Abstract
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’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.
Highlights
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]
We investigate an innovative collaborative task offloading framework for multi-access edge computing (MEC)-enabled deployed small-cell networks (DDSCNs) that can compute various service requests based on the user’s demand
This is because our proposed collaborative approach can distribute the arrival user requests among Small Base Stations (SBS)-MEC servers, and it can improve the system performance to handle a large number of mobile devices
Summary
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]. 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. A fuzzy-logic based technique is proposed to deal with the collaborative task offloading problem in MEC-enabled SCNs. When the exact mathematical model is difficult to develop in a rapidly changing system that is dynamic and uncertain, fuzzy logic is the most employed method because its computation complexity is lower with respect to different decision-making algorithms [29,30,31].
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