# Survey on Secure Scientific Workflow Scheduling in Cloud Environments
## 9 Discussion

Table 2: _Cont._* References [60; 61; 62]: This recent work investigates multi-objective workflow scheduling using deep reinforcement learning, a promising approach that adapts dynamically to evolving cloud environments and security threats. * References [64; 65]: This research represents an integrated blockchain platform scientific dataset such as healthcare, montage, epigenomic, and others. This level of security provided access management, transparency, and integrity. The evaluated techniques exhibit considerable progress in safe cloud-based scientific workflow scheduling. Diverse methodologies, including multi-objective optimization, hybrid algorithms, game theory, and deep learning, have improved security, cost-effectiveness, and performance. These techniques emphasize resource allocation, data security, and efficient job execution in intricate and evolving cloud settings. A crucial element of these methodologies is their conformity to the CIA trinity (confidentiality, integrity, and availability), which is vital for safeguarding scientific processes. The triangle establishes the basis for assessing and formulating safe workflow scheduling methods, as outlined below:  
* Confidentiality: Safeguarding sensitive workflow data from unauthorized access is essential, especially in private or classified data processes. Heuristic methods, including encryption techniques, offer rapid answers but may be deficient in scalability. Metaheuristic techniques, such as particle swarm optimization (PSO) and hybrid strategies like SPHEFT, include robust encryption protocols and allocate jobs to high-security virtual machines to maintain confidentiality while preserving efficiency. * Integrity: Data integrity guarantees that workflow data remains unchanged during execution. Heuristic approaches frequently use hashing algorithms such as MD5 or SHA-256 to ensure data integrity. Metaheuristic methods, like genetic algorithms (GA), use sophisticated integrity-checking systems in optimization procedures. * Availability: Workflow availability guarantees continuous access to resources and the prompt execution of tasks. Heuristic methods depend on fundamental failover mechanisms, but metaheuristics employ dynamic resource allocation to ensure availability during system failures. Hybrid methodologies such as ACISO employ resource redundancy and load balancing to guarantee the uninterrupted operation of processes in the face of disturbances. This study examines the trade-offs between security and performance by linking the CIA principles with the analyzed algorithms. Heuristic methods offer efficiency and speed but may falter in complex scenarios. Metaheuristic approaches provide adaptation and flexibility; nevertheless, hybrid models balance security and efficiency. These techniques address the intricate challenges of secure process scheduling in cloud systems. As illustrated in Figure 9, these studies collectively aim to minimize execution costs, meet critical deadlines, and maximize security within the framework of scientific workflows in cloud computing. The figure visually represents the trends and connections between the different approaches discussed in this survey. From Section 8, each paper has limitations. These limitations are explained below in detail to provide a comprehensive understanding:  
1. Scalability limitations: Numerous paper approaches encounter challenges in scalability when implemented in extensive, intricate operations. As the work quantity rises, expenses escalate considerably, resulting in possible inefficiencies. 2. Resource Heterogeneity: Although many methodologies tackle resource allocation and security levels of datasets, they frequently neglect the heterogeneity inherent in cloud systems. This issue may lead to inefficient work distribution and increased operational expenses. Dynamic Threats: Several papers emphasize static security measures but lack adaptability to dynamic and evolving threats, such as zero-day vulnerabilities or other threats. 4.