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(Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading

(Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading

Low Choon Keat, Ang Tan Fong, Chun Yong Chong, Tew Yiqi
Copyright: © 2022 |Volume: 19 |Issue: 1 |Pages: 28
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781799893462|DOI: 10.4018/IJWSR.299017
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MLA

Keat, Low Choon, et al. "(Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading." IJWSR vol.19, no.1 2022: pp.1-28. http://doi.org/10.4018/IJWSR.299017

APA

Keat, L. C., Fong, A. T., Chong, C. Y., & Yiqi, T. (2022). (Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading. International Journal of Web Services Research (IJWSR), 19(1), 1-28. http://doi.org/10.4018/IJWSR.299017

Chicago

Keat, Low Choon, et al. "(Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading," International Journal of Web Services Research (IJWSR) 19, no.1: 1-28. http://doi.org/10.4018/IJWSR.299017

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Abstract

Fog computing is a potential solution for the Internet of Things in close connection with things and end-users. Fog computing will easily transfer sensitive data without delaying distributed devices. Moreover, fog computing is more in real-time streaming applications, sensor networks, IoT which need high speed and reliable internet connectivity. Due to the heterogeneous and distributed characteristics, finley distributing the task with computation offloading is a challenging task. Developing an efficient QoE-aware application mapping policy is challenging due to the different user interests. The energy consumption would usually increase after such an algorithm and policy are implemented. In this paper, we enhanced the future from the previous QoE paper by proposing a computation offloading algorithm. The proposed algorithm is to prevent overloading on fog devices. Our proposed solution has been evaluated and compared with other existing solutions, the results show that our proposed solution performs better in terms of execution time, energy consumption, and network usage.