Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145340
Title: Security modeling and efficient computation offloading for service workflow in mobile edge computing
Authors: Huang, Binbin
Li, Zhongjin
Tang, Peng
Wang, Shangguang
Zhao, Jun
Hu, Haiyang
Li, Wanqing
Chang, Victor
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Huang, B., Li, Z., Tang, P., Wang, S., Zhao, J., Hu, H., . . . Chang, V. (2019). Security modeling and efficient computation offloading for service workflow in mobile edge computing. Future Generation Computer Systems, 97, 755-774. doi:10.1016/j.future.2019.03.011
Journal: Future Generation Computer Systems
Abstract: It is a big challenge for resource-limited mobile devices (MDs) to execute various complex and energy-consumed mobile applications. Fortunately, as a novel computing paradigm, edge computing (MEC) can provide abundant computing resources to execute all or parts of the tasks of MDs and thereby can greatly reduce the energy of MD and improve the QoS of applications. However, offloading workflow tasks to the MEC servers are liable to external security threats (e.g., snooping, alteration). In this paper, we propose a security and energy efficient computation offloading (SEECO) strategy for service workflows in MEC environment, the goal of which is to optimize the energy consumption under the risk probability and deadline constraints. First, we build a security overhead model to measure the execution time of security services. Then, we formulate the computation offloading problem by incorporating the security, energy consumption and execution time of workflow application. Finally, based on the genetic algorithm (GA), the corresponding coding strategies of SEECO are devised by considering tasks execution order and location and security services selection. Extensive experiments with the variety of workflow parameters demonstrate that SEECO strategy can achieve the security and energy efficiency for the mobile applications. ’
URI: https://hdl.handle.net/10356/145340
ISSN: 0167-739X
DOI: 10.1016/j.future.2019.03.011
Rights: © 2019 Elsevier B.V. All rights reserved. This paper was published in Future Generation Computer Systems and is made available with permission of Elsevier B.V.
Fulltext Permission: embargo_20211231
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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