RHCSA (11): Advanced Features of GNU/Linux
Mind Map
gnu_linux_advanced_features
│
├── clustering
│ │
│ ├── high-availability clusters
│ ├── load balancing
│ └── distributed computing
│
├── specialized storage
│ │
│ ├── network-attached storage (NAS)
│ ├── storage area networks (SAN)
│ └── object storage systems
│
├── virtualization
│ │
│ ├── full virtualization (KVM, QEMU)
│ ├── containerization (Docker, Podman)
│ └── para-virtualization (Xen)
│
├── cloud computing
│ │
│ ├── infrastructure as a service (IaaS)
│ ├── platform as a service (PaaS)
│ └── cloud orchestration (OpenStack, Kubernetes)
│
└── real-time computing
│
├── real-time kernels
├── deterministic scheduling
└── low-latency tuning
Alphabetical List of the Abbreviations used in this article:
Docker = An open-source platform for automating the deployment of containerized applications
DRBD = Distributed Replicated Block Device
GNU = GNU's Not Unix (a free software operating system project)
HA = High Availability
HAProxy = High Availability Proxy (a software load balancer)
IaaS = Infrastructure as a Service
Kubernetes = An open-source platform for automating container orchestration
KVM = Kernel-based Virtual Machine
LVS = Linux Virtual Server
NAS = Network-Attached Storage
NFS = Network File System
OpenStack = An open-source platform for cloud infrastructure management
PaaS = Platform as a Service
Pacemaker = An open-source high-availability resource manager
Podman = A daemonless container engine for developing, managing, and running OCI containers
QEMU = Quick Emulator
RHCSA = Red Hat Certified System Administrator
SAN = Storage Area Network
Xen = A hypervisor providing para-virtualization and hardware-assisted virtualization
How I Used Reference 1 in This Article:
On page 9 of 107, Reference 1 cites five advanced features of GNU/Linux. These five features are the 100% focus of this article:
- Clustering
- Specialized Storage
- Virtualization
- Cloud Computing
- Real-Time Computing
Executive Summary
This article, the 11th article in my RHCSA series, provides an in-depth overview of five advanced features of the GNU/Linux operating system.
Clustering enables multiple systems to work together as a unified environment. High-availability clusters ensure continuous service by shifting workloads between nodes when needed. Load balancing distributes network traffic across servers to improve efficiency, while distributed computing allows tasks to be processed across multiple connected systems for greater performance.
Specialized Storage includes network-attached storage (NAS), storage area networks (SAN), and object storage systems. NAS allows shared file access over a network, SAN offers high-speed, dedicated connections for rapid data transfer, and object storage efficiently manages large volumes of unstructured data.
Virtualization covers full virtualization with KVM and QEMU, containerization with Docker and Podman, and para-virtualization using Xen. These technologies allow multiple operating systems or isolated application environments to run on a single physical machine, enhancing flexibility and resource management.
Cloud Computing is divided into infrastructure as a service (IaaS), platform as a service (PaaS), and cloud orchestration. IaaS provides fundamental computing resources like virtual machines and storage, PaaS offers cloud-based development environments, and orchestration platforms such as OpenStack and Kubernetes automate the management of cloud resources and services.
Real-Time Computing focuses on meeting strict timing requirements through real-time kernels, deterministic scheduling, and low-latency system tuning. These features ensure fast and predictable task execution, which is critical for time-sensitive applications.
These five advanced features demonstrate the power and versatility of GNU/Linux in complex and high-demand computing environments.
Keywords: RHCSA, GNU/Linux, Advanced Features, Clustering, High-Availability Clusters, Load Balancing, Distributed Computing, Specialized Storage, Network-Attached Storage (NAS), Storage Area Networks (SAN), Object Storage, Virtualization, Full Virtualization, KVM, QEMU, Containerization, Docker, Podman, Para-Virtualization, Xen, Cloud Computing, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Cloud Orchestration, OpenStack, Kubernetes, Real-Time Computing, Real-Time Kernels, Deterministic Scheduling, Low-Latency Tuning, Linux Administration, Systems Administration, Server Management, Enterprise Computing
Credits
The folllowing research assistants were invaluable tools that allowed me to complete this article in a timely manner: Mistral (an open-source local large language model - LLM) and ChatGPT (an online portal to OpenAI's remote LLMs).
1. Clustering in GNU/Linux: An Overview
An RHCSA should be well-versed in clustering, specifically understanding the concepts of high-availability clusters, which provide redundancy and system availability. Additionally, they should have knowledge about load balancing techniques for efficiently distributing network traffic across multiple servers to prevent overloading any single server. Lastly, an RHCSA should be familiar with distributed computing concepts, as it enables applications to run across a group of interconnected computers for increased processing power.
1a. High-Availability Clusters in GNU/Linux
High-availability (HA) clusters in GNU/Linux provide continuous availability for mission-critical systems by ensuring that essential services remain accessible even during hardware failures or other disruptions. These clusters consist of multiple servers connected together, and each server runs identical copies of important applications or services to ensure redundancy.
Clustering software like Pacemaker, Corosync, and DRBD (Distributed Replicated Block Device) are essential for managing and synchronizing these servers. When a failure occurs on one server in the cluster, another server can automatically take over the failed service without any interruption of service.
To configure an HA cluster, it is necessary to follow best practices such as setting up shared storage solutions like NFS or GlusterFS for storing data that is replicated across servers. Network interfaces should also be configured appropriately to facilitate communication between the servers in the cluster.
Additionally, resource dependencies need to be defined so that critical services are always available and can start when required. Monitoring tools such as Clustat or Ganglia can help track the health and performance of the cluster and its individual components.
It's important to note that HA clusters require careful planning and configuration to ensure optimal performance, high availability, and scalability. Regular testing and monitoring are crucial for maintaining the integrity and reliability of these systems.
1b. Load Balancing in GNU/Linux
Load balancing in GNU/Linux is essential for efficiently distributing network traffic across multiple servers to prevent overloading any single server. By using load balancers, you can increase system performance, reduce response times, and improve overall availability of web applications or other services.
Load balancers work by directing incoming requests to different servers based on various factors like the current server load, server health, and geographical location. This ensures that no single server is overwhelmed with traffic while maintaining a high level of service quality.
There are several load balancing solutions available for GNU/Linux, including hardware load balancers like F5 BIG-IP or Citrix NetScaler, as well as software load balancers such as HAProxy, Nginx, and LVS (Linux Virtual Server).
To configure a load balancer in GNU/Linux, you need to install the chosen solution, create virtual servers for each target server, set up health checks to ensure that only healthy servers receive traffic, and define appropriate routing policies to distribute incoming requests effectively. Additionally, it's essential to monitor the performance of the load balancer and its individual servers to identify potential issues before they impact service availability.
1c. Distributed Computing on GNU/Linux
Distributed computing in GNU/Linux enables applications to run across multiple interconnected computers for increased processing power, faster completion times, and improved scalability. This approach allows for more efficient use of hardware resources while also providing a high level of fault tolerance.
There are several distributed computing frameworks available for GNU/Linux, including MPI (Message Passing Interface), Hadoop, and GridEngine. Each framework provides tools to manage the distribution of tasks, data transfer, and synchronization between nodes in the system.
To use distributed computing on GNU/Linux, you need to install the chosen framework and configure it appropriately for your specific needs. This may involve setting up a cluster of computers, configuring network communication between nodes, defining the resources available for each node, and configuring job submission and scheduling policies.
It's essential to monitor the performance of distributed computing systems to ensure optimal resource utilization and to identify potential bottlenecks or issues before they impact the overall system performance. Additionally, it may be necessary to troubleshoot individual nodes in the event of failures or errors.
2. Specialized Storage in GNU/Linux: An Overview
Specialized storage solutions are essential for handling large amounts of data efficiently and providing the performance, scalability, and reliability required by modern applications. In GNU/Linux, there are three main types of specialized storage: network-attached storage (NAS), storage area networks (SAN), and object storage systems.
2a. Network-attached storage (NAS)
Network-attached storage (NAS) is a file-level storage solution that provides easy access to data over a network. NAS devices offer high performance, scalability, and flexibility for small to medium-sized businesses. They are suitable for applications like file sharing, media streaming, and backup solutions.
2b. Storage area networks (SAN)
Storage area networks (SAN) provide block-level storage connectivity between servers and storage devices. SANs consist of switches, hosts, and storage devices connected through dedicated high-speed interfaces. This approach offers improved performance, scalability, and availability for large-scale applications like databases or virtualization environments.
2c. Object Storage Systems
Object storage systems are designed to handle massive amounts of unstructured data efficiently. They store objects as separate entities rather than files or blocks on a storage device. This allows for scalable, flexible, and cost-effective solutions for storing and managing large amounts of data, such as multimedia content, logs, or backup data.
2d. Specialized Storage Conclusions
To implement specialized storage in GNU/Linux, it's essential to choose the right solution based on your specific needs and requirements. This may involve configuring NAS devices, setting up SAN infrastructure, or implementing object storage systems like Ceph or Swift. Additionally, you should monitor the performance of these storage solutions to ensure optimal resource utilization and identify potential bottlenecks or issues before they impact system performance.
3. Virtualization in GNU/Linux: An Overview
Virtualization in GNU/Linux allows multiple operating systems or application environments to run on a single physical machine. It provides flexibility, better resource utilization, and isolation between workloads. There are three major types of virtualization used in GNU/Linux environments: full virtualization, containerization, and para-virtualization.
3a. Full Virtualization
Full virtualization completely simulates the underlying hardware, allowing unmodified operating systems to run as virtual machines. In GNU/Linux, two common tools for full virtualization are KVM (Kernel-based Virtual Machine) and QEMU (Quick Emulator). KVM is built directly into the Linux kernel and provides fast and efficient virtualization for running multiple operating systems simultaneously. QEMU works alongside KVM to emulate hardware components, making it possible to virtualize systems that require different architectures or devices. Together, KVM and QEMU enable powerful and flexible virtualization setups for both desktop and server environments.
3b. Containerization
Containerization is another form of virtualization that isolates applications and their dependencies within containers, rather than virtualizing an entire operating system. Containers are lightweight and use fewer resources compared to full virtual machines. Popular containerization tools in GNU/Linux include Docker and Podman. Docker has become widely known for its simplicity and vast ecosystem of pre-built container images. Podman, on the other hand, offers a more secure, daemon-less alternative that works well in enterprise environments. Both tools allow developers and system administrators to deploy, manage, and scale applications with ease.
3c. Para-Virtualization
Para-virtualization is a virtualization method that requires a modified guest operating system to interact more directly with the hypervisor, leading to better performance than full virtualization in some cases. Xen is a well-known para-virtualization platform used in many GNU/Linux systems. It provides a robust environment for running virtual machines, especially in scenarios where performance and resource efficiency are critical. While para-virtualization requires some adjustments to the guest systems, it remains a valuable option for advanced virtualization tasks.
3d. Virtualization in GNU/Linux: Conclusions
By understanding these three approaches: full virtualization, containerization, and para-virtualization; Linux users and administrators can choose the most appropriate method for their needs, balancing performance, flexibility, and security.
4. Cloud Computing in GNU/Linux: An Overview
Cloud computing in GNU/Linux refers to the use of Linux-based systems to deliver computing services over the internet. These services include servers, storage, networking, and software. Cloud computing allows organizations to scale resources quickly, reduce costs, and simplify the management of complex systems. In GNU/Linux environments, cloud computing is typically divided into three categories: infrastructure as a service (IaaS), platform as a service (PaaS), and cloud orchestration.
4a. Infrastructure as a Service (IaaS)
Infrastructure as a service (IaaS) provides virtualized computing resources over the internet. With IaaS, users can create and manage virtual machines, storage, and networking without needing to buy or maintain physical hardware. Linux-based systems are widely used to power IaaS platforms because of their reliability, security, and flexibility. Users can quickly deploy servers, adjust computing resources, and build large virtual environments as needed.
4b. Platform as a service (PaaS)
Platform as a service (PaaS) goes a step further by offering a complete development and deployment environment in the cloud. With PaaS, developers can focus on writing and deploying applications without worrying about the underlying hardware, operating systems, or networking. GNU/Linux provides the foundation for many PaaS solutions, enabling support for popular programming languages, databases, and web servers. PaaS platforms help simplify software development and speed up delivery by automating many system management tasks.
4c. Cloud Orchestration
Cloud orchestration refers to the automated management of cloud resources, including virtual machines, networks, and storage. It allows administrators to coordinate and control large cloud environments efficiently. OpenStack and Kubernetes are two of the most popular cloud orchestration tools in GNU/Linux ecosystems. OpenStack enables organizations to manage large-scale infrastructure, including virtual networks and storage systems, while Kubernetes focuses on automating the deployment, scaling, and management of containerized applications. Both tools play key roles in making cloud computing systems more efficient, reliable, and scalable.
4d. Cloud Computing in GNU/Linux: Conclusions
By understanding these three components: IaaS, PaaS, and cloud orchestration; Linux users can effectively build, deploy, and manage cloud-based environments to meet a wide variety of business and technical needs.
5. Real-Time Computing in GNU/Linux: An Overview
Real-time computing in GNU/Linux focuses on ensuring that tasks are completed within strict time constraints. This is essential for systems where delays or unpredictability can lead to failures, such as in industrial control systems, telecommunications, or embedded devices. GNU/Linux offers several tools and techniques to meet these requirements, including real-time kernels, deterministic scheduling, and low-latency tuning.
5a. Real-Time Kernels
Real-time kernels are specially modified versions of the Linux kernel that prioritize time-sensitive tasks. These kernels are designed to minimize delays and ensure that high-priority processes receive immediate attention from the system. By reducing the unpredictability found in general-purpose kernels, real-time kernels make it possible for Linux systems to handle critical workloads that require guaranteed response times.
5b. Deterministic Scheduling
Deterministic scheduling is a technique used to control the exact order and timing of task execution. In real-time systems, it is vital to know precisely when and how long a process will run. GNU/Linux provides various scheduling policies that allow system administrators to set fixed priorities and ensure consistent execution times. This helps prevent unexpected delays and ensures that critical tasks are performed on schedule.
5c. Low-Latency Tuning
Low-latency tuning involves fine-tuning system parameters to reduce delays in data processing and communication. This includes optimizing the kernel’s behavior, adjusting CPU affinities, and configuring interrupt handling to favor rapid responses. By applying low-latency tuning, administrators can further reduce response times and improve the overall performance of real-time systems.
5d. Real-Time Computing in GNU/Linux: Conclusions
Through the use of real-time kernels, deterministic scheduling, and low-latency tuning, GNU/Linux can effectively handle applications where speed and predictability are essential.
Conclusions
This concludes Article 11 of my RHCSA series. We discussed the five advanced features of GNU/Linux computer systems:
- An RHCSA should be well-versed in clustering, specifically understanding the concepts of high-availability clusters, which provide redundancy and system availability. Additionally, they should have knowledge about load balancing techniques for efficiently distributing network traffic across multiple servers to prevent overloading any single server. Lastly, an RHCSA should be familiar with distributed computing concepts, as it enables applications to run across a group of interconnected computers for increased processing power.
- Specialized storage solutions are essential for handling large amounts of data efficiently and providing the performance, scalability, and reliability required by modern applications. In GNU/Linux, there are three main types of specialized storage: network-attached storage (NAS), storage area networks (SAN), and object storage systems.
- Virtualization in GNU/Linux allows multiple operating systems or application environments to run on a single physical machine. It provides flexibility, better resource utilization, and isolation between workloads. There are three major types of virtualization used in GNU/Linux environments: full virtualization, containerization, and para-virtualization.
- Cloud computing in GNU/Linux refers to the use of Linux-based systems to deliver computing services over the internet. These services include servers, storage, networking, and software.
- Real-time computing in GNU/Linux focuses on ensuring that tasks are completed within strict time constraints. This is essential for systems where delays or unpredictability can lead to failures, such as in industrial control systems, telecommunications, or embedded devices. GNU/Linux offers several tools and techniques to meet these requirements, including real-time kernels, deterministic scheduling, and low-latency tuning.
References:
[1] 2020 - Lecture - CSCI 275: Linux Systems Administration and Security - Moe Hassan - CUNY John Jay College - NYC Tech-in-Residence Corps. Retrieved June 26, 2025 from https://academicworks.cuny.edu/cgi/viewcontent.cgi?article=1053&context=jj_oers