# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 2 Related Work
Researchers created a three-tier resource provisioning strategy for a fog environment. The solution's efficiency was tested on two simulated and real-world workloads. Cost, resource usage, and delay violation measures were significantly improved over alternative techniques [18]. In another study [19], in a cloud environment, a framework autonomously manages the data center resources. Using the ANFIS workload prediction model, FA can reliably forecast future game demands. It adjusts a prediction model's parameter based on the latest measured workloads. Then, utilizing a fuzzy decision tree approach as a decision maker, improved the planner component's performance. The performance of the analyzer component and the overall performance of the autonomic resource provisioning approach are evaluated and compared. The experiment used both real and synthetic workloads to validate the evaluations. The ANFIS prediction model's findings and the experimental data demonstrate a strong correlation. ANFIS's developed model has a high coefficient of determination and a low coefficient for other criteria, indicating an excellent fit to the data and excellent prediction ability. The proposed approach reduces resource rental costs for the MMOG provider while meeting player QoS requirements, especially response time. The authors [20] offer an autonomous resource provisioning strategy. Using an RBF neural network to forecast user behavior generated a technique that is aware of past, present, and future fluctuations. While ensuring QoS, the suggested approach leveraged providence to minimize resource provisioning costs (50%) and prevent hasty judgments (70%) while increasing load resistance (42%). The authors also covered how to choose excess VM. As such, the cost-aware algorithm alone saved 1%. The suggested technique is also flexible and customizable. The ASP may utilize the flexibility feature to adjust the mechanism's performance for his gain or to boost end-user satisfaction. Its capability allows ASPs without a resource management specialization to apply their policy risk Lesley on resource capacity. Unlike other mechanisms, the mechanism tended to the ASP and eventual user's demands efficiently and equitably. The researchers presented a model [21] that predicts the cloud resource bundle using real workload and resource demand data; a maximum probability probabilistic model that predicts resource selection. The suggested OKNB model outperformed the GMP-SVM model by 3.15-8.04% on res, cpu, and mem model, respectively. The suggested model reduces waiting time by 16.83-58.5 s when applied to the cloud selection technique simulated on CloudSim. It also addressed the weighted probabilistic cloud service selection approach that uses resource bundle weights. As the cloud is elastic, it can manage changing workloads and their consumption. The researchers [22] presented resource provisioning for cloud applications using workload clustering. Their method combined BBO with K-means clustering to split cloud workloads based on QoS requirements. The Bayesian learning approach was used to identify resource provisioning activities that fulfill user QoS criteria. The suggested method decreases time, SLA violation ratio, cost, and energy use compared to current resource provisioning systems. Autonomous and smart systems are desired with high precision and accuracy in cloud capacity management resource allocation and provision domains. As a result, study [23] presented a resource strategy based on cloud service 'providers' smart agents and cloud user services. With a minimum commitment to VMs, the strategy stresses low-cost maximum resource usage and QoS guarantee. The classic Best Fit algorithm gives service providers and users the best cost and VM-placement ratio. The suggested approach also specifies the optimal VM resource allocation, flexibility, scalability, and dependability in data centers regarding performance and energy consumption. A neural network is employed in a study [24] to find the optimal and proper platform for hosting virtual machines.