Docker vs. Virtual Machine: Understand the Difference between the Two

Although virtualization is still the most prominent technology in the cloud market, containerization also addresses the need for hosting business, web, and mobile applications. The adoption of these solutions seems to be shared with business CIOs.
However, the differences between container technologies such as Docker and Virtual Machines are enormous, both conceptually and in practice.
To help you understand how Virtual Machines and Container technologies work, we’ve prepared this article in which we will briefly review both technologies to see what they are and will understand their differences as well.

Docker: What is it and what are its benefits?

Docker consists of an Open Source platform developed in Go, which is a programming language developed by Google that simplifies the creation and administration of isolated environments. Unlike server virtualization, which corresponds to the total isolation of an operating system, Docker performs isolation using common kernel libraries between the host and the container.
Thus, Docker groups pieces of software from a complete file system and that covers all the resources required for its execution. That’s why it’s a container platform. This means that everything can be installed on the server and stored in the containers. In this way, the same software and its versions can be easily executed in any development environment.
Docker provides a way to standardize the application package by performing packaging inside a container, so that the environment becomes portable to any other host that has Docker already installed. Container framework, such as Docker itself, for example, includes application code, system tools, runtime, and shared libraries with the host.

Three main benefits of using containers:

  • Containers can run directly on a physical (bare-metal) server, thus avoiding allocation and consumption of resources associated with virtual servers. In this way the containers running directly on the physical server tend to ensure a lower latency and a high performance.
  • Due to its lower resource consumption, a physical server can host a larger number of containers, thus increasing its utilization.
  • Container utilization will bring great agility and flexibility to IT as developers will be able to “bundle” application dependencies with the host, making it consistent during an application portability process between development, approval and production environments.
Due to all the facilities it offers, the container is one of the main IT trends. It simplifies the application of the DevOps methodology and facilitates agile development, so much so that Google has been using this technology for more than 10 years.

Virtual Machine: What is it and what are its benefits?

Machine virtualization consists of packaging a particular application into a standard model with its own operating environment. So now regardless of the machine you are running this application your settings will be the same from the OS level to the necessary libraries and dependencies.
Having a Virtual Machine means that a virtual hard disk was created within a physical environment by means of specific software and operates independently of it. Having one hardware inside another makes it possible to run operating systems and all kinds of programs.

Comments

Post a Comment

Popular posts from this blog

Device Mesh – The new disrupter in the Digital World

Gartner’s Top most Tech disrupter, the Device mesh is a network of connected devices. What is Device Mesh? Device mesh is just a network of devices that aid communication between various entities such as humans, businesses and machines providing a more innovative cohesive user experience. It includes numerous mobile devices, electronic machines, wearable and a wide range of sensors connected through IoT. https://www.wisdomplexus.com/blogs/device-mesh-new-disrupter-digital-world/ Difference between Device Mesh and Traditional network:- In the traditional wireless network there used to be a single router which would transmit signal and all other devices were connected to it, with increasing distance the signal would go on attenuating till a point that it would be impossible for a device to stay connected to the network. Whereas in Mesh network being like a woven fabric every device connected acts as a transmitting node and passes the signal to the next device resulting in i...
Read more

Artificial Intelligence Predictions for Every Business to know in 2019

Image
Artificial Intelligence abbreviated as AI is the self-performance of machines and computers without any human interference which means it is the ability of machine’s intelligence to manage tasks on their own. What is the Future of Artificial Intelligence ? It is presently blooming in all areas of the global market, be it medicines, education or technology machines are replacing human workforce everywhere. But still, it is said to be in the infant stage as some states it will replace humans completely in coming years while others are yet not fully aware of the concept. The answer to this still lies between these two predictions. There are assumptions for Artificial Intelligence that You Must Know to Make 2019 big for Your Business. Let us have a look into how will the year be for Artificial Intelligence: Many Businesses use chatbots for faster services, replacing human resources, but 2019 will introduce “ Virtual Agents ” a real big thing for machine learning as now custo...
Read more

Harnessing Tensor Processing Units: Transforming the Landscape of Machine Learning

Image
  Tensor Processing Units (TPUs) are specialized hardware accelerators designed by Google to enhance the performance of machine learning tasks. Unlike traditional processors, TPUs are application-specific integrated circuits (ASICs) optimized for tensor operations, which are fundamental to deep learning algorithms. This unique architecture allows TPUs to excel in processing large volumes of data and executing complex neural networks efficiently. What Makes TPUs Unique? TPUs were developed to meet the growing computational demands of machine learning, particularly for tasks involving matrix multiplications and other tensor operations. Their design is tailored specifically for these mathematical operations, allowing them to outperform general-purpose CPUs and even GPUs in certain AI workloads. While GPUs were initially created for graphics processing and later adapted for AI, TPUs are built from the ground up for machine learning tasks, making them more efficient in this domain. ...
Read more