Supporting Big Data at the vehicular edge

Supporting Big Data at the vehicular edge

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The discussion of supporting Big Data at the vehicular edge will focus on a simple case of parked vehicles. A model will be created to evaluate processing Big Data using a datacenter comprising these parked vehicles. The model will simulate a datacenter implemented on the vehicles in the parking lot of a business that operates 24 h a day, 7 days a week. The employees of the business work in staggered 8-h shifts. This provides a pool of vehicles that can serve as the basis for a datacenter for the business. The vehicles in the parking lot are provided a standard power outlet for charging their vehicles in return for the use of their computing resources. The challenge of facing the implementation of the datacenter is to maintain high availability and reliability.

Chapter Contents:

  • 12.1 Introduction and motivation
  • 12.2 The Internet of Things
  • 12.3 Big data processing
  • 12.4 Cloud computing and the datacenter
  • 12.5 A survey of recent work on vehicular clouds
  • 12.6 Our contributions
  • 12.7 The vehicle datacenter model
  • 12.8 The vehicle datacenter simulation
  • 12.8.1 Datacenter controller
  • 12.8.2 Resource manager
  • 12.8.3 Job manager
  • 12.8.4 Log manager
  • 12.8.5 Network
  • 12.8.6 Vehicles
  • 12.9 Empirical performance evaluation
  • 12.9.1 Simulation factors
  • Size of parking lot
  • Residency time of vehicles
  • Network configuration
  • Network throughput
  • Percentage of vehicles tasked
  • Number of worker objects
  • Number of simultaneous jobs
  • Size of jobs
  • 12.9.2 Response variables
  • 12.10 Simulation results
  • 12.10.1 Correlation of job completion times
  • 12.10.2 Performance between random and set job sizes
  • 12.11 Concluding remarks
  • 12.12 Looking into the crystal ball
  • References

Inspec keywords: Big Data; computer centres; vehicular ad hoc networks; traffic engineering computing

Other keywords: vehicular edge; datacenter; parked vehicles; big data; computing resources

Subjects: Traffic engineering computing; Data handling techniques; Mobile radio systems; Computer facilities

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