An Optimized Deep Machine Learning And Micro Services Architecture Based Proactive Elastic Cloud Framework
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An Optimized Deep Machine Learning And Micro Services Architecture Based Proactive Elastic Cloud Framework
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Author : Tariq Daradkeh
language : en
Publisher:
Release Date : 2022
An Optimized Deep Machine Learning And Micro Services Architecture Based Proactive Elastic Cloud Framework written by Tariq Daradkeh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
To achieve elasticity in cloud environment a holistic solution must be considered that measures all running applications and resources performance, including its cloud management system. Cloud resources and applications are continuously changing in their capacity and behavior, which implies a dynamic change in the cloud management system architecture and characteristics. The new era of application modeling is to decouple its components and make them as standalone cooperated modules following micro-service pattern architecture. This design gives an application a fast adaptation agility to change in requirements by customizing the application operation modules to match new tasks. The proposed elastic framework is achieved using multiple tasks as a sequence of steps. First, cloud resources monitoring, and workload changes are tracked. Second, workloads clustering using custom K-means method is used to categorize unlabeled workload sets. Third, workload demands, and datacenter configuration are predicted, classified, and labeled using deep machine learning techniques. Fourth, resources are scaled and scheduled based on workload characteristics and scaling dimension conditions. Fifth, a micro-service pattern based elastic framework is implemented for dynamic resources management and operation. First task, monitoring system must provide needed information to cloud manager to describe cloud dynamic state by reading cloud-generated logs and sending them to cloud manager. Log updates should be accurate, instantaneous, and sent with minimum time delay. Data sources vary from low to high level of cloud infrastructure resources, or they can be generated from workload demands. Logs are used to discover cloud system status, which is input for future actions in cloud management resources orchestration. Point Estimator (PE) log tracker is proposed that can dynamically adapt to the type of workload providing accurate fresh logs values to cloud manager with minimum number of data transactions. Second task, dynamic K-Means clustering using kernel density estimator is proposed to analyze and characterize both workloads and datacenter configurations. This method enhances K-Means clustering by automatically determining optimum number of classes and finding the mean centroids for the clusters. In addition, it improves the accuracy and the time complexity of standard K-Means clustering model, by best correlating between clustering attributes using statistical correlation methods. Third task, cloud workload prediction is a very critical task for elastic scaling, because cloud manager decides what configuration sequence is to be considered for resource provisioning. Predicting workload demands to optimize datacenter configuration, such that increasing/decreasing datacenter resources provides an accurate and efficient configuration. Three methods of deep machine learning (namely NN, CNN and LSTM) are used and compared with analytical approach to model workload and datacenter actions. Analytical model is used as predictor to evaluate and test optimization solution set and find the best configuration and scaling actions before applying it on the real datacenter. Deep machine learning together with analytical approach is used to find the best prediction values of workload demands and evaluate the scaling and resources capacity required to be provisioned. Deep machine learning is used to find optimal configuration and to solve the elasticity scaling boundaries values. %Matching the demand guarantees Service Level Agreement (SLA) conditions and Quality of Service (QoS) performance. Fourth task, resources scaling and scheduling in cloud elasticity involves timely provisioning and de-provisioning of computing resources and adjusting resources size to meet the dynamic workload demand. This requires fast and accurate resource scaling methods at minimum cost (e.g. pay as you go) that match with workload demands. Two dynamic changing parameters must be defined in an elastic model, the workload resource demand classes, and the data center resource reconfiguration classes. These parameters are not labeled for cloud management system while data center logs are being captured. A deep machine learning method is used to label datacenter configuration. Fifth task, micro-service pattern architecture with open standard API is used to integrate between all elastic cloud framework components. Full stack micro-service based elastic cloud management system is implemented considering elastic scaling and management requirements of all resources. The model focuses on elasticity scaling performance by analyzing cloud micro-service management modules in different aspects: interactions, end to end delay, and communication. It also focuses on optimizing decoupling of system components and optimizing orchestration scheduling for elastic scaling.
Microservices For Machine Learning
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Author : Rohit Ranjan
language : en
Publisher: BPB Publications
Release Date : 2024-04-20
Microservices For Machine Learning written by Rohit Ranjan and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-20 with Computers categories.
Empowering AI innovations: The fusion of microservices and ML KEY FEATURES ● Microservices and ML fundamentals, advancements, and practical applications in various industries. ● Simplify complex ML development with distributed and scalable microservices architectures. ● Discover real-world scenarios illustrating the fusion of microservices and ML, showcasing AI's impact across industries. DESCRIPTION Explore the link between microservices and ML in Microservices for Machine Learning. Through this book, you will learn to build scalable systems by understanding modular software construction principles. You will also discover ML algorithms and tools like TensorFlow and PyTorch for developing advanced models. It equips you with the technical know-how to design, implement, and manage high-performance ML applications using microservices architecture. It establishes a foundation in microservices principles and core ML concepts before diving into practical aspects. You will learn how to design ML-specific microservices, implement them using frameworks like Flask, and containerize them with Docker for scalability. Data management strategies for ML are explored, including techniques for real-time data ingestion and data versioning. This book also addresses crucial aspects of securing ML microservices and using CI/CD practices to streamline development and deployment. Finally, you will discover real-world use cases showcasing how ML microservices are revolutionizing various industries, alongside a glimpse into the exciting future trends shaping this evolving field. Additionally, you will learn how to implement ML microservices with practical examples in Java and Python. This book merges software engineering and AI, guiding readers through modern development challenges. It is a guide for innovators, boosting efficiency and leading the way to a future of impactful technology solutions. WHAT YOU WILL LEARN ● Master the principles of microservices architecture for scalable software design. ● Deploy ML microservices using cloud platforms like AWS and Azure for scalability. ● Ensure ML microservices security with best practices in data encryption and access control. ● Utilize Docker and Kubernetes for efficient microservice containerization and orchestration. ● Implement CI/CD pipelines for automated, reliable ML model deployments. WHO THIS BOOK IS FOR This book is for data scientists, ML engineers, data engineers, DevOps team, and cloud engineers who are responsible for delivering real-time, accurate, and reliable ML models into production. TABLE OF CONTENTS 1. Introducing Microservices and Machine Learning 2. Foundation of Microservices 3. Fundamentals of Machine Learning 4. Designing Microservices for Machine Learning 5. Implementing Microservices for Machine Learning 6. Data Management in Machine Learning Microservices 7. Scaling and Load Balancing Machine Learning Microservices 8. Securing Machine Learning Microservices 9. Monitoring and Logging in Machine Learning Microservices 10. Deployment for Machine Learning Microservices 11. Real World Use Cases 12. Challenges and Future Trends
Machine Learning And Optimization Models For Optimization In Cloud
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Author : Punit Gupta
language : en
Publisher: CRC Press
Release Date : 2022-02-17
Machine Learning And Optimization Models For Optimization In Cloud written by Punit Gupta and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-17 with Computers categories.
Machine Learning and Models for Optimization in Cloud’s main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition. This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud. Key Features · Comprehensive introduction to cloud architecture and its service models. · Vulnerability and issues in cloud SAAS, PAAS and IAAS · Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models · Detailed study of optimization techniques, and fault management techniques in multi layered cloud. · Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network. · Advanced study of algorithms using artificial intelligence for optimization in cloud · Method for power efficient virtual machine placement using neural network in cloud · Method for task scheduling using metaheuristic algorithms. · A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment. This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.
Microservices From Theory To Practice Creating Applications In Ibm Bluemix Using The Microservices Approach
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Author : Shahir Daya
language : en
Publisher: IBM Redbooks
Release Date : 2016-04-04
Microservices From Theory To Practice Creating Applications In Ibm Bluemix Using The Microservices Approach written by Shahir Daya and has been published by IBM Redbooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-04 with Computers categories.
Microservices is an architectural style in which large, complex software applications are composed of one or more smaller services. Each of these microservices focuses on completing one task that represents a small business capability. These microservices can be developed in any programming language. They communicate with each other using language-neutral protocols, such as Representational State Transfer (REST), or messaging applications, such as IBM® MQ Light. This IBM Redbooks® publication gives a broad understanding of this increasingly popular architectural style, and provides some real-life examples of how you can develop applications using the microservices approach with IBM BluemixTM. The source code for all of these sample scenarios can be found on GitHub (https://github.com/). The book also presents some case studies from IBM products. We explain the architectural decisions made, our experiences, and lessons learned when redesigning these products using the microservices approach. Information technology (IT) professionals interested in learning about microservices and how to develop or redesign an application in Bluemix using microservices can benefit from this book.
Managing Distributed Cloud Applications And Infrastructure
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Author : Theo Lynn
language : en
Publisher: Springer Nature
Release Date : 2020-07-20
Managing Distributed Cloud Applications And Infrastructure written by Theo Lynn and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-20 with Business & Economics categories.
The emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver quality of service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities.
Managed Software Evolution
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Author : Ralf Reussner
language : en
Publisher: Springer
Release Date : 2019-06-26
Managed Software Evolution written by Ralf Reussner and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-26 with Computers categories.
This open access book presents the outcomes of the “Design for Future – Managed Software Evolution” priority program 1593, which was launched by the German Research Foundation (“Deutsche Forschungsgemeinschaft (DFG)”) to develop new approaches to software engineering with a specific focus on long-lived software systems. The different lifecycles of software and hardware platforms lead to interoperability problems in such systems. Instead of separating the development, adaptation and evolution of software and its platforms, as well as aspects like operation, monitoring and maintenance, they should all be integrated into one overarching process. Accordingly, the book is split into three major parts, the first of which includes an introduction to the nature of software evolution, followed by an overview of the specific challenges and a general introduction to the case studies used in the project. The second part of the book consists of the main chapters on knowledge carrying software, and cover tacit knowledge in software evolution, continuous design decision support, model-based round-trip engineering for software product lines, performance analysis strategies, maintaining security in software evolution, learning from evolution for evolution, and formal verification of evolutionary changes. In turn, the last part of the book presents key findings and spin-offs. The individual chapters there describe various case studies, along with their benefits, deliverables and the respective lessons learned. An overview of future research topics rounds out the coverage. The book was mainly written for scientific researchers and advanced professionals with an academic background. They will benefit from its comprehensive treatment of various topics related to problems that are now gaining in importance, given the higher costs for maintenance and evolution in comparison to the initial development, and the fact that today, most software is not developed from scratch, but as part of a continuum of former and future releases.
Deep Learning With Azure
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Author : Mathew Salvaris
language : en
Publisher: Apress
Release Date : 2018-08-24
Deep Learning With Azure written by Mathew Salvaris and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-24 with Computers categories.
Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure Who This Book Is For Professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.
Building Microservices With Net Core
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Author : Gaurav Kumar Aroraa
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-06-14
Building Microservices With Net Core written by Gaurav Kumar Aroraa and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-14 with Computers categories.
Architect your .NET applications by breaking them into really small pieces—microservices—using this practical, example-based guide About This Book Start your microservices journey and understand a broader perspective of microservices development Build, deploy, and test microservices using ASP.Net MVC, Web API, and Microsoft Azure Cloud Get started with reactive microservices and understand the fundamentals behind it Who This Book Is For This book is for .NET Core developers who want to learn and understand microservices architecture and implement it in their .NET Core applications. It's ideal for developers who are completely new to microservices or have just a theoretical understanding of this architectural approach and want to gain a practical perspective in order to better manage application complexity. What You Will Learn Compare microservices with monolithic applications and SOA Identify the appropriate service boundaries by mapping them to the relevant bounded contexts Define the service interface and implement the APIs using ASP.NET Web API Integrate the services via synchronous and asynchronous mechanisms Implement microservices security using Azure Active Directory, OpenID Connect, and OAuth 2.0 Understand the operations and scaling of microservices in .NET Core Understand the testing pyramid and implement consumer-driven contract using pact net core Understand what the key features of reactive microservices are and implement them using reactive extension In Detail Microservices is an architectural style that promotes the development of complex applications as a suite of small services based on business capabilities. This book will help you identify the appropriate service boundaries within the business. We'll start by looking at what microservices are, and what the main characteristics are. Moving forward, you will be introduced to real-life application scenarios, and after assessing the current issues, we will begin the journey of transforming this application by splitting it into a suite of microservices. You will identify the service boundaries, split the application into multiple microservices, and define the service contracts. You will find out how to configure, deploy, and monitor microservices, and configure scaling to allow the application to quickly adapt to increased demand in the future. With an introduction to the reactive microservices, you strategically gain further value to keep your code base simple, focusing on what is more important rather than the messy asynchronous calls. Style and approach This guide serves as a stepping stone that helps .NET Core developers in their microservices architecture. This book provides just enough theory to understand the concepts and apply the examples.
Building Microservices With Net Core 2 0
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Author : Gaurav Aroraa
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-12-22
Building Microservices With Net Core 2 0 written by Gaurav Aroraa and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-22 with Computers categories.
Architect your .NET applications by breaking them into really small pieces - microservices -using this practical, example-based guide. Key Features Start your microservices journey and get a broader perspective on microservices development using C# 7.0 with .NET Core 2.0 Build, deploy, and test microservices using ASP.Net Core, ASP.NET Core API, and Microsoft Azure Cloud Get the basics of reactive microservices Book Description The microservices architectural style promotes the development of complex applications as a suite of small services based on business capabilities. This book will help you identify the appropriate service boundaries within your business. We'll start by looking at what microservices are and their main characteristics. Moving forward, you will be introduced to real-life application scenarios; after assessing the current issues, we will begin the journey of transforming this application by splitting it into a suite of microservices using C# 7.0 with .NET Core 2.0. You will identify service boundaries, split the application into multiple microservices, and define service contracts. You will find out how to configure, deploy, and monitor microservices, and configure scaling to allow the application to quickly adapt to increased demand in the future. With an introduction to reactive microservices, you’ll strategically gain further value to keep your code base simple, focusing on what is more important rather than on messy asynchronous calls. What you will learn Get acquainted with Microsoft Azure Service Fabric Compare microservices with monolithic applications and SOA Learn Docker and Azure API management Define a service interface and implement APIs using ASP.NET Core 2.0 Integrate services using a synchronous approach via RESTful APIs with ASP.NET Core 2.0 Implement microservices security using Azure Active Directory, OpenID Connect, and OAuth 2.0 Understand the operation and scaling of microservices in .NET Core 2.0 Understand the key features of reactive microservices and implement them using reactive extensions Who this book is for This book is for .NET Core developers who want to learn and understand the microservices architecture and implement it in their .NET Core applications. It’s ideal for developers who are completely new to microservices or just have a theoretical understanding of this architectural approach and want to gain a practical perspective in order to better manage application complexities.
Service Level Agreements For Cloud Computing
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Author : Philipp Wieder
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-11-06
Service Level Agreements For Cloud Computing written by Philipp Wieder and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-11-06 with Computers categories.
Service Level Agreements for Cloud Computing provides a unique combination of business-driven application scenarios and advanced research in the area of service-level agreements for Clouds and service-oriented infrastructures. Current state-of-the-art research findings are presented in this book, as well as business-ready solutions applicable to Cloud infrastructures or ERP (Enterprise Resource Planning) environments. Service Level Agreements for Cloud Computing contributes to the various levels of service-level management from the infrastructure over the software to the business layer, including horizontal aspects like service monitoring. This book provides readers with essential information on how to deploy and manage Cloud infrastructures. Case studies are presented at the end of most chapters. Service Level Agreements for Cloud Computing is designed as a reference book for high-end practitioners working in cloud computing, distributed systems and IT services. Advanced-level students focused on computer science will also find this book valuable as a secondary text book or reference.