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Job Completion Time in Dynamic Vehicular Cloud Under Multiple Access Points

  • Aida GhazizadehEmail author
  • Puya GhazizadehEmail author
  • Stephan Olariu
Conference paper
  • 32 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12403)

Abstract

Vehicular cloud is a group of vehicles whose corporate computing, sensing, communication and physical resources can be coordinated and dynamically allocated to authorized users. One of the attributes that set vehicular clouds apart from conventional clouds is resource volatility. As vehicles enter and leave the cloud, new compute resources become available while others depart, creating a volatile environment where the task of reasoning about fundamental performance metrics such as job completion time becomes very challenging. In general, predicting job completion time requires full knowledge of the probability distributions of the intervening random variables. However, the datacenter manager does not know these distribution functions. Instead, using accumulated empirical data, she may be able to estimate the first moments of these random variables. In this work we offer approximations of job completion time in a dynamic vehicular cloud model involving vehicles on a highway where jobs can be downloaded under multiple stations.

Keywords

Cloud computing Vehicular cloud Edge computing Internet of vehicles Connected cars 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Old Dominion UniversityNorfolkUSA
  2. 2.St. John’s UniversityQueensUSA

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