## 5 Performance Analysis and Discussion
### Evaluation Results  
We implemented WRPS [34], SCS [9] and HEFT [29] algorithms. Then, we evaluated these scheduling methods and our proposed DSB scheduling algorithm on a standard and real set of scientific workflow applications. Finally, we compared their results with our scheduling model. The workflows were executed by changing the deadline. The experiments were repeated 30 times for each workload instance and the average value of output metrics are reported in this section. The effect of varying deadline over the cost was evaluated, as shown in Figures 3 and 4. The proposed DSB scheduling algorithm successfully scheduled maximum number of tasks while meeting the deadline constraint with the average success rate of 97.93% compared to the success rates of WRPS, SCS and HEFT which are 92.68%, 82.86% and 66.93% respectively. Figures 3 and 4 shows the average cost of Montage and CyberShake workflow respectively for 1000 tasks. The structure of Montage and CyberShake consists of many small-sized tasks in each level with almost same priorities, dependency constraints and deadline. It can be seen that the average cost of the proposed DSB is lower because the workflow tasks are ordered on the basis of their priorities, dependency constraints and sub-deadlines and afterwards partitioned into horizontal BoTs. The cost of SCS and HEFT are comparatively high because these methods take longer to process tasks of large size. Similarly, it can be seen that the cost of the proposed DSB was comparatively lower than the other algorithms which shows that the proposed DSB exhibits better performance than its counterparts. Figure 5 shows that SCS and HEFT missed the deadline when it is minimum for executing Epigenomics workflow, while in all other cases, the deadline was successfully met. The proposed method not only execute maximum number of tasks successfully without violating the deadline but also achieves lowest cost for Epigenomics workflows. Figure 6 illustrates that the proposed DSB shows best performance under hard deadline. This is due to the fact that LIGO Inspiral Analysis workflow consists of blocks of tasks and each block create specific results on a portion of data. These results must be created with minimum makespan. Our proposed method can execute the blocks in each level in parallel by partitioning them in horizontal groups while taking care of the deadline and dependency constraints. The SIPHT workflow has different parts and their intermediate output is required as input to the last node to achieve the final output. Our proposed algorithm prioritizes reuse of rented intervals and schedule the different parts of the SIPHT by considering priority and sub-deadline of each task and map it with the VM that leads to minimum cost of the task on the assigned VM, as shown in Figure 7.  
Figure 4: Average cost of CyberShake under varied normalized deadline. Figure 3: Average cost of Montage under varied normalized deadlines. It can be seen from the results that the proposed method DSB shows significant improvement in cost when the deadline was minimum. Moreover, it showed better resource management by exploiting efficient level of parallelism. Figure 8 shows the normalized deadline obtained for each workflow with different scheduling algorithms. In the proposed DSB, the greater value of ND represents that it could achieve an efficient schedule map with lowest cost under the user-defined deadline constraint. Its value less than 1 denotes  
Figure 5: Average cost of Epigenomics under varied normalized deadline. Figure 6: Average cost of LIGO Inspiral Analysis under varied normalized deadline. Figure 7: Average cost of SIPHT under varied normalized deadline. that the algorithm was unable to generate a schedule map while satisfying the deadline constraint. Moreover, the proposed DSB achieves lower cost with workflows having longer deadlines because they are scheduled on least expensive VMs.