# Survey on Secure Scientific Workflow Scheduling in Cloud Environments
## 3 Scientific Workflow Scheduling in the Cloud  
Cloud computing can boost data science workflows by providing data availability and accessibility, data processing and analysis, data visualization and presentation, and data security and compliance. Cloud computing can help data scientists work faster, smarter, and more efficiently on their data science projects. Scheduling of scientific workflows in cloud computing introduces the following challenges [3; 8; 19; 20]:  
1. Mapping task classes to virtual resources results in a significant make-span, and the challenge is in identifying a minimal set of ideal schedules that maximize performance according to user-defined quality of service parameters, such as cost and speed. 2. A user-controlled scheduler assigns resources in a cloud environment. The challenge lies in determining the types and quantities of resources required for the workflow application to function effectively. Resource overprovisioning enhances performance but escalates costs, while resource under-provisioning detrimentally affects efficiency. 3. Dependencies on data and control flow between tasks increase the wait time before a task is ready to start, which lengthens the makespan. Figure 4 below illustrates numerous classifications of methods and techniques for workflow scheduling [16; 21]. Reinforcement learning is a machine learning methodology that addresses decision-making in dynamic contexts [22; 23]. Metaheuristics are more computationally intensive  
Figure 3: The architecture of a conventional cloud-based scientific workflow system [18]. algorithms that can be utilized to address a wide range of optimization problems. They also attempt to find more necessary schedules by investigating various options and using guided search. Metaheuristics can be divided into two primary categories: population-based search and single-solution search [20; 24]. A heuristic is a collection of guidelines for solving a particular problem in a reasonable amount of time. By integrating a workflow application and understanding cloud characteristics, the heuristic approach finds a timetable that meets the user's requirements [16; 18; 24]. Hybrid combines two heuristic algorithms, PSO and GA, or hyper with reinforcement learning or another way [25; 26; 27]. Hyper-heuristic is an automated methodology for selecting or generating heuristics to solve hard computational search problems [27]. Table 1 shows the differences, benefits, and limitations of each approach used in scheduling. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Approach** & **Differences** & **Benefits** & **Limitations** \\ \hline \multirow{6}{*}{**Reinforcement Learning**} & - & Uses agents to learn optimal scheduling policies through trial and error. & - & Adaptive to dynamic environments. & - & Requires large training time and data. \\  & - & Uses agents to learn optimal scheduling policies through trial and error. & - & Can optimize long-term objectives. & - & May struggle with complex, \\  & - & - & Learned from experience. & - & - & high-dimensional state spaces. \\  & - & High-level strategies guiding other heuristics to explore the search space (e.g., genetic algorithms, particle swarm optimization). & - & Capable of finding near-optimal solutions. & - & No guarantee of finding the optimal solution. \\  & - & Problem-specific algorithms that use domain knowledge to find good enough solutions quickly. & - & Easy to implement. & - & May require fine-tuning of parameters. \\  & - & - & Requires less computational resources. & - & Often computationally expensive. \\ \hline \multirow{6}{*}{**Heuristics**} & - & & & & - & May not find optimal solutions. \\  & - & Problem-specific algorithms that use domain knowledge to find good enough solutions quickly. & - & - & Easy to implement. & - & Can be inflexible for different problem types. \\ \cline{1-1}  & - & - & Requires less computational resources. & - & Susceptible to getting stuck in local optima. \\ \hline \end{tabular}
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