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The goal of sixth-generation (6G) wireless communication network is towards space-atmosphere-land-submarine connectivity. 6G communications are mainly characterized by massive connectivity, ultra-reliability, low latency, and extreme high bandwidth to meet the requirements of practical applications such as robotics and autonomous vehicles, transportation management systems, healthcare as well as telepresence, virtual reality (VR), augmented reality (AR), and mixed reality (MR) applications. Multi-Access edge computing (MEC) is a key component of future 6G wireless networks, enabling latency-sensitive, bandwidth-intensive, and compute-intensive devices. This article presents an overview of the technologies assisting, challenges, and opportunities for envisioning MEC in future 6G wireless networks.

Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network’s condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme.

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications, therefore, it increases the user&rsquo, s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.

Mobile Edge Computing (MEC) enhances mobile cloud computing capabilities by fetching services close to the edge of the network, which adds 4C (Computing, Communication, Control and Caching) to the edges. MEC-enabled small cell network is regarded as the key technology in future 5G networks where for rapid task execution, a user offloads their tasks to the nearest small BS (SBS). The research work regarding MEC-enabled small cell network is still in its infancy. Recently, some researchers are trying to integrate MEC with small cell networks (SCNs) while ignoring the unlimited computation resource in a remote cloud and the computational capability of a single SBS-MEC server, which has the limited capacity for handling huge number of user request. To effectively handle latency-sensitive tasks and resources-hungry mobile applications in small-cell networks, two collaborative task offloading schemes of our proposed model is introduced in this paper. Our proposed collaborative model can make decision dynamically where the SBS-MEC server collaborate with mobile devices or remote cloud for executing the computation tasks. The simulation results confirm that collaborative task offloading between mobile with SBS-MEC scheme will reduce the average number of task failure more efficiently than other schemes and the collaborative task offloading between SBS-MEC with cloud scheme will provide lower task execution latency than others in small-cell networks.