## 1 Introduction  
Nowadays, user equipment terminals, such as smart mobile phones, smart sensors, smart bands, virtual reality (VR) glass, wearable devices, smart watches, and smart cameras are growing in popularity [1; 2; 3; 4]. The high-demanding applications and services, such as mobile augmented reality, gesture and face recognition, intelligent transportation, smart healthcare, interactive gaming, human heart-rate monitoring, voice recognition, natural language processing, and wearable virtual reality streaming are undergoing tremendous developments [5; 6; 7; 8; 9]. With the enormous enhancement of smart user devices and the emergence of recent innovative applications and high-demanding services, data traffic is rising exponentially [10; 11]. Therefore, storing, processing big data, and supporting real-time as well as computation-intensive tasks and applications is a big challenge. Additionally, owing to the confined processing capability and battery-life time, the execution of latency-sensitive and computation-intensive applications in user devices will undoubtedly affect the QoS and performance. To overcome these limitations, the applications are offloaded to the conventional remote cloud for execution. Although conventional remote cloud has unlimited storage and computing capacity, it has some difficulties in executing latency-sensitive and real-time applications because of the distance between the end users and the central cloud as well as unpredictable network latency [11]. To deal with these challenges, diverse approaches such as mobile cloud computing (MCC) [12], cloudlet [13], fog Computing [14], and multi-access edge computing (MEC) [15], can be used as complementary solutions to cloud computing by employing services adjacent to the edge network. The comparison among the MCC, fog computing, and the MEC based on the technical aspect is shown in Table 1.  
MEC has emerged as a promising approach to cope with growing computing demands in 5G networks, where the traditional base stations are upgraded to MEC-enabled small base stations (SBS-MEC) [18]. The SBS-MEC provides higher bandwidth, low latency, location awareness, dramatic reduction of energy consumption, and real-time radio network information [17]. One of the major limitations of the SBS-MEC server is that it does not allow continuous offloading service for all tasks which are offloaded from the end user because of its confined computation resources. However, if the number of mobile user's increases, the QoS will be depreciated for service interruptions, larger execution, and waiting time. Therefore, it is necessary to deal with the resources of SBS-MEC effectively for better system performance. Moreover, without providing proper information on the task-arrival time and sizes, MEC faces some challenges in a dynamic and rapidly changing environment to determine where the offloaded task will be processed. Due to the incomplete information, it is difficult to predict the demand of end users in advance. As a result, the quality of experience (QoE) will conspicuously degrade and negate the advantages of MEC. With this need in mind, several authors are trying to solve this problem using optimization techniques, but when the number of constraints is larger, multi-constraint optimization becomes a difficult task. In the MEC system, multiple constraints are engaged and these come from its infrastructure, end user device, and different application characteristics. Meanwhile, due to lack of proper advance information about task offloading requests, traditional offline optimization is not suitable for the collaborative task offloading problem. To solve the above-mentioned issues, we have introduced a novel collaborative task offloading scheme among multiple SBS-MEC servers to boost the computation offloading service. By using the proposed system, we can easily reduce the load of local SBS-MEC servers and the execution time as the tasks are distributed among neighboring SBS-MEC servers.