Abstract
Cloud computing systems have become widely used for Big Data processing, providing access to a wide variety of computing resources and a greater distribution between multi-clouds. This trend has been strengthened by the rapid development of the Internet of Things (IoT) concept. Virtualization via virtual machines and containers is a traditional way of organization of cloud computing infrastructure. Containerization technology provides a lightweight virtual runtime environment. In addition to the advantages of traditional virtual machines in terms of size and flexibility, containers are particularly important for integration tasks for PaaS solutions, such as application packaging and service orchestration. In this paper, we overview the current state-of-the-art of virtualization and containerization approaches and technologies in the context of Big Data tasks solution. We present the results of studies which compare the efficiency of containerization and virtualization technologies to solve Big Data problems. We also analyze containerized and virtualized services collaboration solutions to support automation of the deployment and execution of Big Data applications in the cloud infrastructure.
Highlights
Cloud computing systems have become widely used for implementation of Big Data processing tasks
The Docker platform is gradually gaining popularity in the field of scientific problems associated with the Big Data processing. This is due to the fact that the Docker platform provides a single mechanism for containerized applications execution on the basis of a distributed computational environment, while simultaneously providing the necessary interfaces for network ports, volumes, etc., allowing different system users to work within the standardized computing environment [30]
There are some disadvantages of containerization that should be addressed, especially in such cases as shared resources usage, weaker isolation, and security issues, comparing to virtual machines
Summary
Cloud computing systems have become widely used for implementation of Big Data processing tasks. Virtual Machines (VMs) underlie the cloud computing infrastructure layer, providing virtual operating systems. The use of virtual machines is associated with large overheads [38, 101], which can significantly limit the performance of I/O systems and efficiency of the computational resources. The containerization technology has significantly advanced recently It is based on the concept of limiting the amount of resources that are provided to an application by the computational node. Containers can be viewed as a flexible tool for packaging, delivering and orchestrating both applications and software infrastructure services They allow you to focus on a portable way to increase compatibility [73], while still using the principles of operating system virtualization. In this paper we would provide an overview of the current state of virtualization and containerization approaches and technologies in the context of Big Data tasks solution. In the last Section, we would provide conclusions on the performed analysis
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