# Survey on Secure Scientific Workflow Scheduling in Cloud Environments
## 8 Survey of Secure Workflow Scheduling Approaches in Cloud Environments  
Early in the 1980s, workflow technology was developed. Business workflows, grid workflows, and cloud workflows have all emerged with the growth of distributed computing. Systems for cloud computing use virtualization to provide flexible resource management. Cloud workflows are more productive and adaptable than previous workflow solutions. Consequently, cloud workflow solutions have emerged as a popular topic for research in recent years. In scientific workflows, scheduling in the cloud started many years ago, almost between 2009 and 2010. This section discusses various existing approaches to scheduling workflows in cloud environments, particularly those considering security aspects. [PERSON] [50] proposed a security-conscious and cost-effective workflow scheduling technique (SA-BA) that ensures an affordable allocation of activities among available Cloud Service Providers (CSPs) to deliver consumers reduced makespan and enhanced security services. They performed comprehensive simulation tests utilizing six distinct workflows from both real-world and synthetic applications, significantly enhancing resource utilization by concurrently managing dynamic data transfer and execution. [PERSON] et al. [42] This study presents five policies for selecting sensitive data tasks. Five task selection policies were available depending on security needs. The first policy, entry-end, secures all entry and end data. Workflows with sensitive beginning and end data can be used. gather, and scatter rules protect tasks that receive or send sensitive data from various sources. Cleaned up activities with various parent-child roles. When all task data needs secure handling, all policies are applied. All five policies safeguard critical processes via authentication, integrity verification, and encryption. They suggested a multi-population genetic algorithm workflow scheduling system to save costs and meet deadlines. Four process application experiments show the idea reduces make-span and cost. [PERSON]'s [51] provides a cost-effective and secure scheduling method for various jobs in a scientific workflow in the Cloud. The suggested algorithm is based on the hybrid optimization technique, which incorporates the Firefly and Bat algorithms. The coding technique aims to achieve the time and risk rate limitations while minimizing the overall execution cost. A multi-objective function is used in the proposed system, and the results show that the approach consistently performs better than conventional techniques. [PERSON] and all [41] use coding to lower execution costs while meeting timing and risk rate constraints. The multi-objective function system routinely outperforms earlier methods. Five policies for selecting sensitive data tasks are suggested. Their workflow scheduling method employs a multi-population genetic algorithm to cut expenses and stay on time. Using four workflow applications in experiments shows that reducing time and money while keeping sensitive data safe is possible compared to a different method. [PERSON] and others' [52] paper includes workflow simulators incorporating the added labor of establishing security services for sensitive data. This study advises adding security services to the workflow simulator. These found seven workflow execution security overhead evaluation methodologies. They tested the extension using a process that used authentication, integrity verification, and encryption. By adding security services to private data and investigating how security affected time, cost, and security metrics, the add-ons emulated process execution. [PERSON] and all [35], they proposed (ACISO) system to increase the availability, confidentiality, and integrity of the intermediate data, thereby securing it. Hash functions, encryption algorithms, and erasure codes create availability, confidentiality, and integrity strategy pools.