Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:IEEE ACCESS

Key Words:Task analysis Edge computing Energy consumption Cloud computing Privacy Biological system modeling Computational modeling Mobile edge computing (MEC) task offloading trust evaluation machine learning privacy protection

Abstract:Task offloading in Mobile Edge Computing (MEC) is a solution to augment resource-limited mobile devices capabilities by migrating tasks to the edge of the network (i.e., edge servers and idle devices). At present, a lot of work is focused on optimizing policies to reduce latency or energy consumption for users. However, they mostly ignore that services are not necessarily trustworthy because the resource providers are complex, dynamic, and unreliable. The trustworthiness of a service in our paper mainly includes two aspects. One is that resource providers will not violate users privacy. The other is that resource providers will perform well to ensure the effectiveness of services. To solve this problem, we propose a trust-aware task offloading framework. The main purpose of the framework is to select a resource provider for a user to reduce latency or energy consumption and ensure service trustworthiness at the same time. The framework can be divided into three modules (i.e., trust evaluation, filtering and selection). By combining trust evaluation and filtering modules, some resource providers that are not trusted by users are filtered out to ensure that the services provided to users are trustworthy. In the selection module, we select an appropriate provider for a user from the qualified (i.e., left after the filtering process) resource providers based on an offloading policy. The experimental results show that our framework not only reduces latency or energy consumption for users, but also reduces the failure rate of tasks.

ISSN No.:2169-3536

Translation or Not:no

Date of Publication:2019-01-01

Co-author:Wu, Dexiang,Huang ZhiQiu,Cao, Yan,Du, Tianbao

Correspondence Author:Shen Guohua