## 4 Literature Review and Data Extraction
### Machine-Learning-Based Task Allocation  
A method for online scheduling optimization for requests based on DAGs in edge computing networks was proposed by [PERSON] et al. [28]. The process was characterized as MDP, where the best task allocation strategy was learned at each decision step via temporal-difference learning. Temporal restrictions between concurrent requests were satisfied while the system's long-term latency and energy use were reduced. In comparison to state-of-the-art approaches, the suggested mechanism exhibits promising results in lowering long-term delay and energy usage. The offloading of complicated structured tasks and pertinent strategies for task scheduling in edge networks are also reviewed and discussed in this paper. The suggested approach attempts to ensure the quality of experience (QoE), particularly when requests are latency-sensitive and include a lot of network traffic. A work published by [PERSON] et al. [54] addressed the issues with managing deep learning applications over an edge-cloud architecture where data are evaluated and transported between resources at the network's edge. Trade-offs between accuracy and latency are important for DL applications since they need outcomes close to real-time with a precision that the user has set as acceptable. When trade-off improvements are possible, the research recommended a data-driven scheduling technique and a data management strategy that lowers the resolution of incoming data. This architecture is for time-critical DL operations. The pipeline was deployed on distributed resources using the suggested system, which also has the ability to monitor each job separately to maintain the system performance. To effectively manage the full DL pipeline in practical deployments while resolving trade-offs between quality of service (QoS) indicators, the paper emphasizes the need for resource and data management solutions. It also highlights related developments in edge computing, microservices, and edge-enhanced data analytic systems. The architecture of the system has three levels: data management, infrastructure, and workflow management. The developer's requirements were met by the system's timeliness and accuracy thanks to the data management level's selection of the data quality distribution for data sources. The study defines the system's objective and analytical models to evaluate the accuracy and end-to-end latency of K data sources. The study looked at object detection in a multi-user setting on Grid'5000 and found that it increased the average system makespan by up to 54.4% compared to a cloud-only setup. To improve this, future research should develop a resource allocation tool for deep learning workflows that considers load, tasks, and resource requirements to meet performance constraints. Another work [38] studied the topic of load balancing as well as task allocation in computational infrastructures for smart cities that include edge and cloud computing. The authors provided an RL method for task distribution that takes into account the differences between nodes and tasks, as well as their interdependencies and time delays. The authors showed a simulator to evaluate their method with existing allocation methods and introduce an abstract description of the computing infrastructure for smart cities. The suggested method handles dynamic changes well and evenly distributes the workload across computing components. The contribution of this study was to find ways to optimize resource usage for smart city applications. A new tool to guide the task controller in making decisions (i.e., the allocation process) was provided by [PERSON] et al. [42]. Such decisions are dynamically influenced by the effectiveness of the edge computing nodes and the updated datasets. The authors assumed that TCs assign various jobs to various edge computing nodes simultaneously as they process the incoming tasks in batches.