## 4 Literature Review and Data Extraction
### Resource-Aware Task Allocation
The researchers approached the subchannel allocation problem in the context of a two-sided matching process between mobile terminals (MTs) and subchannels. They proposed a sub-optimal algorithm with low complexity to allocate subchannels, and then treated the transmission power allocation as a convex optimization problem, using the Lagrange multiplier approach. A task allocation method based on resource allocation was used to overcome the task allocation issue. Computer simulation results demonstrated that the suggested task offloading and resource allocation technique enhanced the MEC system's overall performance. Recently, [PERSON] et al. [37] proposed a set of four modules that work together to support developers in adjusting the deployment process based on the specific requirements of an application and the capabilities of the infrastructure. These modules are designed to function independently while also complementing one another. The work first defined the FMs feature models (FMs) of the infrastructure and application. An FM depicted, based on characteristics, the relationship between the common and variable parts of a product family (either an application family or a system family). FMs were represented as a collection of features that were arranged hierarchically, with parent-child connections between the features and a set of constraints (known as cross-tree constraints) that show the links between the features. The infrastructure consisted of a range of different devices, each with various hardware and software attributes, such as the device type, computational capacity, peripherals, network capabilities, operating system, third-party libraries, and so on. These attributes were related to the types of services or tasks that could be performed on the execution platform, and they pertained to the hardware and software requirements of the applications. The use of feature modeling (FM) helped create the more accurate models of the infrastructure, which is often overlooked in the software product line (SPL) models. This approach allowed the mapping of application features from the application of FM to the software and hardware features of the infrastructure, even when different nodes had different capabilities. To simplify the configuration of the infrastructure's feature model (FM) and manage the evolution of hardware and software characteristics separately, the authors divided the hardware and software features into two separate FMs. A load-balancing method for MEC servers was published by [PERSON] et al. [39] which operates in ultra-dense networks based on load estimated. The suggested method accounts for the dynamic distribution of user equipment and ad hoc application activities, and it provides a novel idea of task unit load transfer overhead for load balancing that is both low-complexity and high-efficiency. In addition, the study investigated load estimations based on overlapping coverage and user load prediction, and it suggested a three-tiered computing network design made up of devices, edge nodes, and cloud servers that improves load balancing while taking energy use and latency into account. Simulated experiments were performed to verify the efficacy of the remedies that were suggested. [PERSON] et al. [40] integrated the edge computing with MCS and put up a three-tier architecture. The authors worked on creative task allocation and user data submission ideas. The edge servers transmit the job requirements to users during task allocation so that exact personal information is no longer required. Edge servers assist participants in submitting high-quality altered data that protect their privacy. They define the challenge of privacy-preserving sensing cost reduction and provide a solution using DS-UPP. In DS-UPP, a compressive sensing-based method to reduce the quantity of required sensing data and an LDP-based algorithm to safeguard the privacy of the participants were created. Also, the authors examined the DS-UPP's theoretical properties.