# Indetermsoft-Set-Based D* Extra Lie Framework for Resource Provisioning in Cloud Computing
## 2 Related Work  
This section provides a comprehensive overview of the existing literature and developments pertinent to resource provisioning in cloud computing. Through this review of previous research, methodologies, and technological advancements, we establish the foundation/scope for the proposed work. [PERSON] et al. [11] present an artificial-intelligence-based holistic approach named HUNTER for resource management in cloud computing. They view optimizing energy emission in cloud data centers as a multiple-objective-based scheduling problem. A large amount of energy is consumed by cloud data centers, and hence, there is a need to optimize energy emissions. One of the key factors to consider while optimizing energy consumption is reducing the number of thermal hotspots which lead to the degradation of system performance. Three important models are considered for resource management: cooling, thermal, and energy. A gated graph convolutional network is viewed as a surrogate model for optimizing Quality of Service (QoS) to perform optimal task scheduling. The continuous training of the model helps in quick adaption to dynamic scenarios by providing the permission to change the scheduling decisions through task migration. Power performance is used as a heuristic to efficiently balance the load among the cloud hosts. The performance of the HUNTER is evaluated through the CloudSim simulation toolkit and is good with respect to energy conservation. However, the approach exhibits poor scalability as it cannot scale up to large-scale graphs with higher node degrees. [PERSON] et al. propose an auto-scaling framework for resource provisioning in a cloud computing environment [12]. The over- and under-provisioning of resources results in a loss of revenue for the cloud brokers whose primary function is to select, manage, and provide the resources in a complex heterogeneous environment. Since the client resource requirements are uncertain, it becomes difficult for the cloud broker to predict and process the client resource demands. Here, the resource-provisioning problem is divided into two stages: resource selection and resource management. The resource selection problem deals with the process of selecting the services that meet the requirements of multiple cloud service providers. The resource management problem deals with the effective maintenance of cloud resources in terms of resource utilization and overhead maintenance. Both selection and management of resources have been considered as decision-making problems that focus on matching the resource requests with services provided. The cloud users make use of virtualized resources in order to benefit from long-term pricing strategies. For providing cost-effective solutions, a precise estimation of the upcoming workload is required. An adaptive auto-scaling framework for resource provisioning that uses historical time-series data for training a K-means-enabled CNN framework to categorize the future workload demands as low, medium, or high as per their CPU utilization rate is proposed here. From the performance evaluation, it is observed that the solution deployment cost is minimal. However, the framework exhibits higher sensitivity towards initial parameter setting and an inability to handle categorical data in large-state-space environments. [PERSON] et al. present an efficient algorithm for resource provisioning for the efficient execution of workflows in cloud computing platforms [13]. The workflow considered here is composed of tasks exhibiting varying resource requirements in terms of memory storage, memory type, and computation speed. Improper mapping of the workflows to resources leads to wastage of resources and increased makespan time. Here, the workflow is divided into three categories: compute-intensive, memory-intensive, and storage-intensive. The proposed algorithm operates in two phases to provision resources precisely by distinguishing the tasks as computation-intensive and non-computation-intensive.