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Träfflista för sökning "WFRF:(Taheri Javid) "

Sökning: WFRF:(Taheri Javid)

  • Resultat 1-10 av 182
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  • Ahammed, Farhan, et al. (författare)
  • Finding lower bounds of localization with noisy measurements using genetic algorithms
  • 2011
  • Ingår i: Proceedings of the first ACM international symposium on Design and analysis of intelligent vehicular networks and applications (DIVANet '11). - Miami, Florida, USA : Association for Computing Machinery (ACM). - 9781450309042 ; , s. 47-54
  • Konferensbidrag (refereegranskat)abstract
    • Vehicular Ad-Hoc Networks (VANETs) are wireless networks with mobile nodes (vehicles) which connect in an ad-hoc manner. Many vehicles use the Global Positioning System (GPS) to provide their locations. However the inaccuracy of GPS devices leads to some vehicles incorrectly assuming they are located at different positions and sometimes on different roads. VANETs can be used to increase the accuracy of each vehicle's computed location by allowing vehicles to share information regarding the measured distances to neighbouring vehicles. This paper looks at finding how much improvement can be made given the erroneous measurements present in the system. An evolutionary algorithm is used to evolve instances of parameters used by the VLOCI2 algorithm, also presented in this paper, to find instances which minimises the inaccuracy in computed locations. Simulation results show a definite improvement in location accuracy and lower bounds on how much improvement is possible is inferred.
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  • Al-Dulaimy, Auday, et al. (författare)
  • BWSLICER : A bandwidth slicing framework for cloud data centers
  • 2020
  • Ingår i: Future generations computer systems. - : Elsevier. - 0167-739X .- 1872-7115. ; 112, s. 767-784
  • Tidskriftsartikel (refereegranskat)abstract
    • Bandwidth allocation is an important and influential factor in enhancing the performance of the data centers' nodes. In this paper we propose bwSlicer, a framework for bandwidth slicing in cloud data centers, that sheds light on the virtues of effective dynamic bandwidth allocation on improving the system performance and energy efficiency. Three algorithms are investigated to deal with this issue. In the first algorithm, called Fair Bandwidth Reallocation (FBR), two virtual machines co-hosted on the same node conditionally exchange bandwidth slices based on their requirements. The second algorithm, called Required Bandwidth Allocation (RBA), periodically monitors the co-hosted virtual machines and adds/removes bandwidth slices for each of them based on their bandwidth utilization. The third algorithm, called Divide Bandwidth Reallocation (DBR), divides the bandwidth of the virtual machine into slices once it finishes its execution, and distributes the slices among the co-hosted running virtual machines according to a specific policy. The proposed bandwidth slicing algorithms are emulated in a virtualized networking environment using the Mininet network emulator. The emulation results demonstrated a promising improvement ratio in execution time and energy consumption reaching up to 30%. These results present a call for action for further research into bandwidth slicing and reallocation as a viable complement to other energy-saving techniques for enhancing the energy consumption in cloud data centers.
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  • Al-Dulaimy, Auday, et al. (författare)
  • Introduction to edge computing
  • 2020
  • Ingår i: Edge Computing. - : Institution of Engineering and Technology. - 9781785619410
  • Bokkapitel (refereegranskat)abstract
    • Edge computing is the model that extends cloud computing services to the edge of the network. This model aims to move decision-making operations as close as possible to data sources since it acts as an intermediate layer connecting cloud data centres to edge devices/sensors. Transferring all the data from the network edge to the cloud data centres for processing may create a latency problem and outstrip the network's bandwidth capacity. To resolve this issue, it might be best to process data closer to the devices/sensors. This chapter will take a deep dive into edge computing, its applications, and the existing challenges related to this model.
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