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

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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.

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Authors and Affiliations

  1. Department of Information Engineering, Electronics, and Telecommunications (DIET), Sapienza University di Roma, Rome, Italy

    Mohammad Shojafar

  2. Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran

    Zahra Pooranian

  3. Computer and IT department, Amirkabir Technical University, Tehran, Iran

    Mohammad Reza Meybodi

  4. Electrical Engineering and Computer Science, University of California, Merced, CA, USA

    Mukesh Singhal

Authors
  1. Mohammad Shojafar
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  2. Zahra Pooranian
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  3. Mohammad Reza Meybodi
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  4. Mukesh Singhal
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Correspondence to Mohammad Shojafar.

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Shojafar, M., Pooranian, Z., Meybodi, M.R. et al. ALATO: An efficient intelligent algorithm for time optimization in an economic grid based on adaptive stochastic Petri net. J Intell Manuf 26, 641–658 (2015). https://doi.org/10.1007/s10845-013-0824-0

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