukshetra, India. He has more than 20 years research, teaching and administrative experience in various University systems of the India, UK, Australia and Malaysia. Dr. Singh obtained his Ph. D. degree in Electronics Engineering from Indian Institute of Technology-BHU, India; Post Doc from Department of Computer Science, University of Bristol, United Kingdom and Charted Engineer from United Kingdom. He is the recipient of Japan Society for the Promotion of Science (JSPS) fellowship for visit in University of Tokyo and other universities of Japan. His research area includes Verification, Synthe