## 4 Literature Review and Data Extraction
### Machine-Learning-Based Task Allocation
A clustering-based allocation method and a bio-inspired model (i.e., a modified version of the well-known PSO technique) were both proposed as alternative allocation techniques. The goal of both models was to identify the best allocations at a given moment in time. They relied on procedures that did not require any training. In order to show the cost for each allocation based on the demands of the jobs and the condition of the edge computing nodes, they also considered the cost of allocation as stated in their past work [85]. The cost of allocation is also used as an indication for calculating the ranks of edge computing nodes, resulting in a \"rewarding\" mechanism for resolving the issue at hand. A method for handling large-scale tasks in edge computing was presented by [51] using the federated learning-based optimization methodology (FLOM). To accomplish accurate task categorization, global load balancing, and lower task processing costs, FLOM combines FL and deep feature learning approaches. The suggested method incorporates an FL architecture that permits data exchange and model parameter updates across several edge computing and cloud centers without disclosing sensitive information to outsiders. To learn the deep features of task requests and hosts in the substrate network, the authors provide a deep network model. The experimental findings show that the FLOM technique outperforms other approaches and efficiently handles large-scale task categorization and allocation. In edge computing environments, the research emphasizes the necessity for effective task allocation methods that can identify independently jobs while achieving system load balancing. The suggested method has several potential applications in industries including intelligent manufacturing, intelligent IoT, and smart cities. Previous research [53] tried to apply edge computing in smart cities in order to implement a task distribution among the various nodes. The authors researched a variety of allocation techniques, from stochastic allocation to intuitive methods to integer programming-based mathematical optimization. Furthermore, a demonstration of how efficient job distribution in HEuCs offered appreciable gains in total performance. The authors also showed that it is completely possible to execute integer programming at the periphery with little overhead (less than 2% of the total makespan time in their tests in the worst case scenario) for the size of the scheduling they performed. Their research contribution was that they defined and provided a motivation for the idea of heterogeneous edge micro-clusters (HEuCs) and also that they showed the examples of common HEuC compute platform tasks. This research demonstrated the effectiveness of using mathematical optimization as a method for batch job distribution in HEuCs. [PERSON] et al. [57] proposed a task allocation method where all agents involved in executing tasks can express their preference for which tasks they want to work on, and the manager agent then allocates tasks based on these preferences and system performance requirements, without employing any extra knowledge of the agents' capabilities or state. The authors also took into account the manager's particularity, which means that each manager has a different set of customer requests based on the place and the time. Therefore, by balancing their workloads, agents must be able to dynamically select which manager to support with the specified tasks. The author suggests that, when making decisions about service choice, justice and societal considerations should be given priority over the concept of centralized control. This is because centralized control is not suitable for existing applications, where services are provided by multiple independent organizations. Lastly, [PERSON] and [PERSON] [58] proposed an approximate technique for task allocation that uses the least amount of overall energy while taking into account multi-task parameters in MEC.