# Replication-Based Dynamic Energy-Aware Resource Provisioning for Scientific Workflows
## 2 Related Work
This research suggested a \"hybrid bi-objective optimization based on simulated annealing and task duplication algorithm\" (BOSA-TDA) would improve conventional SA by utilizing two fundamental heuristics: duplication and \"heterogeneous earliest finish time\" (HEFT) techniques. Simulation was performed and the results reflect that the proposed BOSA-TDA outperforms the existing approaches. Security is a significant issue in the cloud; therefore, the authors in [30] included security limitations in an optimization model. Their main goal is to establish an optimum plan in the shortest time and with the lowest cost, while also satisfying the necessary security limitations. During the negotiation process, consumers can submit their security demands to the cloud provider. The workflow is first scheduled using list-based heuristics, and then particle swarm optimization is used to optimize. As a result, the authors devised a secured scheduling algorithm based on \"smart particle swarm optimization\" (SPSO) to determine the optimal schedule with the shortest time and lowest cost. The suggested method is capable of allocating tasks in SWs to the most appropriate virtual cloud machine. Thus, the suggested strategy addresses resource allocation. In addition, a variation of the PSO algorithm known as variable neighborhood PSO is being tested to solve the local optima problem. By providing on-demand and low-cost computer resources to customers, cloud computing is now a promising paradigm. As a result, scientific workflows (SWs), which are big-data applications, are becoming increasingly reliant on cloud computing resources. However, in such a huge distributed computing system, internal failure (or host fault) is unavoidable. It has also been established that cloud data centers would be subjected to hostile attacks on a regular basis. As a result, when conducting SWs in the cloud, external failure (failure due to a malicious assault) should be taken into account. In [31], a \"fault-tolerant scheduling\" (FTS) solution for SW in a cloud computing environment is presented to minimize workflow cost while achieving deadline constraints, even when internal and external defects are present. The FTS method, which is based on the jobs replication strategy, is one of the most widely used fault-tolerant algorithms. The outcomes of the experiments have proven to be beneficial in real-world SW applications. The authors in [32] describe a \"dynamic fault-tolerant workflow scheduling\" (DFTWS) approach that includes hybrid spatial and temporal re-execution strategies. Initially, DFTWS finds task time attributes and detects the workflow critical path. Then, the resource allocation phase is started, in which a suitable virtual machine (VM) is assigned to each job based on the task deadline and budget. Finally, during execution, DFTWS runs online scheduling, where real-time fault-tolerant decisions are taken depending on failure type and job criticality. Real-world workflows are used to test the suggested method. In addition, the elements that influence DFTWS performance are examined. DFTWS acquires a trade-off between high dependability and minimum cost objectives in cloud computing. In [9], the authors present a quality-of-service aware \"fault-tolerant workflow management system\" (QFWMS) for scientific processes in cloud computing. For the purpose of evaluating the proposed QFWMS, the authors used two real-time SWs: Montage and CyberShake. The proposed QFWMS scheduling was tested using the WorkflowSim [33] simulation environment. A proactive intelligent fault-tolerant model is required to complete a workflow without interruption. The research in [34] offers a cognitive fault-tolerant (CFT) model with three key phases for proactively tolerating task and VM failure. Combined ensemble prediction methods are used to anticipate task failures in the prediction phase, and label-tuning techniques are employed to generate intermediate labels and reinforce the prediction. The work is isolated during the segregation step based on its priority.