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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines.

Akif Quddus Khan1, Nikolay Nikolov2, Mihhail Matskin3

  • 1Department of Computer Science, Norwegian University of Science and Technology-NTNU, 2815 Gjøvik, Norway.

Sensors (Basel, Switzerland)
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a method to integrate storage-as-a-service (StaaS) with big data pipelines, optimizing data processing. A ranking system helps select the best storage option based on cost, performance, and user needs.

Keywords:
big data pipelinesdata localitydata placement strategiessoftware containersstorage-as-a-service

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Area of Science:

  • Computer Science
  • Data Engineering
  • Cloud Computing

Background:

  • Big data pipelines process large, fast, and diverse datasets using sequential steps.
  • Implementing data pipelines on the computing continuum (cloud/fog/edge) is complex.
  • Local storage presents challenges in cost, maintenance, availability, security, and backup.

Purpose of the Study:

  • To propose a generic approach for integrating storage-as-a-service (StaaS) with data pipelines.
  • To develop a storage option ranking method considering cost, proximity, network performance, encryption, and user preferences.
  • To evaluate the effectiveness of the proposed approach for dynamic storage selection.

Main Methods:

  • Integration of StaaS with data pipelines executed on-premise or in the cloud.
  • Development of a ranking algorithm for storage options based on five key parameters.
  • Evaluation of data transfer performance and parameter utility through user scenarios.

Main Results:

  • Demonstrated effectiveness of the proposed approach in enhancing data transfer performance.
  • Showcased the utility of individual ranking parameters (cost, proximity, network, encryption, user weights).
  • Validated the feasibility of dynamic storage option selection for diverse user needs.

Conclusions:

  • The proposed generic approach effectively integrates StaaS with data pipelines.
  • The ranking method facilitates informed and dynamic selection of optimal storage solutions.
  • This work addresses challenges in managing storage for scalable big data processing.