Cloud computing is one of the growing technology usage for the day-to-day business operations in today's IT industry. The diverse features of cloud such as on-demand self-service, quality of service, pay-per-usage pricing, virtualization and elasticity make the cloud more popular in industries as well as research communities. However, the mapping of the cloud resources in forms of virtual machines (VMs) to fulfill the customer requests is very challenging and a well-known NP-Complete problem. In this paper, we propose an allocation-aware task scheduling (ATS) algorithm for heterogeneous multi-cloud systems. The algorithm has three phases, namely matching, allocating and scheduling that aim to map the customer requests (or tasks) to the VMs of the clouds such that the overall completion time i.e., makespan is minimized. Moreover, the algorithm introduces a new phase called allocating to reschedule the tasks to meet the requirement of scheduling strategy. We perform rigorous experiments on benchmark as well as synthetic datasets and compare the experimental results by extending two existing multi-cloud scheduling algorithms as per the proposed model. The results clearly indicate that the proposed algorithm outperforms both the algorithms in terms of makespan and average cloud utilization