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A task scheduling algorithm with deadline constraints for distributed clouds in smart cities.

Jincheng Zhou1,2, Bo Liu3, Jian Gao4

  • 1School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou, China.

Peerj. Computer Science
|June 22, 2023
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Summary
This summary is machine-generated.

This study introduces an efficient local search algorithm for task scheduling in smart city cloud networks. The algorithm reduces data load and latency, outperforming swarm-based methods like GA and PSO for improved cloud service efficiency.

Keywords:
Distributed cloudsLocal search algorithmSmart citiesTask scheduling

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cloud Computing

Background:

  • Smart cities leverage computing technologies and 5G for infrastructure management.
  • Cloud computing integrates data from IoT devices, sensors, and cameras for smart city operations.
  • Hierarchical distributed cloud models with micro and core clouds enhance network architecture.

Purpose of the Study:

  • To address task scheduling with deadline constraints in distributed cloud models for smart cities.
  • To reduce communication network data load and provide low-latency cloud services.
  • To enhance the efficiency of cloud computing services for local users.

Main Methods:

  • Development of an efficient local search algorithm for task scheduling.
  • Implementation of a greedy search strategy for iterative solution improvement.
  • Utilization of randomized methods for task and virtual machine reassignment.

Main Results:

  • The proposed local search algorithm demonstrates superior performance in task scheduling.
  • The algorithm effectively reduces communication network data load and latency.
  • Experimental results show the algorithm outperforms Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).

Conclusions:

  • The presented local search algorithm is an efficient solution for deadline-constrained task scheduling in smart city distributed clouds.
  • The approach enhances cloud computing efficiency by minimizing data load and latency.
  • This method offers a promising strategy for optimizing smart city cloud infrastructure.