# Constructing Reliable Computing Environments on Top of Amazon EC2 Spot Instances +
## 6 Conclusions  
In this paper, we have proposed a multi-workflow scheduling algorithm, allied with a container migration-based mechanism, to dynamically construct and readjust virtual clusters on top of non-reserved Amazon EC2 pricing model instances. Our objective is to address the unreliable behavior of Amazon EC2 spots and make it possible to use these instances to execute workflow applications constrained by user-defined deadline and budget QoS parameters. This objective implies an increase in the rate of completed tasks and in the rate of completed workflows. To achieve said objective, we developed MISER, a best-effort scheduling algorithm, which: (a) leverages containers to ease migration of tasks between spots (to deal with premature termination of SIs); and (b) dynamically re-allocates tasks that were previously scheduled to spots and whose requests were not timely fulfilled. The tests were conducted by injecting a set of 190 Montage, CyberShake, and Ligo workflow applications. Various budget and deadline limits for each workflow application were defined in order to analyze their impacts on the performances of the scheduling algorithms. The proposed solution completed almost 100% of tasks and more than 99.7% of workflows, which improves the rate of completed tasks by almost 20%, and the rate of completed workflows by at least 30%, compared with other state-of-the-art algorithms, for a worse-case scenario. What is more, MISER algorithm was able to maintain its performance with diverse deadline and budget constraints, and diverse EC2 instance pricing models. Additional interesting findings related to MISER are: (a) the quality of service provided is improved as spots are included in the scheduling process (i.e., the ratio of successfully finished tasks is kept at almost 100% while the service cost perceived by end users is improved); (b) the efficient management of diverse EC2 instances to reduce costs and execution times (i.e., the product  
Figure 3: Characterization of the schedules regarding the instance pricing model.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Budget = 0.25**} & \multicolumn{2}{c}{**Budget = 0.50**} \\ \cline{2-5}  & **Deadline = 1.0** & **Deadline = 1.5** & **Deadline = 1.0** & **Deadline = 1.5** \\ \hline Unfulfilled Spot Requests (*-OB) & 110 & 126 & 118 & 122 \\ Unfulfilled Spot Requests (*-OBS) & 175 & 184 & 175 & 174 \\ Migration of Tasks (*-OBS) & 36 & 47 & 35 & 30 \\ Additional Schedules (*-OB) & 2.05\% & 2.35\% & 2.18\% & 2.27\% \\ Additional Schedules (*-OBS) & 3.93\% & 4.30\% & 3.89\% & 3.80\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for MISER in terms of number of occurrences of unfulfilled spot requests, for both *-OB and *-OBS configurations; migration of tasks, for *-OBS configuration; and additional schedules compared to *-O configuration, for both *-OB and *-OBS configurations, and for diverse deadline and budget QoS constraints.  
of average task runtime and the monetary costs related to instances renting decreases as spots are introduced in the scheduling process); and (c) the small number of migrations observed, to deal with SIs interruptions (only 0.86% of a maximum of 5483 tasks scheduled onto SIs needed to be migrated).  
Conceptualization, A.M.S.; methodology, A.M.S. and J.G.B.; software, A.M.S.; validation, A.M.S. and J.G.B.; formal analysis, A.M.S. and J.G.B.; investigation, A.M.S. and J.G.B.; resources, A.M.S.; data curation, A.M.S. and J.G.B.; writing--original draft preparation, A.M.S.; writing--review and editing, A.M.S. and J.G.B.; visualization, A.M.S.; supervision, J.G.B.; project administration, A.M.S. and J.G.B.; funding acquisition, A.M.S. and J.G.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.