Experimental Study of the Cloud Architecture Selection for Effective Big Data Processing

TL;DR

This paper investigates how to select optimal cloud architectures for big data processing, considering factors like security, network quality, and dynamic architecture changes to improve efficiency and effectiveness.

Contribution

It provides an experimental analysis of cloud architecture options for big data, highlighting challenges and strategies for effective architecture selection.

Findings

Cloud architecture choice impacts data security and processing efficiency.

Dynamic architecture adaptation is necessary for optimal big data processing.

Network infrastructure considerations are critical in cloud-based big data solutions.

Abstract

Big data dictate their requirements to the hardware and software. Simple migration to the cloud data processing, while solving the problem of increasing computational capabilities, however creates some issues: the need to ensure the safety, the need to control the quality during data transmission, the need to optimize requests. Computational cloud does not simply provide scalable resources but also requires network infrastructure, unknown routes and the number of user requests. In addition, during functioning situation can occur, in which you need to change the architecture of the application - part of the data needs to be placed in a private cloud, part in a public cloud, part stays on the client.