Notice of RetractionAn improved LLF scheduling algorithm based on fuzzy inference in the uncertain environments

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

LLF(Least Laxity First) is one of classic dynamic real-time task scheduling algorithms. Considering the unsteadiness and unpredictability of a practical task running environment due to the unsteadiness of network communication and the time estimation deviation, it is necessary to introduce fuzzy concept and theory to the scheduling field of embedded soft real-time application systems.. In this paper, we proposed an improved fuzzy LLF scheduling model based on fuzzy inference which is more suitable for periodic tasks of an embedded soft real-time system in an uncertain environment. In our scheduling model, all task are periodic and a task´s criticality and slack time are described with fuzzy set. In our scheduling algorithm, a task´s scheduling priority is gotten by looking up the inference rule table with its fuzzy slack time and fuzzy criticality patterns. Tasks with shorter fuzzy slack time and higher fuzzy criticality are scheduled first. The simulation test shows that our scheduling model has less deadline missing ratio than traditional pure LLF scheduling algorithm and the important tasks have less deadline missing ratio than that of others tasks in an overloaded uncertain embedded soft real-time system.