A Fuzzy-Based Mobile Edge Architecture for Latency-Sensitive and Heavy-Task Applications

Abstract

Appropriate task offloading management strategy is a challenging problem for high delay-sensitive and heavy-task applications. This paper proposes a fuzzy-based mobile edge manager with task partitioning, which can handle the multi-criteria decision-making process by considering multiple parameters in the MEC network framework and make appropriate offloading decisions for incoming tasks of IoT applications. Considering that the mobile devices are becoming more and more powerful, this paper also takes WLAN delay and the computing power of mobile devices into account, forming a three-level fuzzy logic system. In addition, since many tasks of Internet of Things applications are composed of several independent modules, this paper also sets two optimal task partitioning ratios, which have symmetry, so that each module can be independently executed in each layer of the MEC network. In addition, results will return to the mobile devices after execution, so as to minimize the service time and improve QoS. Finally, several indexes such as task failure rate and service time are simulated, and the results show that the proposed scheme has better performance compared with the other four comparison schemes, especially for high-latency sensitivity and heavy-task applications.

Flexible Offloading and Task Scheduling for IoT Applications in Dynamic Multi-Access Edge Computing Environments

“…In the research of computation offloading for application-level services, the timedependent characteristics of tasks within the same application can be modeled as a directed acyclic graph (DAG) [17,18,[20][21][22][30][31][32][33], based on which the tasks can be sequentially offloaded to multiple MEC servers to improve service quality. Xu et al in [33] proposed a novel offloading algorithm for time-dependent tasks, which minimized the makespan by finding the dynamic critical path based on the task graph.…”

“…However, the above literature concentrated on scenarios involving a single application and multiple MEC nodes. Shi et al,in [30], proposed a fuzzy-based mobile edge architecture with task partitioning to efficiently offload tasks of IoT applications in multi-layer MEC networks. Fu et al in [18] considered the fog/edge collaborative system and developed a priority and dependency-based DAG tasks offloading algorithm (PDAGTO) to minimize the application delay while meeting energy consumption requirements.…”

A computation offloading method with distributed double deep Q‐network for connected vehicle platooning with vehicle‐to‐infrastructure communications

“…In addition, with the rapid development of wireless communication, the wireless data transmission rate has significantly improved, which has brought many promising applications to the thriving connected and automated vehicle (CAV) [1], such as autonomous driving, interactive gaming, and real-time navigation [2]. Typically, such applications are computationally intensive and latency sensitive, and the local computing and network resources of the vehicle may not meet their requirements [3].…”