What is a Virtual Machine? 3 Types of Virtual Hostings
Infrastructure
27.11.2021
14 min read
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Want to know everything about the virtual machines? Read the article to find out. We'll discuss how virtual machine works, tell about all types of server virtualization and give links to the best virtual machine hosts.
Simply put, a virtual machine or VM is a simulation of a computer inside another computer. It is an artificial programmable environment that makes it possible to imitate a fully functional operating system on top of the one already installed on your device.
They are mostly used by developers who have to deal with different workspaces based on different software platforms and test their projects using a variety of tools that are not always cross-platform. Also, VM’s are popular among enthusiasts who are eager to try out specific Linux distributives.
How does a virtual machine work
Typically, you have a host machine and a guest machine. A host machine is a computer whose hardware will be used as a basis for virtual “devices”. The guest machine is an artificial computer that takes part of the real hardware and ties it together as a structure to run the operating system.
You can create several guest machines and run them on one host. For instance, installing Ubuntu Linux and Windows 10 on an Apple computer and using it in parallel as you would do with two real devices. They are less powerful and a bit restricted in their capabilities but in essence not really different from actual computers.
How to use personal virtual machines
There are several ways to launch a VM on your hardware. Usually, it resembles installing a special application (VMWare or Parallels Desktop) and choosing an operating system to download on it. A bit of mouse clicking without any hassle. Simple as that.
The interfaces of such apps are quite user-friendly so you won’t struggle even if you have no experience of working with virtual machines. If you know how to install OS on a real device then you know how to do it with a virtual one. Moreover, they all have great documentation which will help to get things done right.
Pros and cons of the virtual machines
The biggest advantage of VMs is the opportunity to work with an isolated operating system without sacrificing the one you already have installed.
It means that you have the essence of another PC without actually buying another PC. Furthermore, it is easy to deploy. Just a few clicks and the new virtual machine is ready to use. No USB sticks, no rebooting, no hard disk partitioning, no time-consuming setting up, or other frustrating things to worry about.
What you will have to sacrifice is performance. Virtual machines tend to be slower than real computers. Even if you have a reasonably powerful PC with 32Gb of RAM and an 8-core chip, you won’t be able to use all its potential, and artificial devices will struggle to perform some tasks.
Best apps to create Virtual machines on different OSes
If you want to run virtual machines on Linux, we would recommend you try out the cross-platform and the open-source application VirtualBox. It was created by Oracle.. This app is free and runs really well on Linux-based OSes. As an alternative, you might consider an app called Boxes created by the developers of Gnome Desktop and Gnome OS.
For those of you who consider yoursleves a Mac-person, we’d recommend an application called Parallels Desktop. It is native, the most advanced and performant virtual machine on Mac with features like quick OS-install. But it is a bit pricey. As an alternative, you might want to install VirtualBox.
Windows users usually prefer VMware Workstation. It is one of the most powerful solutions to quickly create and maintain functional and reliable virtual machines. It uses all the underlying Windows technologies to achieve the best performance for guest OSes. It is the best virtual machine for Windows 10 and if you don’t want to pay you can use a restricted free version called VMware Player that will be enough to install some Linux distributions and try them out.
Server-side virtual machines
When we talk about server-side virtualization, we usually mean a huge set of technologies and practices that help engineers decompose complex infrastructures, make them secure, make them more reliable, and much more cost effective to use on a large scale in today’s IT industry.
Normally, server virtualization implies techs used by a business. And there are many ways people at large corporations utilize virtualization. Creating a large number of tools to simplify a variety of tasks and create brand new tools based on virtualization technologies.
And there are many engineers around the globe who have spoken about this. Like Matthew Portnoy, author of a book called Virtualization Essentials, who once said:
«Virtualization in computing often refers to the abstraction of some physical component into a logical object. By virtualizing an object, you can obtain some greater measure of utility from the resource the object provides. For example, Virtual LANs (local area networks), or VLANs, provide greater network performance and improved manageability by being separated from the physical hardware».
Pros and cons of server virtualization
Let’s talk about the advantages and disadvantages of using a virtual machine on a server.
The main thing which should be considered an advantage of virtual machines on servers is the price of such technology. It helps to make hosting websites, databases, and web applications cheaper. Create artificial networks and subsystems. Additionally, it helps developers around the world to deploy their project in a few quick clicks without ordering real hardware and spending time configuring it.
Speaking of cons we must say that there are few disadvantages of virtual machines. The most noticeable drawbacks are:
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lack of security (as with some types of virtualization)
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and lack of performance (while using options with modest payment plans)
What can be virtualized?
Virtualization is a broad term that can be defined in a variety of ways depending on the purpose of a given type of virtualization. That’s why you often stumble upon titles like “Application virtualization”, “Storage virtualization”, etc.
Some sources claim that you can only find five types of virtualization, while others believe there are seven. We will discuss just four that are used most regularly (and are closely connected with the term VM) and explain the difference between different types of virtualization in general.
Application
Small virtual machine representing a single product encapsulated with its dependencies. You can create a virtual copy of an app installed on the remote PC and use it as if it was installed on your device. Some types of application-level virtualization make it possible to use only the hardware of the host machine, but some utilize the resources of both guest and host computers.
Storage
This type of virtualization helps businesses around the world to store massive amounts of data and home users to divide hard drives into virtual sections for personal convenience.
Yep, when you partition off the disk you are virtualizing storage making it easier to isolate files from each other but physically they exist on the same hardware.
Large companies do the opposite. They bind thousands of real disks into one massive virtual one.
OS
We already discussed this type of virtualization at the beginning of the article. OS simulation implies using virtual machines on the server to create digital workspaces.
Virtual machines installed on top of the remote machine can be considered a platform to develop applications on and test them out. Furthermore, such virtualization is popular among webmasters and server administrators who use VMs on the servers for maximum control.
Network
You’ve probably heard of this one. Virtual private networks (or VPN’s) are often used in enterprise areas and sometimes by generic customers for more specific purposes.
Virtual networks help large companies create private webspace inside the internet. This private network is accessible via special software and makes it possible for team members to live across multiple continents but work together as if the whole party were sitting inside one office using the local network.
Furthermore, a VPN is used to imitate the webspace of different countries, so users can access websites and applications that are not available in the country they live in.
Types of server virtualization
Above, we’ve peeped into what things can be virtualized. Below, we will talk about different types of virtualization. Most specialists divide them into three groups (but you may find more on the web). All imply specific relationships between the hardware of the host machine (a real one) and the software of the guest machine (a virtual one). So, it is important to pinpoint that when we speak about virtualization types, we don’t touch on the technological realization of things.
Full virtualization
The first type is the easiest to understand. Full virtualization means creating some kind of virtual machine that uses the real hardware of the host machine exclusively and at the same time becomes completely isolated from it. This kind of virtualization makes the process of creating and maintaining VMs more compatible and portable. It is easier to launch a fully functional OS on top of the host using full virtualization. But at the same time, this type of virtualization is less performant than its modern alternatives like para or OS-level technics widely used today.
OS-level virtualization (containerization)
This kind of virtualization is useful when you need to create instances of an operating system or certain applications within an already running OS. It provides administrators with the tools that allow the quick deployment of many containers (OSes or apps), scale them, port, copy, reorganize, etc.
But at the same time, containerization in some cases makes the whole platform less secure and stable (but it is not true in the case of Docker, for example).
Para-virtualization
When virtual machines are controlled via an application called hypervisor, they are called para-virtualized. It means that they use a specific layer of software between the RAM, CPU and software of the guest OS. This layer helps them to get the necessary part of the hardware to launch the OS and apps and run them smoothly.
Virtualization technologies
It is time to discuss more specific essences like real applications and sets of tools that are used in the virtualization area to create VMs on different host systems.
These software products are quite similar to the ones we reviewed above but they were created precisely for the use of administrators and developers who run VMs on remote servers. We will review four popular solutions used by hosting providers to virtualize fully-fledged OSes on the machines in their data centers.
ESXi
ESXi is a hypervisor developed by VMWare, one of the pioneers in the industry of creating and configuring VMs. What makes ESXi popular is the opportunity to install a virtual machine right on top of the real hardware, neutralizing the software layer between the hypervisor and the host machine. Because of its approach ESXi easily becomes one of the quickest and performant technologies to work with even though it has some disadvantages because of used technics. ESXi brings a user-friendly graphical interface to manipulate the VMS and advanced mechanisms that allow administrators of guest systems to deploy identical systems on other hardware platforms easily without any delays and glitches.
OpenVZ
OpenVZ is a technology that demonstrates how OS-level virtualization could work. The biggest advantage of OpenVZ is the ability to create many cheap Linux-based virtual workspaces with the capabilities of real servers but there are too many drawbacks you will encounter using this software:
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It can incorrectly distribute resources between many virtual machines.
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It makes it impossible for users to have any impact on the core of the system.
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It only supports Linux and there’s no way to use any other OS.
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It is not as secure as most of the concurrent software products.
Xen
Xen is a cross-platform hypervisor that is also an open-source project. Xen is absolutely free, it is compact and easy to install on any Linux distributive or FreeBSD.
The biggest advantage of Xen is the ability to use para-virtualization. That is why most VMs built with Xen run much faster and demonstrate impressive reliability. Speaking of disadvantages we might consider the lack of a graphical interface and not so the user-friendly interface to control the VMs. It can be quite hard to cope with Xen especially if you’re a novice in this area.
KVM
KVM is a technology built into the Linux kernel. One of the most powerful and functional platforms to date. It allows every VM hosted on the computer to have its own kernel. Because of the layer of technologies that lie in the foundation of KVM, it is possible to create VMs that are performant, isolated, secure, and not really restricted in terms of functionality. Even though KVM is based on Linux it is possible to launch Windows as a guest operating system.
Types of virtual hostings
Described technologies are used to create and support hosting products. Servers with virtualization are the most prevalent option now and they are extremely important for hosting providers because they simultaneously lower the price of the servers and raise the performance of cloud computing solutions.
At the moment, we have three hosting categories that you can use as a platform for your applications.
Shared hosting
The cheapest way to host a website on the web. Renting shared hosting you get access to containers launched inside a Linux-based operating system that generally works as a small bit of storage on the SSD also used by tenants using the same host as you. It is possible to deploy a fully functional static website using shared hosting as a platform, but you should be ready to deal with all the caveats that OpenVZ technology brings such as a not infrequent lack of hardware resources to run the deployed project smoothly.
VPS/VDS
Virtual Private Servers are mostly created using technologies like KVM. They are independent and isolated OSes inside the host machine. VPSes can provide users with all the benefits of a KVM such as an opportunity to deploy dynamic web applications, store massive databases, install and use Windows Server as a digital workspace, etc.
One of the reasons why VDSes became extremely popular is the price. It costs much less than a dedicated server but provides almost identical capabilities that you would usually expect from a real computer. KVM-based virtual machines even make it possible to influence the kernel of the OS.
Cloud computing
A modern solution that ideally suits projects of any size and complexity. The biggest plus of cloud-based systems is scalability. You can gradually make it grow with the functionality of your app and the audience that uses it.
Cloud hosting is a network of computers around the world developed using the most advanced technologies available. That’s why it is the most performant and secure way to create any virtualized products (workspaces, VPNs, digital data storage, etc.).
How to choose virtual machine hosting?
The choice depends on the demands of your project. You can use the cheapest option to deploy a static site but if you want something more powerful and useful you’ll have to get over to VDS or a cloud-based solution.
Also, we strongly recommend paying for the hosting because cheap virtual machine hosting providers exist but free ones are mostly run by crooks. It might also be dangerous to host any project on free hosting.
Best virtual machine hosts
- Digital Ocean — one of the most popular hosting providers that offer reliable machines for any use. Well-balanced in terms of price and functionality. A good choice for experienced developers and administrators.
- Netlify — easy to use hosting that can automatically convert GitHub projects into running applications.
- Hostman — an advanced alternative to Netlify that not only offers an opportunity to deploy apps using GitHub and GitLab repositories but also provides customers with a set of pre-made servers.
- InMotion — a powerful VPS host that can boast 99.99% uptime. It works almost 24/7 without interruptions so your project will be accessible to your customers most of the time.
- IONOS — the cheapest web hosting for virtual machines that makes it possible to create your own VM for just $1.
Here it is. Now you know how virtualization works, why you need to install a virtual machine and how to do this. We recommend trying out demo versions of apps like VMware and Parallels to decide which one will become your go-to solution. If you’re trying to find the best server to host a VM you might want to read our article about the 8 best VPS hosts.
Infrastructure
27.11.2021
14 min read
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Infrastructure
Data Processing Unit (DPU): Meaning & How It Works
A DPU is a special type of processor designed for data processing. The abbreviation stands for Data Processing Unit. Technologically, it is a kind of smart network interface card.
Its main purpose is to offload the central processing unit (CPU) by taking over part of its workload.
To understand why DPUs are important and what potential this technology holds, we need to go back several decades.
A Bit of History
In the 1990s, the Intel x86 processor, combined with software, provided companies with unprecedented computing power. Client-server computing began to develop, followed by multi-tier architectures and then distributed computing. Organizations deployed application servers, databases, and specialized software, all running on numerous x86 servers.
In the early 2000s, hypervisors became widespread. Now, multiple virtual machines could be launched on a single powerful server. Hardware resources were no longer wasted and began to be used efficiently.
Thanks to hypervisors, hardware became programmable. Administrators could now write code to automatically detect and initiate virtual machines, forming the foundation of today’s cloud computing paradigm.
The next step was network and storage virtualization. As a result, a powerful CPU became the foundation for emulating virtually everything: virtual processors, network cards, and storage interfaces.
The downside of this evolution was that pressure on the CPU increased significantly. It became responsible for everything, from running the operating system and applications to managing network traffic, storage I/O operations, security, and more. All system components began competing for CPU resources.
The CPU’s functions went far beyond its original purpose. At this point, two major trends emerged:
The appearance of specialized hardware for artificial intelligence (AI).
The evolution of programmable hardware.
CPU, GPU, and DPU
AI workloads require parallelism, which cannot be achieved with a general-purpose CPU. Thus, graphics processing units (GPUs) became the driving force behind AI development.
Originally designed to accelerate graphics rendering, GPUs evolved into coprocessors for executing complex mathematical operations in parallel. NVIDIA quickly seized this opportunity and released GPUs specifically designed for AI training and inference workloads.
GPUs were the first step toward offloading the CPU. They took over mathematical computations. After that, the market saw the emergence of other programmable chips.
These microchips are known as application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs), which can be programmed for specific tasks, such as optimizing network traffic or accelerating storage I/O operations. Companies like Broadcom, Intel, and NVIDIA began producing processors that were installed on network cards and other devices.
Thanks to GPUs and programmable controllers, the excessive load on the CPU started to decrease. Network functions, storage, and data processing were delegated to specialized hardware. That’s the simplest explanation of what a coprocessor is: a device that shares the CPU’s workload, allowing hardware resources to be used to their full potential. The secret to success is simple: each component does what it does best.
Understanding the Architecture
Before discussing DPUs, we should first understand what an ASIC processor is and how it relates to network interface cards.
Standard and Specialized Network Cards
A network card is a device that allows a computer to communicate with other devices on a network. They are also referred to by the abbreviation NIC (Network Interface Controller).
At the core of every NIC is an ASIC designed to perform Ethernet controller functions. However, these microchips can also be assigned other roles. The key point is that a standard NIC’s functionality cannot be changed after manufacturing; it performs only the tasks it was designed for.
In contrast, SmartNICs have no such limitations. They allow users to upload additional software, making it possible to expand or modify the functionality of the ASIC, without even needing to know how the processor itself is structured.
To enable such flexibility, SmartNICs include enhanced computing power and extra memory. These resources can be added in different ways: by integrating multi-core ARM processors, specialized network processors, or FPGAs.
DPU Characteristics
Data Processing Units are an extension of SmartNICs. Network cards are enhanced with support for NVMe or NVMe over Fabrics (NVMe-oF).
A device equipped with an ARM NVMe processor can easily handle input/output operations, offloading the central processor. It’s a simple yet elegant solution that frees up valuable CPU resources.
A DPU includes programmable interfaces for both networking and storage. Thanks to this, applications and workloads can access more of the CPU’s performance, which is no longer burdened with routine network and data management tasks.
Market Solutions
One of the best-known solutions is NVIDIA® BlueField, a DPU line first introduced in 2019, with the third generation announced in 2021.
NVIDIA BlueField DPU is designed to create secure, high-speed infrastructure capable of supporting workloads in any environment. Its main advantages include:
Zero-trust architecture, ensuring strong security within data centers.
Low latency with direct data access.
Data transfer speeds up to 400 Gbit/s.
SDKs that help developers build high-performance, software-defined, cloud-optimized services accelerated by DPUs using standard APIs.
Another company in this space is Pensando, which develops the Distributed Services Card, a data-processing card featuring a DPU. It includes additional ARM cores and hardware accelerators for specific tasks such as encryption and disk I/O processing.
Google and Amazon are also developing their own ASIC-based projects:
Google TPU (Tensor Processing Unit): a processor designed for machine learning, optimized to run advanced ML models in Google Cloud AI services.
AWS Graviton: an ARM-based chip designed to provide the best performance-to-cost ratio for cloud workloads running in Amazon EC2.
What’s Next?
It is quite possible that the DPU will become the third essential component of future data center servers, alongside the CPU (central processing unit) and GPU (graphics processing unit). This is due to its ability to handle networking and storage tasks.
The architecture may look like this:
CPU: used for general-purpose computing.
GPU: used for accelerating AI applications.
DPU: used for processing and transferring data.
It appears that DPUs have a promising future, largely driven by the ever-growing volume of data. Coprocessors can breathe new life into existing servers by reducing CPU load and taking over routine operations. This eliminates the need to look for other optimization methods (such as tweaking NVIDIA RAID functions) to boost performance.
Estimates suggest that currently, around 30% of CPU workload is consumed by networking functions. Transferring these tasks to a DPU provides additional computing power to the CPU. This can also extend the lifespan of servers by several months or even years, depending on how much CPU capacity was previously dedicated to networking.
By adding a DPU to servers, clients can ensure that CPUs are fully utilized for application workloads, rather than being bogged down by routine network and storage access operations. And this looks like a logical continuation of the process that began over 30 years ago, when organizations started building high-performance systems based on a single central processor.
09 October 2025 · 6 min to read
Infrastructure
Service Level Agreement (SLA): Meaning, Metrics, Examples
An SLA is an agreement that defines the level of service a company provides to its customers. This term is usually used in IT and telecommunications.
Unlike standard service contracts, a Service Level Agreement provides a very detailed description of service quality, operating modes, response times to incidents, and other parameters.
Main Characteristics of an SLA
A Service Level Agreement usually has the following characteristics:
Maximum possible transparency of all processes and interactions between the service provider and the client. When drafting the contract, vague wording that could be interpreted ambiguously in one direction or another is avoided.
Clearly defined rights and obligations understood by all participants in the agreement. For example, a provider commits to ensuring 99.9% service availability and to pay compensation if a lower figure is recorded, while the client has the right to request that compensation.
Expectation management. For instance, a client might expect 24/7, ultra-fast support even for minor issues, while the provider cannot offer such a service. In this case, the client should either lower their expectations or sign a contract with another provider. A third option is also possible: the provider may raise the service level if it benefits their business processes.
The agreement specifies the timeframes for fixing issues and solving other problems. It also describes possible compensations that the client may receive if the company fails to meet the declared metrics.
An SLA does not always need to be a large document. The main thing is that it clearly describes the core parameters of the service in understandable terms. For example, the AWS S3 SLA is only one page long. It lists monthly uptime percentages and the amount of compensation the client receives if the service fails to meet those thresholds.
What is Usually Included in an SLA
The example above from Amazon Web Services is not a standard; it is just one possible format tailored to a specific service.
An IT SLA often includes the following sections:
The procedure for using the service.
Responsibilities of both parties, including tools for mutual monitoring of performance.
Specific steps for troubleshooting and restoring functionality.
The agreement may also specify its term. In some cases, the parties describe in detail the procedure for adding new requirements for functionality or service availability.
When describing service quality, its parameters are also disclosed. These typically include:
Service availability.
Response time to a problem.
Time to fix incidents.
The SLA may also specify a metric for operating hours.
When describing payment procedures, it may indicate the billing model (e.g., pay-as-you-go, fixed rate, etc.). If penalties are provided, the SLA will specify the situations in which the provider must pay them. If the client is entitled to compensation, the SLA also describes the relevant situations and payment procedures.
Key SLA Parameters
SLA parameters are metrics that can be measured. The agreement should not contain vague phrases like “issues will be resolved quickly, before you even notice.” Such wording is unclear and prevents all participants from organizing proper workflows.
For example, the support schedule metric should clearly define when and for which groups of users technical support is available.
Suppose a company divides its clients into several groups:
Group 1: 24/7 phone and chat support.
Group 2: phone and chat support only on weekdays.
Group 3: chat-only support on weekdays.
Metrics are necessary so that all participants understand which services they receive, when, and in what scope.
From this, several key characteristics follow:
Metrics must always be publicly available.
Their descriptions must be unambiguous for all parties.
Clients must be notified in advance about metric changes.
When defining metrics, it’s important not to set overly strict requirements, as this significantly increases costs.
For instance, suppose a typical specialist can resolve a problem in 4 hours, while a higher-level expert can do it in 2 hours. Writing “2 hours” as the SLA metric is not ideal, as it would immediately make the expert’s work more expensive. If you specify “1 hour,” costs rise further due to the increased risk of penalties for non-compliance.
Other important metrics can include response time to a client request. The values may differ depending on the client’s status and problem criticality.
For example, a company providing IT outsourcing services might have:
Premium clients: response within 15 minutes.
Basic clients: response within 24 hours.
All of this must be clearly reflected in the SLA.
In addition to response time, there’s also incident resolution time. The logic for this parameter is similar: even if a client is important, requests are prioritized based on criticality.
For example:
If a client’s local office network stops working and all processes halt, that issue must be prioritized. The SLA may state that local network troubleshooting should take no more than 5 hours.
If the same client needs to add a few new devices to an already working network, the resolution time may be several hours or even days.
The combination of response time and resolution time forms downtime.
These and other parameters must be described in the SLA and accepted by all parties before cooperation begins. This approach reduces conflicts; everyone understands what to expect from each other.
Service Availability
For providers, one of the most important SLA parameters is service availability. It is usually measured in days, hours, or minutes over an agreed period. For example, a provider guarantees that a cloud computing service will be available 99.99% of the time during a year.
At first glance, the difference between SLA 99 and SLA 100 may seem small. But in absolute terms, it’s significant.
At 99%, you agree that servers may be down up to 4 days per year.
At 100%, downtime should be zero—something no company can guarantee.
That’s why SLAs are usually written with “nines”: e.g., 99.9%, 99.99%, etc.
For example, Hostman.com guarantees 99.98% uptime, meaning total annual downtime will not exceed 1 hour 45 minutes.
Some providers promise “five nines”: 99.999% uptime, or less than 15 minutes of downtime per year. But this is not always the best option. Two points to consider:
The higher the SLA percentage, the higher the cost.
Not every client needs such a high level. In most cases, 99.982% uptime (or slightly higher) is sufficient.
It’s important to check not only the number of nines but also the time unit used for measurement. By default, SLA indicators are calculated annually. For example, 99.95% availability equals no more than 4.5 hours of downtime per year.
If the contract doesn’t explicitly say that the time unit is “per year,” be sure to clarify, as some providers disguise monthly values as annual.
Another key concept is aggregate availability, which equals the lowest of all measured values.
Benefits of an SLA
Signing and adhering to an SLA benefits both parties.
For the company, it defines obligations and protects against unreasonable client demands, such as urgently fixing a minor issue in the middle of the night.
Other benefits include:
The provider can use the SLA to organize both external and internal processes, such as introducing different support levels depending on service criticality and client importance.
Clients gain clarity about what services they can expect, in what timeframes, and in what order, helping them plan their core operations.
SLA vs. SLO: What’s the Difference
An SLA can also be viewed as an indicator of user satisfaction, ranging from 0% to 100%.
Absolute satisfaction (100%) is impossible, just as it’s impossible to guarantee 100% uptime. Therefore, when choosing metrics, one should be realistic and select achievable values.
For example, if your team doesn’t provide 24/7 support, you shouldn’t promise it. When the team expands, you can update the SLA and delight clients by offering round-the-clock assistance.
To monitor service levels internally, another system is used: SLO (Service Level Objective). These are the target values the provider aims to achieve.
Example: Current capabilities are handling 50 tickets per business day, working 9:00 to 18:00, five days a week. These metrics are fixed in the SLA and shown to clients.
Meanwhile, the SLO document sets internal goals, for example, increasing the number of handled tickets to 75 per day or switching to 24/7 support. This directly affects the company’s future service level.
How to Create a Proper SLA
Start with a descriptive section, which usually includes:
A glossary
System description
Participant roles (users, support specialists)
Boundaries of operation: geography, time, functionality
The next section describes the services provided, giving the client a full understanding of what they can expect when signing with the provider.
Then comes the main section, describing the service level. It should include metrics that reflect quality and are easily measurable, as well as metric values that are specific numbers guiding all participants.
You can end the SLA with references to other documents that regulate service processes.
At all stages of preparing an SLA, remember: it is a regulatory document. Its main goal is control. The more control over the process, the better the SLA. If there is no control, such an agreement is meaningless.
Checklist: What to Consider When Preparing an SLA
If you are not signing but drafting an SLA to offer clients, pay attention to the following points:
Users. In large systems, divide users into groups and manage them separately. This helps allocate resources efficiently and avoid overload from different client types.
Services. Consider the criticality of each service for each client group. Example: You provide a CRM to trading companies. If they can’t use it, they lose money and complain, meaning it’s a high-criticality service. Printer replacement or user account creation can wait until tomorrow.
Service quality parameters. They must align with business goals and client needs. A typical example is incident resolution times, e.g., 24/7 support versus 9 a.m. to 5 p.m. on weekdays only.
An SLA is a document that must be announced to all users whenever it is introduced or updated, regardless of privilege level or service criticality.
SLA is a management tool that constantly evolves. You may find that current quality parameters harm business processes or no longer meet client expectations. In that case, management should decide to optimize processes or improve services.
The main goal of SLA indicators is not to attract users but to ensure open dialogue with them. Every participant accepts the agreement and commits to following it.
Violation of an SLA is grounds to claim compensation and terminate cooperation.
09 October 2025 · 9 min to read
Infrastructure
What is Docker: Application Containerization Explained
Docker is software for containerizing applications. Today, we’ll talk about what containerization and Docker are, what they are used for, and what advantages they bring.
Containerization
Containerization is one of the methods of virtualization. To understand it better, let’s take a brief historical detour.
In the 1960s, computers couldn’t perform multiple tasks at once. This led to long queues for access to such rare machines. The solution was to distribute computing power among different isolated processes. That’s how the history of virtualization began.
Virtualization is the allocation of computing resources to isolated processes within a single physical device.
The main development of virtualization came during the Internet era. Imagine you’re a business owner and you want your company to have a website. You need a server connected to the global network. Today, that’s as easy as visiting hostman.com and choosing a server that fits your needs.
But in the early days of the internet, such convenient services didn’t exist. Companies had to buy and maintain servers on their own, which was inconvenient and expensive. This problem led to the rise of hosting providers: companies that purchased hardware, placed it in their facilities, and rented out servers.
As technology advanced, computers became more powerful, and dedicating a full physical server to a single website became wasteful. Virtualization helped: several isolated virtual machines could run on one computer, each hosting different websites. The technology allowed allocating exactly as many resources as each site needed.
However, that still wasn’t enough. As the internet evolved, the number of applications required for running a website grew, and each required its own dependencies. Eventually, it became “crowded” within a single virtual machine. One workaround was to host each application in its own virtual machine, a kind of virtual “matryoshka doll.” But a full VM was still excessive for a single application: it didn’t need a full OS instance. Meanwhile, virtual machines consumed a lot of resources, much of which went unused.
The solution was containerization. Instead of running a separate virtual machine for each application, developers found a way to run them in isolation within the same operating system. Each container includes the application, its dependencies, and libraries: an isolated environment that ensures consistent operation across systems.
Docker
What is a program? It’s a piece of code that must be executed by the CPU.
When you run a container, Docker (through the containerd component) creates an isolated process with its own namespace and file system. To the host system, the container looks like a regular process, while to the program inside it, everything appears as if it’s running on its own dedicated system.
Containers are isolated but can communicate with each other via networks, shared volumes, or sockets, if allowed by configuration.
Data Storage
Isolation from the host OS raises a natural question: how to store data?
Docker Volume: a storage unit created and managed by Docker itself. It can be located anywhere: within the host’s file system or on an external server.
Bind Mount: storage manually created by the user on the host machine, which is then mounted into containers during runtime.
tmpfs Volume: temporary in-memory storage. It is erased when the container stops.
In production environments, volumes are most commonly used, as Docker manages them more securely and reliably.
Docker Architecture
Docker’s architecture consists of several key components that work together to build, run, and manage containers:
Docker Host
A physical or virtual machine running the Docker Engine. This is where containers and images are executed.
Docker Engine (Docker Daemon)
The central service responsible for building, running, and managing containers. Since Docker 1.11, Docker Engine has used containerd, a low-level component that directly manages container lifecycles (creation, start, stop, and deletion).
containerd
A container runtime that interacts with the operating system kernel to execute containers. It’s used not only by Docker but also by other systems such as Kubernetes. Docker Engine communicates with containerd via an API, passing commands received from the client.
Docker CLI (Client)
The command-line interface through which users interact with Docker. CLI commands are sent to the Docker Daemon via REST API (usually over a Unix socket or TCP).
Docker Image
A Docker image is a template that includes an application and all its dependencies. It’s similar to a system snapshot from which containers are created.
Dockerfile
A text file containing instructions on how to build an image. It defines the base image, dependency installation commands, environment variables, and the application’s entry point.
Docker Container
A Docker container is a running instance of an image. A container is isolated from other processes and uses host resources through Docker Engine and containerd.
Docker Registry
A repository for storing and distributing Docker images. There are public and private registries. The most popular public one is Docker Hub, which Docker connects to by default.
Docker Compose
A tool for defining and running multi-container applications using YAML files. It allows developers to configure service dependencies, networks, and volumes for entire projects.
Advantages of Docker
Security
What does isolation provide in terms of security?
An isolated application cannot harm the host operating system.
It has no access to the host’s file system, preventing data leaks.
Any application-related crash won’t affect the host OS.
Compatibility
A container image can be run on any device with Docker installed.
Automation
Docker automates application deployment and configuration, saving time and reducing human error.
Shared Repositories
Docker users have access to repositories with thousands of ready-to-use images for various purposes.
Resource Efficiency
Unlike virtual machines, Docker containers don’t require a separate OS instance, allowing better use of computational resources.
Using Docker
Now let’s move from theory to practice. The first thing we need to do is install Docker.
Installation
Installation begins at the official website: docker.com. Go to the “Get Started” section and choose the version for your operating system. In our case, it’s Windows. Installation guides for other OSs are also available. After installation, a system reboot is required.
Docker requires a hypervisor, special software that enables multiple operating systems to run simultaneously. We’ll use WSL2 (Windows Subsystem for Linux 2).
Docker installs WSL2 automatically, but you must manually download the latest Linux kernel update. Go to Microsoft’s website, download, and install the update package. After rebooting, Docker Desktop will open.
Running a Python Script
Let’s print the message “Hello, World” to the console using a simple Python script:
#!/usr/bin/python3
print("Hello World")
Since we’re not running the script directly, we need a shebang—that’s the first line in the script. In short, the shebang tells the Linux kernel how to execute the script. Let’s name our file the classic way: main.py.
Now open the command line. To run the script, execute:
docker run -v D:\script_dir:/dir python:3 /dir/main.py
Let’s break this down:
docker run runs a container
-v mounts a directory (bind mount)
D:\script_dir is the directory with our script
/dir is the mount point inside the container
python:3 is the image
/dir/main.py is the executable file (our script)
What happens when this command is executed? Docker searches for the python:3 image first locally, then in the registry, and deploys it. Next, it mounts our script directory into the container and runs the script inside it.
Conclusion
In this article, we explored what Docker is, how it works, and even ran our first script. Docker and containerization are not a cure-all, but they’re invaluable tools in modern software development.
08 October 2025 · 7 min to read
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