## 6 Conclusions  
In this paper, a BoT based workflow scheduling algorithm has been proposed for the dynamic and elastic provisioning of VM instances, that considers resource renting cost minimization constrained to user-defined deadline. The proposed model groups the workflow into BoTs based on data dependency  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Workflow** & **Source of Variation** & **SS** & **df** & **MS** & **F** & \(p\)**-Value** \\ \hline \multirow{3}{*}{Montage\_1000} & Between groups & 561,231.4 & 2 & 280615.7 & 35.683 & 3.04 \(\times\) 10\({}^{-8}\) \\  & Within groups & 212,330 & 27 & 7864.074 & & \\  & Total & 773,561.4 & 29 & & & \\ \hline \multirow{3}{*}{CyberShake\_1000} & Between groups & 1,519,506.153 & 2 & 759,753.076 & 21.027 & 3.12\(\times\) 10\({}^{-6}\) \\  & Within groups & 975,550.9 & 27 & 36,131.515 & & \\  & Total & 2,495,057.053 & 29 & & & \\ \hline \multirow{3}{*}{Epigenomics\_997} & Between groups & 1,630,867.799 & 2 & 815,433.8995 & 60.632 & 0.0 \\  & Within groups & 363,117.4 & 27 & 13,448.792 & & \\  & Total & 1,993,985.199 & 29 & & & \\ \hline \multirow{3}{*}{LIGO\_1000} & Between groups & 1,000,549.365 & 2 & 500,274.682 & 63.815 & 0.0 \\  & Within groups & 211,665.6 & 27 & 7839.467 & & \\ \cline{1-1}  & Total & 1,212,214.965 & 29 & & & \\ \hline \multirow{3}{*}{SIPHT\_1000} & Between groups & 4,717,007.744 & 2 & 2,358,503.872 & 24.935 & 7.3 \(\times\) 10\({}^{-7}\) \\ \cline{1-1}  & Within groups & 2,553,788 & 27 & 94,584.741 & & \\ \cline{1-1}  & Total & 7,270,795.744 & 29 & & & \\ \hline \hline \end{tabular} Note: SS = Sum of Squares, MS = Mean Sum of Squares, df = Degree of Freedom.  
\end{table}
Table 4: One-way ANOVA test result.  
Figure 10: Sensitivity to inaccurate estimations and task execution failure.  
and priority constraints and thereafter optimizes the allocation and scheduling of BoTs on elastic, heterogeneous and dynamically provisioned cloud resources called VMs in order to attain the proposed method's objectives. The proposed approach considers pay-as-you-go IaaS clouds having inherent features such as elasticity, abundance, heterogeneity, VM performance variation and VM provisioning delays. Mathematical model was successfully used for the dynamic provisioning of resources. The traces for the experiments were taken from real-world scientific workflow applications. Experimental results demonstrate that our proposed DSB increases the chance of deadline being satisfied and minimizes the execution cost compared to other approaches for the real-world scientific workflow applications considered.  
The future work is intended to investigate more accurate models to predict potential failures, uncertainties and performance variations of time critical applications in the real IaaS environment. Another future direction is to extend the proposed model to implement fault tolerant energy efficient elastic resource provisioning and scheduling. Moreover, future research will also evaluate the proposed model on VMs with different lengths of pricing intervals. Finally, the algorithms will be implemented in a workflow execution engine for their effective utilization in real life.  
[PERSON] carried out the conception and design of the study, performed the experiments, analyzed and interpret the data and contributed in drafting and revising the manuscript. [PERSON] made substantial contributions to the design of the study, the analysis and interpretation of the data, and critically review the manuscript. All authors read and approved the final manuscript.  
The authors declare no conflict of interest.