# Scalable Resource Provisioning Framework for Fog Computing Using LLM-Guided Q-Learning Approach
## 2 Related Work
present an autonomic resource provisioning approach for IoT applications using fuzzy Q-learning methodology [15]. The optimal states of the fog nodes are represented using the MAPE-k, loop which is composed of five major components; these are monitoring, analysis, planning, and execution, which are enriched with a knowledge database. All incoming user requests enter the admission control unit, which decides whether the requests need to be sent to the cloud based on their deadline requirement. Otherwise, the requests are processed at fog nodes for quicker responses. The controller is responsible for scheduling the requests to the nearest fog nodes that have sufficient resources for execution. A feedback loop is added for the autonomic component, which does real-time gathering of data typically representing the variation in the monitored system. The monitoring component takes charge of gathering the response time, power requirement, and container productivity. The main goal is to meet the service level agreement (SLA) and reduce the response time incurred. The analysis component does the job of data preprocessing and the planning component performs an action to adapt the monitoring system to attain the desired state. The set of control inputs is mapped onto control outputs using fuzzy inference mechanisms. It helps in handling the nonlinearity in the system by approximation of nonlinear problems using knowledge expression similar to human reasoning. The uncertainty involved in the workload is handled effectively using fuzzy rules. The self-learning-enabled fuzzy algorithm is responsible for automatically scaling the resource provisioning policies. The computation overhead is less due to limited static fuzzy rules and the resource classification is enhanced by incorporating the signals from the environment. However, due to formulation of static rules, the fuzzy system becomes poorly tuned and leads to inefficient resource allocation. Even though fuzzy inference systems are capable of handling vagueness, they require extensive reconfiguration of rules to adapt to the rapidly changing state of fog nodes. [PERSON] et al. present a learning-based approach for resource provisioning in the fog paradigm [16]. The varying workload demands of the IoT applications are managed very well by formulating appropriate scaling decisions about whether to provision the resources. Nonlinear autoregressive neural networks embedded with a Hidden Markov Model (HMM) are used to predict the resource requirement of the applications. An autoregressive model is used to determine the probability-based correlation between the workload demands of the incoming IoT applications. Then, that knowledge is used to determine the next workload for a new/unknown IoT application. It operates with the assumption that the current value is a function of a past value in a time-series model. The proposed framework is composed of a control module, monitor module, prediction module, and decision module. The control module receives the incoming IoT application and if the deadline is less than the threshold set, corresponding information is stored in the shared memory. Otherwise, it is transferred to the cloud for processing. The prediction module is responsible for predicting the future workload demands of the IoT applications. The quantity of fog resources required for deployment is determined by the prediction module. Finally, the decision module takes up the decision of whether to scale up or scale down the resources, which is transferred to actuator as output. The performance is evaluated over the real-time dataset and is found to achieve a significant reduction in the delay and cost. However, the controller module operates by setting a threshold value, which is not suitable for highly varying dynamic situations. The threshold method does not penalize for false positive classifications; as a result, arriving at a stable threshold value becomes highly difficult. [PERSON] et al.