## 2 Related Works  
MEC allows for devices to offload tasks to a nearby edge without transmitting to or from a cloud, which can decrease the utilization of resource, bandwidth in the backhaul, and computing power from user devices [26]. MEC optimizes resource allocation, minimizes latency, and enhances the overall efficiency of the network ecosystem. This approach not only streamlines data processing but also mitigates the potential bottlenecks associated with centralized cloud computing, resulting in a more responsive and agile network infrastructure. With the continuous advancement of cloud and edge computing models, computation offloading has been embraced as a method to boost the computing capacity of user devices [27]. Resource allocation involves the scheduling of resources to determine the most effective pairing of available resources while meeting all of the users' requirements. Resources are assigned based on factors such as the number of requests in the system, the type of request, its priority, and the acceptable delay for each request [28]. Resource allocation is a critical aspect of network management, encompassing the strategic scheduling and allocation of resources to ensure optimal utilization while satisfying the diverse requirements of users. This process involves intelligently pairing available resources, such as computing power, memory, storage, and network bandwidth, with the tasks or services requested by users. By carefully orchestrating these allocations, resource allocation mechanisms aim to maximize efficiency, minimize latency, and enhance the overall performance of the system. It must consider various factors, including the characteristics of tasks or services, the capabilities of available resources, Quality of Service (QoS) requirements, user preferences, and system constraints. Balancing these considerations is essential to achieve a harmonious allocation that meets the diverse needs of users while maintaining the stability and reliability of the network. In recent years, numerous studies have investigated the utilization of edge offloading for applications that require minimal delay. In [29], the proposed cloudlet framework, which includes the device, cloudlet, and cloud layers, created a three-layer approach to task offloading. High-computing tasks are offloaded to the cloudlet and cloud layers, while low-computingand high-communication cost tasks are completed on the device layer. By doing so, the framework avoids transmitting large amounts of data to the cloud, resulting in a reduction in processing delay. This reduction in data transmission not only minimizes processing delays but also alleviates network congestion, conserves bandwidth, and enhances overall system responsiveness. Additionally, by leveraging edge computing resources for local data processing and analysis, the framework facilitates faster decision-making and real-time responsiveness, particularly in latency-sensitive applications. In [30], the Energy-Efficient Dynamic Offloading Algorithm (EEDOA) was proposed to optimize energy consumption, which can be implemented in real time to make task offloading decisions with polynomial time complexity. Theoretical analysis has been conducted to demonstrate that the EEDOA can approximate the minimum transmission energy consumption while also bounding the queue length. In [31], the proposed approach aims to jointly minimize the system energy consumption and maximize the number of offloaded tasks. For solving this problem, the authors combined the Ant Colony Optimization (ACO) algorithm with load balancing and proposed a Load Balancing ACO (LBA) algorithm. Numerical results show that the proposed algorithm outperforms the traditional algorithms such as greedy algorithm and distance first algorithm. In [32], the authors introduced a new approach for deciding task offloading in MEC using deep reinforcement learning. The decision-making process for task offloading is represented as a Markov Decision Process (MDP).