# Survey on Secure Scientific Workflow Scheduling in Cloud Environments
## 8 Survey of Secure Workflow Scheduling Approaches in Cloud Environments
The researchers say the suggested job performs significantly better than traditional methods in terms of execution time and overall cost. [PERSON] et al. 's [57] paper presented the Security Prioritized Heterogeneous Early Finish Time (SPHEFT) algorithm to maximize the security overhead and guarantee ratio of the workflow activities in cloud systems. In this case, SPHEFT gives jobs with higher security requirements, which are assigned to virtual machines with higher reliability and higher priorities. The traditional HEFT algorithm and SPHEFT are evaluated experimentally for various tasks. Experimental findings demonstrate that SPHEFT performs better regarding security overhead and has superior efficiency in raising the task guarantee ratio. [PERSON] et al. 's [58] paper proposed two scheduling techniques: simulated annealing (PSSA) and privacy and security-aware list scheduling (PSLS), aimed at addressing the constrained optimization problem at hand. PSLS allocates a user-defined deadline to each work and assigns them to a hybrid cloud resource that meets the specifications while minimizing costs. PSSA employs PSLS and simulated annealing to rearrange work lists for iterative enhancement. Simulation tests conducted reveal that PSLS surpasses four existing algorithms in financial cost optimization overall, whereas PSSA exceeds PSLS further, although with increased runtime costs. [PERSON] et al. [59] proposed the approach to decision-making known as minimum weight optimization (MWO). Multi-objective algorithms use this technique to choose a set of permutations that offer the optimal compromise between conflicting objectives. By comparing several weights, (MWO) seeks to identify the optimum option and refines the search for the ideal answer iteratively. The study compared the suggested technique to Pareto dominance, multi-criteria decision-making (MCDM), linear normalization I and II, and weighted aggregated sum product evaluation using common scientific workflows with conflicting goals. MWO outperforms these methods. [PERSON] et al. 's [60] research uses the security-prioritized mapping approach for infrastructure as a service cloud computing to create an SPMWA model. Implementing a security-prioritized allocation technique under precedence limits should enhance workflow processing in dangerous situations. This strategy prioritizes secure workloads and allocates resources to more dependable virtual machines to reduce cloud system failure. Decreased task failures mean assigning tasks to dependable virtual machines with a high trust level for a comparative examination of task failures, failure probability, and makespan. [PERSON] et al. [61] improved the security measures in the cloud system by integrating security services into the workflow allocation. Consequently, they suggested a multi-constraint workflow allocation technique for cloud computing's heterogeneous activities. The recommended course of action is to satisfy deadlines and financial restrictions while minimizing the likelihood of risk and the expense of execution. The results demonstrate that the method consistently outperforms the current multi-constraint methods in the suggested system. [PERSON] et al. [22] suggested a method that centers on monitoring networks and cloud services to find security breaches that occur when workflows are being executed. This method chooses the best adaptation step to minimize the impact on the workflow after detection. He uses adaptive learning to identify the best adaptation action to reduce the uncertain cost of such adaptations and their possible effects on other jobs in the workflow. The metrics used to assess this technique are the efficacy of the detection process and the effects of the chosen modifications on the processes. [PERSON] et al. [62] introduced a deep reinforcement learning-based multi-objective workflow scheduling system. Dependencies determined all workflow priorities before mapping processes to VMs. Next, data center electricity costs influenced VM priority.