QoE‐aware mobile computation offloading in mobile edge computing

Abstract

Due to the rapid development of smart phone utilization, the use of fast response with delay sensitive applications leads to high traffic demands. These issues are not yet fulfill by current researchers. When there is a massive crowd of users gathering in hotspot areas such as, railway stations, corporate centers, and malls requires service accessing social networks, then there cause uncertain traffic with congestion and network delay. In this article, an Efficient Offloading Mechanism (EOM) is proposed to transfer the user's intensive computations to picocells, where each picocell (PE) is integrated with the functionalities of edge. Moreover, a novel distributed multihop mesh middle layer (DMMM) framework is proposed for seamless communication, where the self‐healing network provides high resilience, immediate response, and multiuser content sharing and saves battery power. Generally, a picocell is a distributed antenna system that is an alternative to the repeater utilized to extend wireless services to 100 users. An Adaptive Ant Optimization (AAO) algorithm is developed for backhaul routing conflicts. Simulation outcomes of EdgeCloudSim prove that the offloading tiny devices incorporated with considerable additional computational capacities to fulfill all the demands within 15 ms delay from each network.

A fairness‐aware task offloading method in edge‐enabled IIoT with multi‐constraints using AGE‐MOEA and weighted MMF

“…Afterwards, the value of OC (𝜃,v) and A ′ 𝜃 are updated for the next step (Lines 9 and 10). At last, we obtain MMF (𝜃,v) by traversing OC (𝜃,v) (Lines [15][16][17][18][19]. The MMF (𝜃,v) can be calculated by Equations ( 19)and ( 20)…”

“…The number of multi-constrained task sets [2][3][4][5][6][7][8][9][10][11][12][13][14] The number of edge servers [14][15][16] The virtual machine capacity of edge servers [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] The CPU frequency of edge servers [2000-2500] MHz…”

“…Then, a task offloading scheme is proposed based on the Takagi–Sugeno fuzzy neural network and game theory, which attained industry‐leading optimization results. Likewise, Sivasakthi et al 18 proposed an efficient offloading mechanism to transfer the user's intensive computations to picocells, where each picocell is integrated with the functionalities of edge, thus achieving the effect of fulfilling all the demands within the minimum delay. Aiming to protect the users' privacy, especially the spatial‐temporal context information, Qi et al 19 designed a privacy‐aware data prediction and fusion method for the smart city industrial scenario with MEC assistance, which attained satisfactory performance.…”

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

“…The evaluations have been performed in a realistic testbed, and the results show a significant improvement concerning task execution probability. In another study [225], an intelligent task offloading mechanism is proposed to transfer intensive tasks to picocells equipped with computing power. Moreover, a framework is also proposed to work as a mesh middle layer to provide seamless and high resilience communication.…”