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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach.

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Summary
This summary is machine-generated.

This study introduces a novel model for efficient data replication in multi-cloud and edge computing environments, optimizing resource sharing for smart cities and the Internet of Things (IoT). The model minimizes costs while enhancing data availability and access speed.

Keywords:
data replicationedge serversformal methodsinternet of things (IoT)machine learningmulti-cloudpartial replicationregressionsystem soundness

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Area of Science:

  • Cloud Computing
  • Edge Computing
  • Internet of Things (IoT)
  • Smart City Management
  • Big Data Management

Background:

  • Smart city and IoT management presents complex, multidimensional challenges, particularly in cloud and edge computing.
  • Resource sharing is a critical component for enhancing overall system performance in these distributed environments.
  • Managing multi-petabyte datasets and increasing user/resource numbers in distributed applications requires advanced solutions.

Purpose of the Study:

  • To address the limitations of current big data management approaches in heterogeneous multi-cloud systems.
  • To propose a novel model for data replication that optimizes data access, availability, and cost in IoT-based multi-cloud environments.
  • To improve the scalability and expendability of data management in complex distributed systems.

Main Methods:

  • Developed a data replication model that minimizes a cost function considering storage, host access, and communication costs.
  • Employed a learning mechanism for relative weights of cost components based on historical data, adapting to different cloud environments.
  • Mathematically validated the proposed model for soundness and efficacy.

Main Results:

  • The model ensures data replication enhances availability while simultaneously decreasing overall data storage and access costs.
  • It effectively balances server load and improves data access times.
  • The proposed approach avoids the overheads associated with traditional full data replication techniques.

Conclusions:

  • The developed model provides a mathematically sound and valid solution for efficient data management in multi-cloud and edge computing.
  • It offers a significant improvement over traditional methods for handling large datasets in IoT and smart city contexts.
  • The research contributes to more scalable, cost-effective, and performant distributed data management systems.