Heuristic scheduling strategies focus on identifying a solution by exploiting the heuristics, an important class of algorithms based on which is list scheduling [2-12], such as heterogeneous earliest finish time (HEFT) .
At each step of HEFT, the task with the highest value of upward rank is selected and mapped to the processor with a greedy approach (i.e., the assigned processor minimizes the earliest finish time of the selected task).
The input cost-concious factor of this method is the cloud cost and used as a weight to calculate the earliest finish time EFT of each task.
 develope a method based on genetic algorithm (GA) to find an optimal scheduling, which shows to be efficient to discover optimal solutions more than Heterogeneous Earliest Finish Time (HEFT) with same length of problem size, focusing on the quality of solution and effect of muation probability on the performance of GA.
The earliest finish time
estimated by organisers is around 3.05pm, although somewhere between 3.30pm and 4pm is more likely.
The heterogeneous earliest finish time (HEFT)  uses a recursive procedure to compute the rank of a task by traversing the graph upwards from the exit task and vice-versa for critical path on a processor (CPOP) .
Before proceeding to the next section, it is necessary to discuss some scheduling attributes such as rank upward, rank downward, earliest start time, earliest finish time, and optimistic cost table, which will be used in the proposed scheduling algorithm.