## 4 Literature Review and Data Extraction
### Dynamic Task Allocation
Overall, the study examined the service latency restriction and the smooth service migration requirement, making it appropriate for edge-cloud systems with several IoT users. Moreover, [PERSON] et al. [44] addressed the problems of how edge computing can become a viable tool for reducing the communication lag. In order to balance the workload across edge servers and reduce the transmission distance, the authors suggested a load-balancing model that takes the computational load on edge servers as wellas transmission distance into account. Edge server placement (ESP) in conjunction with the proposed algorithm performed better than conventional heuristics. Furthermore, the proposed method was more successful in integrating work allocation and server placement, according to their simulation findings. [PERSON] et al. [46] analyzed task distribution in latency-constrained fog computing networks, where fog nodes (FNs) are closer to the network edge and have lesser computational capabilities than the cloud resources. An optimization issue to reduce task-related energy for transmission and processing with delay limitations was presented in this study. The optimization takes into account both the workload distribution across the nodes and the CPU frequency at each active node. Two useful algorithms termed energy-efficient resource allocation (EEFFRA) and low-complexity energy-efficient resource allocation (LC-EFFRA) were developed after the issue was converted with successive convex approximation and decomposed using the primal and dual decomposition techniques. The utilization of EEFFRA/LC-EEFFRA greatly decreased the count of computational requests that did not meet the expected delay requirements. Additionally, the authors made use of dynamic voltage and frequency scaling (DVFS) to reduce energy usage while still meeting delay requirements. Modeling the energy consumption and delays associated with transmission and processing in the fog and cloud layers, as well as putting forth and resolving a challenging optimization problem, were the paper's key contributions. A recent paper by [PERSON] et al. [49] proposed an entirely novel edge-native task scheduling system (ENTS) that co-schedules networking and computing resources to improve the performance of edge-native applications. Although Kubernetes is now the de facto standard for container orchestration, it does not enable edge-native apps or take into consideration their unique throughput and latency needs. This paper includes the case study of a video analytics application to show how task distribution and data flow scheduling enable ENTS to maximize work throughput. To tackle the mixed-integer nonlinear problem, this paper offers two online methods. The research utilized metrics like task throughput and the average waiting time to compare the performance of ENTS to different baseline techniques on a real-world testbed. The findings demonstrated the necessity for specialized scheduling systems for strengthening edge-native applications by demonstrating considerable gains in work throughput and latency. This study also offered suggestions for potential future advancements, including the creation of more sophisticated algorithms for group job scheduling and the incorporation of software-defined networking into the network controller. Furthermore, [PERSON] et al. [56] suggested a network architecture of cloud edge fusion for the MEC system to enable thorough query and computing for real-time applications. By utilizing MEC's short distance from the user equipment (UE), difficult computational tasks can be executed by UE and useless data can be removed before flowing to the cloud. A suggested distributed task scheduling technique was based on the network architecture to deal with complicated computational tasks; several MEC servers cooperate, work in parallel, and eventually accelerate the task execution response times. To demonstrate that this approach performs better, the authors ran many simulation trials.