Fuzzy Based Collaborative Task Offloading Scheme in the Densely Deployed Small-Cell Networks with Multi-Access Edge Computing

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.

Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks

“…To minimize the transmission distance and increase the capacity of edge caching systems, Yuan et al [ 26 ] proposed a cooperation approach among edge clouds. Hossain et al [ 27 ] used a collaboration approach based on fuzzy logic among MEC servers to reduce the task failure rate and service time. Moreover, Fan et al [ 28 ] used a cooperation approach between different servers for balancing the computation workload.…”

“…Our proposed system, however, uses only a single stage of fuzzy logic operation to select the optimal target server. In [ 27 ], Hossain et al considered a collaborative approach for task offloading based on fuzzy logic. In this paper, authors considered local MEC and neighboring MEC servers to select the target server for task offloading decisions.…”

“…It is critical to define the fuzzy rules, because the overall performance of the system relies particularly on these rules. In this study, we use a better fuzzy rule set found empirically in [ 27 , 38 ]. Some examples of rules from our fuzzy rule set are given in Table 3 .…”

Fuzzy‐Based Mobile Edge Orchestrators in Heterogeneous IoT Environments: An Online Workload Balancing Approach

“…In [19], Nguyen et al propose the collaborative task offloading scheme between mobile device-edge-cloud servers by adding the WLAN delay as a fuzzy input parameter. Meanwhile, Hossain et al [20] use the neighboring MEC VM utilization as the fuzzy input parameter. However, the existing system leads to unbalanced loads among edge servers when the system is overloaded.…”

Linked-Object Dynamic Offloading (LODO) for the Cooperation of Data and Tasks on Edge Computing Environment

“…As a result, the workload will increase, long queues, and processing delays of tasks accordingly. In limited computational resources, a multi-MEC system has been proposing by joint communication offloading methods at the ends and edges [16][17][18][19]. However, due to the dynamic environment of the computing system, it is not easy to achieve task offload performance.…”