# Survey on Secure Scientific Workflow Scheduling in Cloud Environments
## 2 Scientific Workflows in the Cloud  
Cloud computing provides pay-per-use computer resources over the Internet. The cloud model provides ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (such as networks, servers, storage, applications, and services) that can be quickly provisioned and released with minimal management effort or service provider interaction [2; 3]. Figure 1 shows three cloud services and several deployment methods. Software, platform, and infrastructure as a service are the types of services. Basic deployments are public, private, and hybrid. According to NIST, cloud computing has five fundamental qualities: on-demand self-service, broad network access, elastic resource pooling, quick elasticity, and measurable service [12]. The workflow domain orchestrates task sequences and automates processes, considering energy dissipation, virtual machine types, workflow types, enforced function counts, deadline limits, cloud billing charges, acquisition and termination delays, etc. Workflow processing includes problem identification, dataset collection, loading and summarization, data segregation, model assessment, feature scaling, algorithm selection, model training, validation, and prediction. Face recognition, scientific procedures, and object detection systems benefit from workflow scheduling [1; 13]. Figure 1: The diagram graphically illustrates the cloud computing model [14]. Data bifurcates workflow classifications into business and scientific categories. First, business workflows are practical workflows depicted as a directed acyclic graph. Second, scientific workflows encompass numerous tasks and necessitate an extensive array of tools for their execution [2; 10]. A scientific workflow comprises a series of interdependent computer processes. We categorize the interdependencies between jobs as either data or control-flow dependencies [4; 5]. Intricate workflow applications, such as gravitational wave physics, astronomy, and bioinformatics, require significant computational power and utilize cloud resources to analyze large data volumes. Cloud tasks manifest as workflows, predominantly depicted as directed acyclic graphs (DAGs), as seen in Figure 2[4; 5]. A workflow management system receives an abstract process as input and transforms it into an executable format. Scientific workflows are employed to assess cloud workflow scheduling techniques. These include [6; 15; 16] the following:  
1. **CyberShake**: Developed by the Southern California Earthquake Center, CyberShake is designed to assess seismic hazards in a region by using probabilistic seismic hazard analysis (PSHA). It simulates earthquake ground motions by integrating faults, geology, and seismic wave propagation information. 2. **Montage**: Created by NASA, Montage is a workflow application that creates large-scale sky mosaics by stitching together multiple astronomical images. Using input images from various telescopes and data sources, Montage generates high-resolution mosaics that astronomers use to study celestial objects across different wavelengths. 3. **LIGO inspiral**: The Laser Interferometer Gravitational-Wave Observatory (LIGO) uses this workflow to analyze gravitational wave data produced by events such as the merging of binary systems, including black holes and neutron stars. 4. **Sipht**: Developed by Harvard University, Sipht is a workflow used in bioinformatics research to search for small, non-coding RNAs across various bacterial genomes. These small RNAs play a critical role in gene regulation, and identifying them is essential for understanding cellular processes and developing medical applications. 5. **Epigenomics** is in the bioinformatics field, a CPU-intensive application that automates the execution of various genome sequencing operations [3].