# Scalable Resource Provisioning Framework for Fog Computing Using LLM-Guided Q-Learning Approach
## 2 Related Work
The framework is designed to execute over the Kubernetes computing platform to deliver resources in real-time. The ongoing traffic information is gathered in real-time and resources are allocatedin proportion to the traffic distribution. Here, the Kubernetes platform is used for the purpose of orchestration, and the IoT applications are kept in the form of a container and deployed inside the fog nodes. Fog nodes situated in the nearby vicinity operate together in the form of a cluster. Two important limitations of traditional Kubernetes are that it does not consider network traffic status for application scheduling and it does not monitor the changing popularity of the applications over time. Here, the real-time traffic information is gathered at frequent time intervals. Affinity-based priority rules are formulated that specify the intended location for the fog nodes. The relocation decisions about the fog nodes are made as per the formulated affinity-based priority rules. To determine the best-fit node for the application, several factors are considered, including the health of the node, node affinity, location of the node, and application spreading statistics. The filtering and scoring steps of Kubernetes are completed first. Then the scheduler determines the bandwidth requirement and distributes them among the fog nodes in the nearest vicinity. The performance is tested by considering the small-scale and medium-scale applications. Compared to the traditional Kubernetes performance, the proposed framework achieves an improvement in terms of enhanced throughput and reduced latency. However, the performance considering large-scale applications with dynamic changes in the network traffic is not achieved. This limits its practical application for providing the resources considering computation-intensive applications. [PERSON] et al. present a centralized controller approach combined with a distributed computing model for resource provisioning in a fog environment [14]. The fog computing architecture is made up of heterogeneous fog nodes, which represent high mobility, complex distribution, and sporadic availability of the resources. The computing model of the fog environment is modeled as a Markov Decision Process (MDP) to satisfy the end users with minimum latency. At first, a centralized fog controller was designed, which acquires global knowledge about the fog environment. Then, collaborative reinforcement learning agents are deployed, which manage the fog nodes by satisfying the user requirements. Proper resource coordination is carried out to provide continuous Quality of Service (QoS) satisfaction to end users. Flawless resource provisioning is guaranteed by accurately tracking the resources. The number of satisfied users is enhanced within the predefined latency requirement through distributed computation. The centralized fog controller along with the set of Q-learning agents and market actors formulate a multiple agent-based system. The integral number of fog instances is enumerated, which ensures a lower cost from fog users. The Q-learning agents that are deployed are capable of sharing the resource information among each other to arrive at common resource provisioning policies. No agent has complete knowledge of the fog computing model, and hence the decentralized learning approach is depended on to make better decisions. A real-world mobility-based dataset is considered for experiment purposes, and the higher efficiency is achieved using a collaborative learning approach. Dynamic allocation of resources is performed based on the real-time workload demands. It guarantees better resource utilization without over provisioning or under provisioning of the resources. However, if synchronization does not happen between the Q-learning agents, significant communication overhead occurs. The size of the Q-table increases exponentially and prevents the scalability of the system due to state space explosion. [PERSON] et al.