# Indetermsoft-Set-Based D* Extra Lie Framework for Resource Provisioning in Cloud Computing
## 2 Related Work
The workflow model is composed of limited information about the task contained in it, which makes it applicable to real-time scenarios. The Amazon EC2 cloud model is considered for offering on-demand computational resources for applications using the presented algorithm. However, the approach is found to be static and applies a standard set of operations to process computation-intensive and non-computation-intensive tasks. This limits the practical applicability of the approach. [PERSON] et al. discuss a game approach based on mean-field theory for resource management in cloud computing [14]. Resource management is viewed as one of the prominent problems in serverless cloud computing environments. Multiple users compete for resources which usually suffer from scalability issues. Here, an actor-critic learning algorithm is developed to effectively deal with large-state-space environments. The algorithm is implemented by considering linear and neural network approximations. The mean-field approach is compatible with several forms of function approximations. Theoretically, convergence to Nash equilibrium is achieved under linear and softmax approximations. The performance of this approach is better in terms of resource utilization, but the time to converge towards better resource-provisioning policies is excessive. [PERSON] et al. [15] present an adaptive reinforcement-learning-based approach for resource provisioning in serverless computing. Cloud service providers expect a resource-provisioning scheme to be flexible to meet the fluctuating demands of the customers. A request-based policy is proposed here in which the resources are scaled for the maximum number of requests processed in parallel. The performance is strongly influenced by the predetermined concurrency level. The performance evaluation indicates that with a limited number of iterations, the model formulates efficient scaling policies. But identifying the concurrency level that provides the maximum QoS is difficult because of the varying workload, complex infrastructure, and high latency. [PERSON] et al. discuss a novel resource management framework based on deep learning [16]. Increased multimedia traffic in the cloud leads to minimum extensibility of a service portfolio and poor resource management. A gray-wolf-optimization-based resource allocation strategy that mimics the hunting behavior of grey wolves is proposed here. The deep neural network utilized here provides routing direction based on the data input rate and storage availability. The neural network operates in two phases: data pre-processing and routing, and controlling application. While the delay in processing the requests is reduced by this policy, it suffers from a poor search ability and a slow convergence rate. [PERSON] et al. develop an elastic resource management framework to provide cloud services with high availability [17]. There is a very high demand for resources on the cloud, and the failure to provide on-demand services leads to load imbalance, performance degradation, and excessive power consumption. An online failure predictor that predicts the possibility of the virtual machines resulting in resource starvation due to a resource contention situation is developed. The server under operation is continuously monitored with the aid of a power analyzer, resource monitor, and thermal analyzer. The virtual machines that exhibit a high probability of failure are assigned to the fault tolerance unit that can handle all outages and performance degradation. The virtual machine failure prediction accuracy in this technique is poor, leading to poor resource management policies. In summary, the existing works exhibit the following drawbacks:  
* Inability to determine the parameter uncertainty in the user tasks and virtual machines. * Conventional resource-provisioning approaches are static in nature, limiting their practical application. * Rule-based approaches are time-consuming and hard to scale, and the rate of virtual machine violations in terms of cost and response time is very high.