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Cloud Capacity Management helps readers in understanding what the cloud, IaaS, PaaS, SaaS are, how they relate to capacity planning and management  and which stakeholders are involved in delivering value in the cloud value chain. It explains the role of capacity management for a creator, aggregator, and consumer of cloud services and how to provision for it in a 'pay as you use model'. This involves a high level of abstraction and virtualization to facilitate rapid and on demand provisioning of services. The conventional IT service models take a traditional approach when planning for service capacity to provide optimum services levels which has huge cost implications for service providers. This book addresses the gap areas between traditional capacity management practices and cloud service models. It also showcases capacity management process design and implementation in a cloud computing domain using ITSM best practices. This book is a blend of ITSM best practices and infrastructure capacity planning and optimization implementation in various cloud scenarios. Cloud Capacity Management addresses the basics of cloud computing, its various models, and their impact on capacity planning. This book also highlights the infrastructure capacity management implementation process in a cloud environment showcasing inherent capabilities of tool sets available and the various techniques for capacity planning and performance management. Techniques like dynamic resource scheduling, scaling, load balancing, and clustering etc are explained for implementing capacity management.  .

Computer science. --- Information systems. --- Engineering & Applied Sciences --- Computer Science --- Information Technology --- General and Others --- Cloud computing. --- Database management. --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Computers. --- Computer Science. --- Computer Science, general. --- Information Systems and Communication Service. --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic brains --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Machine theory --- Calculators --- Cyberspace --- Informatics --- Science --- Electronic data processing --- Web services --- Distributed processing --- Computer Communication Networks. --- Computer network architectures. --- Computer Systems Organization and Communication Networks. --- Architectures, Computer network --- Network architectures, Computer --- Computer architecture --- Computer communication systems. --- Computer organization. --- Organization, Computer --- Electronic digital computers --- Communication systems, Computer --- Computer communication systems --- Data networks, Computer --- ECNs (Electronic communication networks) --- Electronic communication networks --- Networks, Computer --- Teleprocessing networks --- Data transmission systems --- Digital communications --- Electronic systems --- Information networks --- Telecommunication --- Cyberinfrastructure --- Network computers

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Discover the methodologies and best practices for getting started with container services monitoring using Prometheus, AppDynamics, and Dynatrace. The book begins with the basics of working with the containerization and microservices architecture while establishing the need for monitoring and management technologies. You’ll go through hands-on deployment, configuration, and best practices for Prometheus. Next, you’ll delve deeper into monitoring of container ecosystems for availability, performance, and logs, and then cover the reporting capabilities of Prometheus. Further, you’ll move on to advanced topics of extending Prometheus including how to develop new use cases and scenarios. You’ll then use enterprise tools such as AppDynamics and Wavefront to discover deeper application monitoring best practices. You’ll conclude with fully automated deployment of the monitoring and management platforms integrated with the container ecosystem using infrastructure-as -code tools such as Jenkins, Ansible and Terraform. The book provides sample code and best practices for you to look at container monitoring from a holistic viewpoint. This book is a good starting point for developers, architects, and administrators who want to learn about monitoring and management of cloud native and microservices containerized applications. You will: Examine the fundamentals of container monitoring Get an overview of the architecture for Prometheus and Alert Manager Enable Prometheus monitoring for containers Monitor containers using Wavefront Use the guidelines on container monitoring with enterprise solutions AppDynamics and Wavefront.

Application software. --- Open source software. --- Computer programming. --- Computer Applications. --- Open Source. --- Computers --- Electronic computer programming --- Electronic data processing --- Electronic digital computers --- Programming (Electronic computers) --- Coding theory --- Free software (Open source software) --- Open code software --- Opensource software --- Computer software --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Programming --- Webometrics. --- Bibliometry, Web --- Cybermetrics --- Internetometrics --- Metrics, Web --- Netometrics --- Web bibliometry --- Web metrics --- Webometry --- Quantitative research --- World Wide Web --- Research

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Discover the methodologies and best practices for getting started with Google cloud automation services including Google Cloud Deployment Manager, Spinnaker, Tekton, and Jenkins to automate deployment of cloud infrastructure and applications. The book begins with an introduction to Google cloud services and takes you through the various platforms available to do automation on the GCP platform. You will do hands-on exercises and see best practices for using Google Cloud Deployment Manager, Spinnaker, Tekton, and Jenkins. You’ll cover the automation aspects of the Google Cloud Platform holistically using native and upcoming open source technologies. The authors cover the entire spectrum of automation from cloud infrastructure to application deployment and tie everything together in a release pipeline using Jenkins. Pro Google Cloud Automation provides in-depth guidance on automation and deployment of microservices-based applications running on the Kubernetes platform. It provides sample code and best practice guidance for developers and architects for their automation projects on the Google Cloud Platform. This book is a good starting point for developers, architects, and administrators who want to learn about Google cloud automation. You will: Gain the fundamentals of Google’s automation-enabling services See an architecture overview for Google Cloud Deployment Manager, Spinnaker, Tekton, and Jenkins Implement automation for infrastructure and application use cases Automate microservices-based applications running on GKE Enable Google Cloud Deployment Manager, Spinnaker, Tekton, and Jenkins .

Application software. --- Data protection. --- Computer Applications. --- Security. --- Data governance --- Data regulation --- Personal data protection --- Protection, Data --- Electronic data processing --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Cloud computing. --- Web services --- Distributed processing

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Welcome to your hands-on guide to artificial intelligence for IT operations (AIOps). This book provides in-depth coverage, including operations and technical aspects. The fundamentals of machine learning (ML) and artificial intelligence (AI) that form the core of AIOps are explained as well as the implementation of multiple AIOps uses cases using ML algorithms. The book begins with an overview of AIOps, covering its relevance and benefits in the current IT operations landscape. The authors discuss the evolution of AIOps, its architecture, technologies, AIOps challenges, and various practical use cases to efficiently implement AIOps and continuously improve it. The book provides detailed guidance on the role of AIOps in site reliability engineering (SRE) and DevOps models and explains how AIOps enables key SRE principles. The book provides ready-to-use best practices for implementing AIOps in an enterprise. Each component of AIOps and ML using Python code and templates is explained and shows how ML can be used to deliver AIOps use cases for IT operations. What You Will Learn Know what AIOps is and the technologies involved Understand AIOps relevance through use cases Understand AIOps enablement in SRE and DevOps Understand AI and ML technologies and algorithms Use algorithms to implement AIOps use cases Use best practices and processes to set up AIOps practices in an enterprise Know the fundamentals of ML and deep learning Study a hands-on use case on de-duplication in AIOps Use regression techniques for automated baselining Use anomaly detection techniques in AIOps Who This Book is For AIOps enthusiasts, monitoring and management consultants, observability engineers, site reliability engineers, infrastructure architects, cloud monitoring consultants, service management experts, DevOps architects, DevOps engineers, and DevSecOps experts.

Computer software --- Artificial intelligence. --- Development. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Development of computer software --- Software development

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Cloud Capacity Management helps readers in understanding what the cloud, IaaS, PaaS, SaaS are, how they relate to capacity planning and management  and which stakeholders are involved in delivering value in the cloud value chain. It explains the role of capacity management for a creator, aggregator, and consumer of cloud services and how to provision for it in a 'pay as you use model'. This involves a high level of abstraction and virtualization to facilitate rapid and on demand provisioning of services. The conventional IT service models take a traditional approach when planning for service capacity to provide optimum services levels which has huge cost implications for service providers. This book addresses the gap areas between traditional capacity management practices and cloud service models. It also showcases capacity management process design and implementation in a cloud computing domain using ITSM best practices. This book is a blend of ITSM best practices and infrastructure capacity planning and optimization implementation in various cloud scenarios. Cloud Capacity Management addresses the basics of cloud computing, its various models, and their impact on capacity planning. This book also highlights the infrastructure capacity management implementation process in a cloud environment showcasing inherent capabilities of tool sets available and the various techniques for capacity planning and performance management. Techniques like dynamic resource scheduling, scaling, load balancing, and clustering etc are explained for implementing capacity management.  .

Computer science --- Information systems --- Computer. Automation --- ICT (informatie- en communicatietechnieken) --- cloud computing --- computers --- informatica --- informatiesystemen --- computerkunde --- Cloud computing. --- Database management.