scholarly journals Self-Adaptive Learning of Task Offloading in Mobile Edge Computing Systems

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1146
Author(s):  
Peng Huang ◽  
Minjiang Deng ◽  
Zhiliang Kang ◽  
Qinshan Liu ◽  
Lijia Xu
Keyword(s):  
Mobile Devices ◽  
Adaptive Learning ◽  
Urban Areas ◽  
High Performance ◽  
Dynamic Environment ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Computationally Intensive ◽  
Task Offloading ◽  
Self Adaptive

Mobile edge computing (MEC) focuses on transferring computing resources close to the user's device, and it provides high-performance and low-delay services for mobile devices. It is an effective method to deal with computationally intensive and delay-sensitive tasks. Given the large number of underutilized computing resources for mobile devices in urban areas, leveraging these underutilized resources offers tremendous opportunities and value. Considering the spatiotemporal dynamics of user devices, the uncertainty of rich computing resources and the state of network channels in the MEC system, computing resource allocation in mobile devices with idle computing resources will affect the response time of task requesting. To solve these problems, this paper considers the case in which a mobile device can learn from a neighboring IoT device when offloading a computing request. On this basis, a novel self-adaptive learning of task offloading algorithm (SAda) is designed to minimize the average offloading delay in the MEC system. SAda adopts a distributed working mode and has a perception function to adapt to the dynamic environment in reality; it does not require frequent access to equipment information. Extensive simulations demonstrate that SAda achieves preferable latency performance and low learning error compared to the existing upper bound algorithms.

scholarly journals Privacy-Aware Online Task Offloading for Mobile-Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Dali Zhu ◽  
Ting Li ◽  
Haitao Liu ◽  
Jiyan Sun ◽  
Liru Geng ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Mobile Networks ◽  
Location Privacy ◽  
Optimization Problems ◽  
Energy Performance ◽  
Edge Computing ◽  
System Level ◽  
Mobile Edge Computing ◽  
Task Offloading

Mobile edge computing (MEC) has been envisaged as one of the most promising technologies in the fifth generation (5G) mobile networks. It allows mobile devices to offload their computation-demanding and latency-critical tasks to the resource-rich MEC servers. Accordingly, MEC can significantly improve the latency performance and reduce energy consumption for mobile devices. Nonetheless, privacy leakage may occur during the task offloading process. Most existing works ignored these issues or just investigated the system-level solution for MEC. Privacy-aware and user-level task offloading optimization problems receive much less attention. In order to tackle these challenges, a privacy-preserving and device-managed task offloading scheme is proposed in this paper for MEC. This scheme can achieve near-optimal latency and energy performance while protecting the location privacy and usage pattern privacy of users. Firstly, we formulate the joint optimization problem of task offloading and privacy preservation as a semiparametric contextual multi-armed bandit (MAB) problem, which has a relaxed reward model. Then, we propose a privacy-aware online task offloading (PAOTO) algorithm based on the transformed Thompson sampling (TS) architecture, through which we can (1) receive the best possible delay and energy consumption performance, (2) achieve the goal of preserving privacy, and (3) obtain an online device-managed task offloading policy without requiring any system-level information. Simulation results demonstrate that the proposed scheme outperforms the existing methods in terms of minimizing the system cost and preserving the privacy of users.

scholarly journals A Secure Multi-Tier Mobile Edge Computing Model for Data Processing Offloading Based on Degree of Trust

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3211 ◽  
Author(s):  
Francisco Mora-Gimeno ◽  
Higinio Mora-Mora ◽  
Diego Marcos-Jorquera ◽  
Bruno Volckaert
Keyword(s):  
Data Processing ◽  
Mobile Devices ◽  
Response Times ◽  
Edge Computing ◽  
Security Level ◽  
Security Model ◽  
Mobile Edge Computing ◽  
Formal Framework ◽  
Computationally Intensive ◽  
Application Execution

Current mobile devices need to run applications with high computational demands and critical response times. The mobile edge computing (MEC) paradigm was developed to improve the performance of these devices. This new computation architecture allows for the mobile devices to execute applications on fog nodes at the network edge; this process is called data processing offloading. This article presents a security model for the externalization of application execution in multi-tier MEC environments. The principal novelty of this study is that the model is able to modify the required security level in each tier of the distributed architecture as a function of the degree of trust associated with that tier. The basic idea is that a higher degree of trust requires a lower level of security, and vice versa. A formal framework is introduced that represents the general environment of application execution in distributed MEC architectures. An architecture is proposed that allows for deployment of the model in production environments and is implemented for evaluation purposes. The results show that the security model can be applied in multi-tier MEC architectures and that the model produces a minimal overhead, especially for computationally intensive applications.

A Novel Framework for Mobile Edge Computing By Optimizing Task Offloading

2021 ◽  
pp. 1-1
Author(s):  
Naouri Abdenacer ◽  
Hangxing Wu ◽  
Nouri Nabil Abdelkader ◽  
Sahraoui Dhelim ◽  
Huansheng Ning
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing ◽  
Task Offloading
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