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Design of Scheduling Algorithms for Vehicular Cloud Computing

Design of Scheduling Algorithms for Vehicular Cloud Computingvon Sohan Kumar Pande Sie sparen 19% des UVP sparen 19%
Über Design of Scheduling Algorithms for Vehicular Cloud Computing

This book's elementary focuses are addressing the various challenges of the vehicular cloud environment and developing scheduling and migration algorithms. Here, we consider various influencing factors like speed, parking time, mobility pattern, vehicles¿ energy source, resource utilization, energy consumption, and cost in the algorithms¿ development process to achieve better performance. In the beginning, we propose a dynamic service migration (DSM) algorithm for vehicular clouds. The algorithm consists of three phases, estimation, assignment and migration. The performance analysis is carried out through simulationusing two scenarios of six datasets, and compared with three well-known algorithms, namely vehicular VM migration-uniform (VVMM-U), round robin and mobility and destination workload aware migration (MDWLAM) using four performance measures. The comparison results followed by statistical validation using T test show the superiority of the proposed algorithm over the existing algorithms.

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  • Sprache:
  • Englisch
  • ISBN:
  • 9786205514955
  • Einband:
  • Taschenbuch
  • Seitenzahl:
  • 192
  • Veröffentlicht:
  • 20. Oktober 2023
  • Abmessungen:
  • 150x12x220 mm.
  • Gewicht:
  • 304 g.
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Beschreibung von Design of Scheduling Algorithms for Vehicular Cloud Computing

This book's elementary focuses are addressing the various challenges of the vehicular cloud environment and developing scheduling and migration algorithms. Here, we consider various influencing factors like speed, parking time, mobility pattern, vehicles¿ energy source, resource utilization, energy consumption, and cost in the algorithms¿ development process to achieve better performance. In the beginning, we propose a dynamic service migration (DSM) algorithm for vehicular clouds. The algorithm consists of three phases, estimation, assignment and migration. The performance analysis is carried out through simulationusing two scenarios of six datasets, and compared with three well-known algorithms, namely vehicular VM migration-uniform (VVMM-U), round robin and mobility and destination workload aware migration (MDWLAM) using four performance measures. The comparison results followed by statistical validation using T test show the superiority of the proposed algorithm over the existing algorithms.

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