:The environment of cloud computing has become widely used in a variety of applications and fields in recent years. Task and resource scheduling, on the other hand, is an area where there is still room for development. Task scheduling methods that allow the mapping of incoming tasks to resources are required to meet good performance data mapping in a heterogeneous computing system. Makespan is reduced and resource usage is maximized when resources and tasks are efficiently mapped. A novel scheduling approach is proposed in this work, which improves the makespan. There are two phases to the recommended method. The Tuples algorithm is used in the first phase that schedules tasks on resources. The second phase rearranges some tasks in order to improve the overall timeframe. The outcomes of the simulation show that the new approach for heterogeneous systems outperforms MASA, max-min, e-MASA, Tuples and Enhanced max-min algorithms in terms of makespan and time complexity.