# Survey on Secure Scientific Workflow Scheduling in Cloud Environments
## 8 Survey of Secure Workflow Scheduling Approaches in Cloud Environments
Next, they presented the Security Strategy Optimal Allocation Model (SSOA), which maximizes intermediate data security while complying with workflow makespan and storage overhead limits. [PERSON] and [PERSON]'s [53] study aims to introduce a novel, robust algorithm for managing the scheduling of numerous workflows by utilizing various quality of service (QoS) criteria. It does this by combining the CPSO and GA metaheuristic algorithms. However, it should be mentioned that the most immediate performance measure used to manage the scheduling process is the combination of three separate QoS: execution cost, load balancing, and security. The execution cost of this algorithm was minimized while meeting deadline and risk rate requirements, according to the conclusion drawn from it. Verifying the suggested algorithm using alternative algorithms was considered. [PERSON] et al. [18] suggest switching defensive methods throughout workflow execution to reduce network scans and turn workflow security into an attack-defense game. Calculating the attack-defense game model's Nash Equilibrium yields the probability distribution of the best-mixed defense options. Based on likelihood, workflow execution uses differentiated virtual machines. A dynamic HEFT (heterogeneous earliest finish time)task-VM mapping mechanism is included to boost workflow efficiency and defense strategy switching. The studies are carried out in both a simulated and real-world setting. The findings show that, in comparison to alternative algorithms, the suggested algorithm can cut the benefits to the attacker by about 15.23% and lower the time expenditures of the algorithm by around 7.86%. [PERSON] et al. [54] argue that all workflow simulators ignore the overhead of installing security services for sensitive data. This study suggests adding security services to the workflow simulator. Workflow execution security overheads may be assessed in seven ways. The extension was tested utilizing authentication, integrity verification, and encryption. The software successfully emulated a process by including security services for sensitive data and investigating how security measures affected timeliness, cost, and security metrics. [PERSON] et al. [55] suggested using the fault and intrusion-tolerant process scheduling method (FITSW) to improve process dependability. The proposed workflow system utilizes task executors of many virtual computers to accomplish workflow operations. FITSW employs a deadline partitioning technique to determine sub-deadlines for each sub-task following the duplication of each sub-task three times and the implementation of an intermediate data decision-making process. Thus, job scheduling with resource flow attains dynamism. The proposed approach enhances efficiency, preserves an organized workflow, and produces or reuses task executors. WorkflowSim tests and evaluates task completion rate, success rate, and completion time, which were conducted to examine the efficacy of FITSW. The data indicate that FITSW improves success rates by around 12%, reduces completion time by about 15.6% compared to the intrusion-tolerant scientific workflow ITSW system, and boosts the task completion rate by 6.2%. The scheduling technique presented by [PERSON] and colleagues [11] takes user annotation of workflow tasks depending on their sensitivity. They optimize scheduling with a multi-population genetic algorithm to save money and fulfill deadlines. Three workflow applications with sensitive job-to-data size ratios were extensively tested for cost, makeuppan, risk, and wastage. The methodology secured critical jobs more effectively and cheaply than earlier techniques in the literature. [PERSON] and [PERSON]'s [56] paper suggests an effective way to manage workflow while taking information value into account by classifying high-value and low-value information and constructing an algorithm that acts as a scheduler using the parallel implementation in the natural process of genetic algorithms (GA) with a secured framework for high-value information.