Hierarchical and distributed data storage for computing continuum Articles uri icon

publication date

  • June 2025

start page

  • 1

end page

  • 16

issue

  • 107931

volume

  • 174

International Standard Serial Number (ISSN)

  • 0167-739X

Electronic International Standard Serial Number (EISSN)

  • 1872-7115

abstract

  • The Internet of Things (IoT) has transformed how everyone interacts with the environment. Over the past few years, this field has experienced exponential growth, which has led to difficulties in efficiently managing the data generated by these devices and has posed new challenges for cloud infrastructures. As the number of devices involved increases, latency and bandwidth issues become increasingly critical in these systems. To address these issues, architectures such as fog and edge computing emerged that proposed bringing information processing and storage closer to the data generators. This reduced the distance the data had to travel, thereby improving latency and bandwidth and reducing potential bottlenecks.
    These architectures have evolved into a new concept known as the computing continuum. This approach, based on dynamic collaboration between the cloud, fog, and edge, creates a continuous infrastructure of computational resources that optimizes data processing at each network level as needed. The computing continuum presents important challenges that need to be addressed at multiple levels: the application/algorithmic level (programming paradigms), middleware level (deployment, execution, scheduling, monitoring, data storage, transfer, processing, and analysis), and resource management level.
    The work introduced in this article addresses the challenges related to the efficient storage and transfer of data between the different levels across the computing continuum. We propose a distributed and parallel file system for this kind of infrastructure that can be used transparently at all levels. This file system can be deployed at different levels (fog, edge, and cloud) hierarchically, allowing the execution of applications at each level and efficient data transfer between these levels and the cloud. It also facilitates the development of IoT applications capable of efficiently transferring data by using typical file system calls.
    The work presents and validates this data storage system at different levels: two IoT devices (Raspberry Pi 1 & 4), an emulated environment, a controlled simulation, and a performance analysis with Amazon Cloud Services.

subjects

  • Computer Science

keywords

  • computing continuum; high-performance computing; internet of things; big data; distributed and parallel file system