Explore practical use cases to learn everything from Linux components, and functionalities, through to hardware and software support Key Features Gain a clear understanding of how to design a Linux environment Learn more about the architecture of the modern Linux operating system(OS) Understand infrastructure needs and design a high-performing computing environment Book DescriptionIt is very important to understand the flexibility of an infrastructure when designing an efficient environment. In this book, you will cover everything from Linux components and functionalities through to hardware and software support, which will help you to implement and tune effective Linux-based solutions. This book gets started with an overview of Linux design methodology. Next, you will focus on the core concepts of designing a solution. As you progress, you will gain insights into the kinds of decisions you need to make when deploying a high-performance solution using Gluster File System (GlusterFS). In the next set of chapters, the book will guide you through the technique of using Kubernetes as an orchestrator for deploying and managing containerized applications. In addition to this, you will learn how to apply and configure Kubernetes for your NGINX application. You'll then learn how to implement an ELK stack, which is composed of Elasticsearch, Logstash, and Kibana. In the concluding chapters, you will focus on installing and configuring a Saltstack solution to manage different Linux distributions, and explore a variety of design best practices. By the end of this book, you will be well-versed with designing a high-performing computing environment for complex applications to run on. By the end of the book, you will have delved inside the most detailed technical conditions of designing a solution, and you will have also dissected every aspect in detail in order to implement and tune open source Linux-based solutions What you will learn Study the basics of infrastructure design and the steps involved Expand your current design portfolio with Linux-based solutions Discover open source software-based solutions to optimize your architecture Understand the role of high availability and fault tolerance in a resilient design Identify the role of containers and how they improve your continuous integration and continuous deployment pipelines Gain insights into optimizing and making resilient and highly available designs by applying industry best practices Who this book is forThis intermediate-level book is for Linux system administrators, Linux support engineers, DevOps engineers, Linux consultants or any open source technology professional looking to learn or expand their knowledge in architecting, designing and implementing solutions based on Linux and open source software. Prior experience in Linux is required.

Risk classification is an important actuarial process for Insurance companies. It allows for the underwriting of the best risks, through an appropriate choice of classification variables, and helps set fair premiums in rate-making. Currently, insurance companies mainly use ad-hoc methods for risk classification, more often based on the type of expenses covered than on the distribution of the corresponding losses. The selection of classification variables is also, in general, based on rate-making variables rather than on an optimal choice criteria based on statistical methods. It is known that logistic regression is among the many sophisticated statistical methods used by the banking industry in order to select credit rating variables. Extending the method to insurance risks seems only natural. Insurance risks are not usually classified in only two categories, good and bad, as can be the case in credit rating, but in a larger number of classes. Here we consider the generalization of the model to extend the use of logistic regression to insurance risk classification.