# Replication-Based Dynamic Energy-Aware Resource Provisioning for Scientific Workflows
## 2 Related Work  
The related work is explored and analyzed regarding resource management in cloud computing, SW scheduling, and fault-tolerant techniques for SWs in cloud computing. As part of their cyber-infrastructure, many workflow applications and workflow management systems (WMSs) are developed to allow scientists to run their applications in a variety of distributed environments. Despite the fact that scientists have used both practical and theoretical techniques, detection, failure prediction, and recovery remain research issues. In [26], a strategy is presented for detecting and mitigating issues before they arise, based on control theory and developed as part of autonomic computing. The suggested technique is based on the \"proportional-integral-derivative controller\" (PID controller). It is widely employed in industrial control systems, and it entails the controller altering its output in response to difficulties. To show the practicality of the proposed technique, the authors tackled two common execution challenges of large-scale data-intensive processes. The authors constructed a simulator that implements and analyses basic standalone PID-inspired controllers in a data-oriented bioinformatics process to manage data and memory use autonomously. To execute all processes simultaneously, the simulator generated over 4.4 TB of data and required over 24 TB of RAM. According to simulation data, the controller-inspired method looks to greatly improve workflow performance, particularly in online and unpredictable contexts. A multi-objective scheduling technique for cloud computing scientific processes is presented in [27]. The method is based on a genetic algorithm that tries to strike a balance between make-span, financial cost, and load. The recommended method firstly finds the best answer for each parameter. Based on these answers, the algorithm chooses the best solution for all parameters. The proposed method was evaluated on benchmark datasets. The results demonstrate that the recommended strategy enhances the make-span and decreases the cost when used with a well-balanced system. Scientific operations are complicated, and they necessitate the efficient use of cloud resources. SW scheduling is considered an NP-complete problem. Some criteria, such as quality of service (QoS), interdependence between jobs, and user deadlines, comprise challenges. There is a substantial body of knowledge about scheduling SWs in cloud systems. Standard schedulers, evolutionary optimization approaches and other options are available. A hybrid approach for scheduling scientific activities in cloud systems is presented in [28]. The algorithm creates task lists for the particle swarm optimization (PSO) algorithm in the first phase. To reduce execution time, bottleneck tasks are given top priority. The PSO algorithm is used to schedule tasks in the next phase, which reduces both execution time and cost. In [17], an approach for scheduling SWs called the dynamic scheduling of bag-of-tasks-based workflows (DSB) is presented; itaims to reduce the financial cost of hired virtual machines (VMs) while meeting user-defined time restrictions. The suggested technique divides the workflow into bag-of-tasks (BoT) applications based on data dependencies and priority restrictions, and then enhances the assignment and scheduling of BoTs. Using benchmark scientific procedures that resemble real-world applications, a trace-based simulation indicated a considerable decrease in workflow computing costs while still meeting deadlines. When compared to modified state-of-the-art methodologies, the findings show that the proposed model produces higher success rates in terms of meeting deadlines and cost efficiencies. As discussed in [29], the scheduling of SWs on a hybrid cloud architecture is modelled as a bi-objective optimization problem with a make-span and monetary cost minimization goal.