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Journal of Intelligent Manufacturing

, Volume 26, Issue 4, pp 641–658 | Cite as

ALATO: An efficient intelligent algorithm for time optimization in an economic grid based on adaptive stochastic Petri net

  • Mohammad Shojafar
  • Zahra Pooranian
  • Mohammad Reza Meybodi
  • Mukesh Singhal
Article

Abstract

Cost and execution time are important issues in economic grids, which are widely used for parallel computing. This paper proposes ALATO, an intelligent algorithm based on learning automata and adaptive stochastic Petri nets (ASPNs) that optimizes the execution time for tasks in economic grids. ASPNs are based on learning automata that predict their next state based on current information and the previous state and use feedback from the environment to update their state. The environmental reactions are extremely helpful for teaching Petri nets in dynamic environments. We use SPNP software to model ASPNs and evaluate execution time and costs for 200 tasks with different parameters based on World Wide Grid standard resources. ALATO performs better than all other heuristic methods in reducing execution time for these tasks.

Keywords

Grid computing Petri nets  Learning automata Optimization Modeling 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Mohammad Shojafar
    • 1
  • Zahra Pooranian
    • 2
  • Mohammad Reza Meybodi
    • 3
  • Mukesh Singhal
    • 4
  1. 1.Department of Information Engineering, Electronics, and Telecommunications (DIET)Sapienza University di RomaRomeItaly
  2. 2.Department of Computer Engineering, Dezful BranchIslamic Azad UniversityDezfulIran
  3. 3.Computer and IT departmentAmirkabir Technical UniversityTehranIran
  4. 4.Electrical Engineering and Computer ScienceUniversity of CaliforniaMercedUSA

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