# Scalable Resource Provisioning Framework for Fog Computing Using LLM-Guided Q-Learning Approach
## 2 Related Work  
In this section, some of the potential recent papers on scalable resource provisioning for fog computing are discussed and their limitations are identified. [PERSON] et al. discuss a Quality of Service (QoS) aware resource provisioning approach for fog environments. Two crucial challenges are involved in designing the resource provisioning framework for maximizing the throughput and minimizing the latency [11]. Low latency applications prefer the fog paradigm, as delay and congestion in service are overcome by bringing the cloud services to the edge of the network. An application placement strategy based on a greedy approach was designed to minimize the latency of IoT applications. Here, the incoming requests are distributed among the fog nodes using a greedy edge placement strategy. The approach is composed of two stages: the first stage is proper placement of requests across fog nodes to satisfy the high QoS needs of the real-time applications; and the second stage is depth-first search logic, which is used to identify the fog nodes that satisfy the bandwidth requirement of the incoming application requests. Each of the incoming requests is assigned priority during the placement process according to its processing requirements. The requests having higher processing requirements are sent to the nearest IoT devices for servicing. Here, resource provisioning latency requirement of the incoming requests is addressed. The evaluation was performed by considering the Distributed Camera Network (DCN) as a benchmark application. The DCN applications are mounted onto the fog nodes using a variable-sized tree network topology. From the results it was inferred that the end-to-end delay is minimized. However, the chances of getting stuck in an infinite loop is greater as the approach does not keep track of the visited fog nodes during placement of the application requests. [PERSON] et al. present an autonomic approach to efficiently allocate the resources for IoT requests [12]. The IoT requests experience heavy workload variation with respect to time, which needs to be addressed to prevent over/under provisioning of the resources. A Bayesian learning-inspired automated computing model was designed to make dynamic scaling decisions about whether to increase/decrease the resources for fog nodes. The Bayesian learning model is dependent on conditional probability for deciding the occurrence or non-occurrence of the event. A time-series prediction model was used to perform planning and analysis of the control loop. The autonomic system is composed of four major components: monitor, analyze, plan, and execute (MAPE). The MAPE module was further enriched with a knowledge database (MAPE-k). The MAPE-k computing model was introduced by IBM and executes MAPE modules automatically to calculate the number of fog nodes to be deployed for executing the IoT services. The time-series model is used to predict the future demand for resources. A three-tier fog computing architecture was considered for implementation purposes comprising a IoT device tier, fog tier, and cloud tier. The proposed MAPE-k module sits at the fog tier, which does automatic resource provisioning. The MAPE-k control module is executed in a loop through several iterations at regular intervals of time to allocate the fog nodes as per the dynamic changes in the IoT services workloads. However, the practical applicability of the approach is limited, as the Bayesian learning expects the computing environment to be static. In a static computing environment, sufficient information on the computing environment is known in advance; however, the fog environment is totally unpredictable, complex, heterogeneous, and highly dynamic in nature. [PERSON] et al. present an elastic form of resource provisioning framework for container-based applications in the fog computing paradigm [13]. Kubernetes is one of the most widely used orchestration platforms for container-based applications.