With increasing computing demand the need for tuned intelligence-based solutions is most required. Most of the focus has been given by the researcher to the scheduling of parallel tasks dynamically to more than one processor and in the current scenario, it is more demandable. Although many DAG scheduling algorithms are available but less focused on dynamic scheduling. Through our projected paper we want to introduce the approach Dynamic task scheduling algorithm DTSA for scheduling task at run time using DAG with an additional factor regarding processor self-Reconfiguration Capacity, which is an important parameter of distributed computing System. Through DTSA we want to sketch out an adaptive task arrangement algorithm that gives the hybrid result of run-time scheduling of DAG and adaptation of tenant configuration by the processor according to computing needs. Finally, A DAG-based dynamic task arrangement with dependency consideration between the tasks and with the use of machine learning (ML) for self-reconfiguration of a processor is proposed for obtaining the optimal task allocations with the optimal Makespan.
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