ALATO: An efficient intelligent algorithm for time optimization in an economic grid based on adaptiv

What is it about?

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 ASPN.

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Why is it important?

This work is the first work which proposed a new adaptive approach based on Stochastic petri net models and apply this work in resource allocation/resource provisioning in economic grid with mathematical optimization techniques which is based on learning automata. Besides, we test our work with several real/state of the art related economic grid resource/scheduling techniques in dynamic test-beds.