USE OF KUBERNETES AND FARGATE TECHNOLOGIES IN SECURITY SYSTEMS

With the advent of artificial intelligence, it has become apparent that its application is inevitable in all areas of the economy, business, financial institutions, technical control, cybersecurity; and its practical use is crucial in control systems, security, video surveillance, operational work, financing operations, business optimization processes, etc. The article presents an innovation, aimed to increase security by using artificial intelligence technologies.

Today, the possibilities of using artificial intelligence in security systems are extremely wide, especially in the field of operational production. First, it is advisable to use it for customer support, as it will allow reaching a new level of comfort for customer service. Furthermore, it is extremely useful to apply artificial intelligence technologies to detect fraud.

This problem is crucial for the security sector, as there is a trend towards an increase in the number and improvement of these methods. In such a situation, artificial intelligence can solve the problem, since its tools allow you to identify the factors, which testify to fraud, and to assist in the investigation of criminal cases.

Artificial intelligence is able to learn, so by processing large amounts of data related to previous criminal cases, such technologies can independently prevent subsequent cases in real time. Besides using Kubernetes as a serverless service, Fargate really helps meet the demand for dedicated servers without using servers at all.

Fargate technology: definition and characteristics, pros and cons

Fargate is a container-based software engine for serverless computing, which lets you fully concentrate on app creation.

With Fargate, you mustn't allocate and operate the servers. Furthermore, this core lets you highlight the resources and pay for them separately for each application. Plus, built-in app isolation increases operational security.

Fargate allots the required quantity of computing resources, so you do not need to select samples and scale cluster volume. You pay solely for the resources that are essential to control containers. You mustn’t even think about over-allocation of resources and payment for extra servers.

Fargate drives every module or task on a separate core, providing them with their own insulated computing medium. Due to this, the app gets a built-in workload isolation and advanced security.

The advantages of Fargate technology

Key benefits of AWS Fargate include:

  • shifting focus from infrastructure management to application development and control;
  • rational selection of the necessary resources;
  • supple pricing;
  • reliable built-in insulation and safety;
  • simple app tracking and broad monitoring;
  • a huge assortment of additional open source tools, which collect metrics and logs to track applications;
  • absence of necessity to over-allocate resources and pay for extra servers;
  • workload insulation and advanced safety for each task or module;
  • interaction only with containers and payment exclusively for them.

The disadvantages of Fargate technology

  • Inability to assemble a file system. Nevertheless, if your demand requires a montage, AWS ensures ECS EC2 Linux. Due to this, you get full monitoring over the server.
  • Learning difficulty. It takes a long time to get accustomed. Performing just several tasks is not difficult, but if you deal with a lot of tasks, it gets a little tricky.
  • Relatively high cost. Is it pricey? Yeah. However, ultimately, it becomes cheaper. The one moment you might take into account is pushing the containers. Fargate cost depends on the quantity of Virtual Central Processing Units and Random Access Memory for each task.

Summing up, the flexibility, availability, security, and performance of AWS Fargate make it ideal for businesses that must keep up with consumer demand.

Kubernetes technology: definition and characteristics, pros and cons

Kubernetes is open source software for automating the deployment, scaling, and controlling of container-based apps. It supports major containerization technologies, including Docker and rkt; support of hardware virtualization technologies is also possible.

K8s reduces build, delivery, and scaling times for containerized applications. This platform allows you to plan the launch and deployment of containers, as well as scale them to the desired state and manage their life cycle. Kubernetes combines containers into logical groups for ease of management. Its main characteristics are cross-platform, extensibility, successful usage of declarative configuration and automation.

The original version was developed by Google for internal needs, later the system was transferred under the control of the Cloud Native Computing Foundation. Today, the project is supported by many companies, including Microsoft, RedHat, IBM, and Docker.

The advantages of Kubernetes technology

Kubernetes is considered to be a container system or even platform with the widest functionality. From the point of view of using K8s in Big Data systems, the following benefits of Kubernetes are most essential:

  • automatic load balancing due to constant monitoring of performance and resource usage information;
  • appropriate distribution of running applications across the entire virtual cluster;
  • easy scaling of containers;
  • creation of apps with additional extensibility;
  • infrastructure as a code that is stored somewhere;
  • idempotency, the ability to add a release with one button: release rollbacks occur fast enough, and the system introspection is quite convenient;
  • availability of API and command line interface for manual control of container distribution and load between system nodes;
  • ensuring information security through the mechanism of network policies and role-based authorization of users by the key;
  • flexible customization of metadata for services, pods, and other entities;
  • the presence of means of logical isolation of applications, commands, and workloads in one cluster in order to provide the driving apps and commands with the minimum necessary set of privileges to use the resources;
  • support of apportioned and networked file systems, such as, for example, HDFS for Apache Hadoop, due to the primordial focus on Big Data clusters;
  • the presence of dashboards — visual showcases with important indicators.

The disadvantages of Kubernetes technology

With all the benefits of K8s, this container cluster management system has the following limitations, which significantly complicate its use in practice:

  • a large number of specific concepts and interdependent entities (pod, service, cubicle, controller, etc.);
  • poor documentation that does not describe the system in sufficient detail;
  • adding an extra level of abstraction, which increases the complexity and fragility of the system;
  • the lack and high cost of experienced specialists who are fluent in this DevOps technology;
  • the complexity of the system and a huge amount of information;
  • the need to study a large stack of technologies.

Nevertheless, Kubernetes is an excellent option for Big Data projects, when you need to launch many container clusters, work with high-load networks, as well as with cloud PaaS / IaaS solutions. Furthermore, its broad functionality and flexible network security policies make K8s an almost ideal tool for deploying the scalable Big Data systems and building a reliable continuous integration and application delivery pipeline (CI / CD pipeline).

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