## 4 Literature Review and Data Extraction
### Collaborative Task Allocation  
[PERSON] et al. [12] developed an edge computing system called cloudlet assisted cooperative task assignment (CACTA), which uses a method to allocate dynamically workloads to edge nodes across several time slots. The goal of this approach is to reduce the time required to complete tasks, either by itself or by working together to minimize both the overall task completion time and the cost of the system. The authors formulated this problem by considering the variable computation capabilities and costs of edge nodes, which are subject to uncertainty and stochastic behavior. They then derived an offlineoptimal solution used as a benchmark to evaluate the online algorithm's performance. The proposed method (PA-EMA) was created to solve the issue where the system does not have access to long-term historical data. The effectiveness of the multi-round allocation approach was validated by this algorithm. Also, the authors presented the PA-OPT algorithm, which can deliver performance that is close to optimal to deal with the second circumstance of acquiring historical data. The PA-OPT algorithm implements the ARIMA approach to predict the computing capacity and cost of a node by examining historical data and information obtained during the task execution. The CACTA system used actual parameters from a video processing application and an empirical dataset from the Google Cluster to conduct experiments. The presented findings recommend that the suggested online prediction algorithm was able to achieve outcomes that were almost as good as optimal, especially in cases where an edge node had high computational power and cost predictability. However, when the edge node's predictability is low, its performance is similar to that of other algorithms. In conclusion, this work presented an online algorithm based on Q learning to achieve joint optimization and real-time resource load balancing. The algorithm used a traditional RL approach and showed better performance compared to other existing algorithms. Furthermore, a paper presented by [PERSON] et al. [17] proposed a method to collect node state information in their paper. The method involves categorizing a node's state by combining intrinsic and real-time attribute values. An intermediate node is then used to send back the produced outcome information. After that, the authors of the study proposed a task allocation model to achieve dynamic system balance. The model involves allocating new tasks to the comparatively lightest nodes and the task arrival node, while temporarily not assigning tasks to the other nodes. The initial state of the nodes was classified using the naive Bayes method by the authors. Additionally, the normalization of the initial data prevented stressing the importance of higher-value indicators in the thorough analysis when the levels of the indicators differ significantly. In order to accomplish dynamic balance, the nodes with relatively light categorization states and the nodes of task arrival as the target nodes were chosen to allocate new tasks. Finally, to analyze the load balancing issue among edge nodes, a mathematical framework was constructed. The load balancing was achieved by assigning tasks and estimating the completion time based on the transmission rate between edge nodes, computation speed, and the current task calculation time. A cooperative edge computing task allocation system was presented by [PERSON] [27] that utilized the maximized resource efficiency and minimized service lag. The proposed model creates a cooperative computing model for two edge nodes. The model was converted into an integer nonlinear programming problem, and the best solution is found using the two-edge-node cooperative-task allocation based on the improved particle swarm optimization (TCA-IPSO) technique. The simulation results showed that the TCA-IPSO algorithm significantly outperformed the benchmark and QBTD methods, with a reduction in the average task completion latency by 53.8% and 36.0%, respectively.