## 2 Related Work  
MEC is a leading technology in satisfying the end user's demands for service requirements [33; 34]. In MEC, the main research points are the task offloading strategy and resource allocation. In recent years, some researchers have focused on these points and considered different scenarios such as a single user or multiple users, single or multiple MEC servers in a single base station, partial or full offloading for task computation, and computation offloading for dynamic as well as static environments. [PERSON] and [PERSON] [35] have optimized the computing mode for a multi-user scenario of a single cell BS. On the other hand, [PERSON] et al. [36] have used adaptive sequential offloading game approach to avert the unpredictable queuing delay for multi-user and multi-cell MEC scenario. To enhance efficiency, some researchers have concentrated on reducing the processing time and consumption of energy by using full offloading approach [37; 38]. [PERSON] et al. [37] have proposed a jointly optimized offloading and scheduling strategy for enhancing the QoE and efficiently reducing the energy consumption. Moreover, in wireless mesh networks [38], by using collaborative edge computing, the saturated backhaul bandwidth will be reduced. However, after analyzing the full offloading and partial offloading technique, [PERSON] et al. [39] concluded that partial offloading is much better and it is more practical as it uses a parallelism approach. Meanwhile, [PERSON] et al. [40] proposed Lyapunov optimization which is an online based algorithm and it is used for reducing energy consumption. Moreover, in case of resource utilization, dynamic resource allocation approach [41; 42] is more efficient than static approach [43]. In [43], the game-theoretic approach has been proposed to increase computation offloading performance in a distributed manner, but the insufficiency of computation resources was not considered. [PERSON] and [PERSON] [41] proposed an iteration-based algorithm based on software-defined network (SDN) concept for resolving the issues of task offloading and dynamic resource allocation problem. Meanwhile, [PERSON] and [PERSON] [42] reduced the energy consumption as well as processing delay by using the cooperation of edge cloud approach. Nevertheless, most of the previous works focus on a single MEC server and [PERSON] and [PERSON] [44] considered the multi-server MEC scheme, but they did not consider the dense deployment of SBS-network environment. MEC and dense deployment of SBSs are the key technologies for 5G mobile communication networks and they provide enormous access capacities for the user because the nearby SBS-MEC servers can create a computation resource pool collaboratively [10; 45]. Research has been conducted to integrate the MEC with SCN, which has attracted the researcher's attention. For balancing the computation workload, [PERSON]. [46; 47] utilized the cooperation between servers to balance the load. [PERSON] et al. [48] used device-to-device (D2D) collaboration for load balancing in densely deployed small-cell network environments. [PERSON] et al. [49] have proposed a novel mobility management approach for MEC in ultra-dense network; however, for reducing latency and energy consumption, [PERSON] et al. [50] introduced a two-tier game-theoretic greedy approach in ultra-dense IoT Networks. MEC-enabled DDSCNs are facing some challenges. In a densely deployed environment, there are many SBS-MEC servers that have limited computing resources. Meanwhile, existing research mostly focuses on end-user-to-local SBS-MEC offloading while the computing capability of local SBS-MEC server is ignored. Therefore, efficiently handling the resources of the distributed SBS-MEC server is a challenging issue. Furthermore, the end user does not have the information about the network traffic, capacity of the SBS-MEC server, etc., when they offload their task to the network. To overcome the above-mentioned challenges, we developed an FCTO scheme for MEC-enabled DDSCN environment.