## 5 Discussion  
Edge computing is a rapidly emerging and dynamically diversifying field that allows for data processing to occur closer to the source of data, rather than solely relying on centralized data centers. This review offers a thorough analysis of edge computing task allocation techniques. This draws attention to the difficulties in choosing the best place for tasks depending on resources such as the processing power, storage, and network bandwidth as well as in adjusting to the network's dynamic nature. The research explores several task allocation strategies, including centralized, decentralized, hybrid, and machine learning algorithms, and assesses their efficiency in completing challenging tasks. After applying the inclusion and exclusion criteria, a total of 69 papers were included in our SLR. Based on the network types, deep learning compression techniques, and job allocation optimization algorithms utilized in edge computing, the research gives a comparative analysis of task allocation approaches. Overall, the paper highlights the difficulties and complexities of allocation in edge computing and underlines the emerging need to achieve a compromise between a number of competing goals, including energy economy, data privacy, security, latency, and QoS. The optimization techniques are the main source of challenge regarding task distribution. Dynamic scaling, resource-aware task distribution, resistive PSO, and meta-heuristic optimization approaches are some of the typical optimization strategies. The selection of an optimization approach must take into account the particular needs of the application and strike a balance between a number of competing goals, including energy efficiency, data privacy, security, latency, and QoS. This review assesses the efficacy of each method that is suggested for further study, emphasizing the difficulty of job distribution in edge computing and the need to compromise between conflicting objectives. These techniques are applied in various networks such as IoT networks, MEC networks, fog networks, vehicular networks, 5G networks, and distributed networks. Deep learning compression methods play a decisive role in choosing the appropriate technique for task allocation in edge computing. General deep learning compression, conventional neural networks, knowledge distillation, auto-encoders, and pruning are among the deep learning compression techniques that merit additional study. These techniques were applied in a number of suggested task optimization algorithms in many network types, including satellite, wireless, cellular, MEC, and the IoT [86; 87; 88; 89; 90]. The development of more effective and efficient task allocation mechanisms for edge computing may be influenced by further investigation into these compression techniques. The main contribution of our work is providing a solid answer to each of the research questions we made in Section 2. Regarding RQ1, the most contemporary task allocation techniques in edge computing are resource-aware task allocation, distributed, dynamic, machine learning-based, energy-efficient, quality-of-service-aware, collaborative, and Contex-aware allocation techniques which are also shown in Figure 3. As far as answering RQ2, the most used optimization techniques are the swarm, reinforcement learning, JTORA, multi-agent, game theory, heuristic approach, and last auction-based algorithms, which are also shown in Figure 4. When it comes to answering RQ3, the most used computer networks in edge computing are the MEC, the EC, vehicular, and lastly, the 5G. After answering the RQs, it is clear that every task allocation technique, optimization algorithm, and network has its own strengths and must be carefully chosen according to the application.