List of FiguresList of TablesPrefaceAuthor BiosAbbreviationsIntroduction1.1 CLOUD COMPUTING1.2 CLOUD MANAGEMENT1.2.1 Workload Forecasting1.2.2 Load Balancing1.3 MACHINE LEARNING1.3.1 Artificial Neural Network1.3.2 Metaheuristic Optimization Algorithms1.3.3 Time Series Analysis1.4 WORKLOAD TRACES1.5 EXPERIMENTAL SETUP & EVALUATION METRICS1.6 STATISTICAL TESTS1.6.1 Wilcoxon Signed-Rank Test1.6.2 Friedman Test1.6.3 Finner TestTime Series Models2.1 AUTOREGRESSION2.2 MOVING AVERAGE2.3 AUTOREGRESSIVE MOVING AVERAGE2.4 AUTOREGRESSIVE INTEGRATED MOVING AVERAGE2.5 EXPONENTIAL SMOOTHING2.6 EXPERIMENTAL ANALYSIS2.6.1 Forecast Evaluation2.6.2 Statistical AnalysisError Preventive Time Series Models3.1 ERROR PREVENTION SCHEME3.2 PREDICTIONS IN ERROR RANGE3.3 MAGNITUDE OF PREDICTIONS3.4 ERROR PREVENTIVE TIME SERIES MODELS3.4.1 Error Preventive Autoregressive Moving Average3.4.2 Error Preventive Auto Regressive Integrated Moving Average3.4.3 Error Preventive Exponential Smoothing3.5 PERFORMANCE EVALUATION3.5.1 Comparative Analysis3.5.2 Statistical AnalysisMetaheuristic Optimization Algorithms4.1 SWARM INTELLIGENCE ALGORITHMS IN PREDICTIVE MODEL4.1.1 Particle Swarm Optimization4.1.2 Firefly Search Algorithm4.2 EVOLUTIONARY ALGORITHMS IN PREDICTIVE MODEL4.2.1 Genetic Algorithm4.2.2 Differential Evolution4.3 NATURE INSPIRED ALGORITHMS IN PREDICTIVE MODEL4.3.1 Harmony Search4.3.2 Teaching Learning Based Optimization4.4 PHYSICS INSPIRED ALGORITHMS IN PREDICTIVE MODEL4.4.1 Gravitational Search Algorithm4.4.2 Blackhole Algorithm4.5 STATISTICAL PERFORMANCE ASSESSMENTEvolutionary Neural Networks5.1 NEURAL NETWORK PREDICTION FRAMEWORK DESIGN5.2 NETWORK LEARNING5.3 RECOMBINATION OPERATOR STRATEGY LEARNING5.3.1 Mutation Operator5.3.1.1 DE/current to best/15.3.1.2 DE/best/15.3.1.3 DE/rand/15.3.2 Crossover Operator5.3.2.1 Ring Crossover5.3.2.2 Heuristic Crossover5.3.2.3 Uniform Crossover5.3.3 Operator Learning Process5.4 ALGORITHMS AND ANALYSIS5.5 FORECAST ASSESSMENT5.5.1 Short Term Forecast5.5.2 Long Term Forecast5.6 COMPARATIVE ANALYSISSelf Directed Learning6.1 NON-DIRECTED LEARNING BASED FRAMEWORK6.1.1 Non-Directed Learning6.2 SELF-DIRECTED LEARNING BASED FRAMEWORK6.2.1 Self Directed Learning6.2.2 Cluster Based Learning6.2.3 Complexity analysis6.3 FORECAST ASSESSMENT6.3.1 Short Term Forecast6.3.1.1 Web Server Workloads6.3.1.2 Cloud Workloads6.4 LONG TERM FORECAST6.4.0.1 Web Server Workloads6.4.0.2 Cloud Workloads6.5 COMPARATIVE & STATISTICAL ANALYSISEnsemble Learning7.1 EXTREME LEARNING MACHINE7.2 WORKLOAD DECOMPOSITION PREDICTIVE FRAMEWORK7.2.1 Framework Design7.3 ELM ENSEMBLE PREDICTIVE FRAMEWORK7.3.1 Ensemble Learning7.3.2 Expert Architecture Learning7.3.3 Expert Weight Allocation7.4 SHORT TERM FORECAST EVALUATION7.5 LONG TERM FORECAST EVALUATION7.6 COMPARATIVE ANALYSISLoad Balancing8.1 MULTI-OBJECTIVE OPTIMIZATION8.2 RESOURCE EFFICIENT LOAD BALANCING FRAMEWORK8.3 SECURE AND ENERGY AWARE LOAD BALANCING FRAMEWORK8.3.1 Side Channel Attacks8.3.2 Ternary Objective VM Placement8.4 SIMULATION SETUP8.5 HOMOGENEOUS VM PLACEMENT ANALYSIS8.6 HETEROGENEOUS VM PLACEMENT ANALYSISBibliographyIndex

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.

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