## 4 Literature Review and Data Extraction
### Resource-Aware Task Allocation  
The authors in [14] employed their proposed model to simulate a practical vehicular environment and investigate the problem in a large-scale network. A small part of the source task was processed at the vehicle, and the remaining task was offloaded at other vehicles and then at a vehicular edge computing server level. This lowers the cost of the entire system while also allowing for the utilization of ample vehicle resources and a reduction in the strain on the overworked VEC server. The MAP algorithm was used in the VEC scenario which enabled each vehicle to choose its adjacent cars depending on the finest resources available at the lowest cost. Additionally, they estimated the transmission rates for V2I and V2V communication while taking into account realistic assumptions. The determination of whether a job should be computed locally, on a nearby vehicle, or at the vehicle edge computer (VEC) was based on the percentage of the job and was conditional on two factors: the maximum allowable delay and the vehicle's stay time. Finally, by contrasting it with other methods, they could assess how various variables and vehicular contexts affect their MAP task offloading approach. In another paper published by [PERSON] et al. [15], the authors suggest a cooperative learning process that makes use of simulated and real-time captured data to decrease the quantity of data required to produce a trustworthy data-driven model. The authors employed the DCTA technique to distribute tasks in a data-driven system. Through both a trace-driven simulation and a brand-new, in-depth real-world AIOps case study that connects theory and practice with a novel architecture, a key component was designed within an AIOps system. The authors also assessed numerous distinct work allocation methodologies. At the end, the DCTA method significantly reduces the processing time by 3.24 times and saves 48.4% of the energy consumption compared to state-of-the-art approaches when addressing task importance for multi-task learning (MTL) using task allocation in time-constrained and integrated management (TATIM). An optimization problem that aims to minimize the total number of users and transmission delays using a multistack RL method was presented by [PERSON] et al. [16]. The results of this study showed that each base station (BS) opted to allocate a greater number of downlink subcarriers and transmit power in the downlink direction to a user whose task needed to be processed by the MEC server, to reduce the maximum delay among all users. The preference of each BS was to give more uplink subcarriers and a greater uplink transmit power to a user who needs to locally complete a job. The authors presented a novel strategy for load balancing in edge computing by allocating tasks through intermediary nodes. [PERSON] and [PERSON] [21] analyzed a network situation with several mobile edge server nodes (MSNs) and a number of edge devices (EDs), each of which had an MEC server to offer wireless and computation resources. Their study concentrated on ED-specific tasks, which could be broken into any number of smaller portions for both local and remote computing needs. Data communication during task loading was achieved using NOMA technology to increase the resource usage and MEC performance. The authors took into account the limitations of wireless and communication resources, and they formulated a combined optimization problem of task loading and resource allocation to increase the system's capacity for job processing. To address the defined MINLP problem with the characteristics of the objective function, the authors divided the original problem into two subproblems: the resource allocation (RA) problem and the task allocation (TA) problem. Then, they further divided the RA problem by allocating them as either computation and communication resources. Initially, the authors assumed that the power distribution of subchannels was equal when allocating communication resources.