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An Evolutionary Task Offloading Schema for Edge Computing

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Big Data and Security (ICBDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1210))

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Abstract

Edge computing allows users to access to applications with high-bandwidth and low-latency. The advantages include fast data transmission and task migration between mobile devices and edge cloud. In this work, we propose a novel task migration model with cached data to reduce service response time and energy consumption. An evolutionary task offloading schema is then developed to optimize the migration strategy on the edge cloud. As a result, our schema is able to minimize the aforementioned objective function while satisfying the resource constraints. We have conducted simulations to prove the effectiveness of our schema in energy-saving, during task migration.

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Author information

Authors and Affiliations

  1. State Grid Gansu Electric Power Information $ Communication Company, Lanzhou, China

    Pei Sun, Baojing Chen, Zhenwei Yang & Xing Li

  2. Nanjing Nari Information & Communication Technology Co. Ltd., Nanjing, China

    Shaocong Han & Huizhong Shi

Authors
  1. Pei Sun
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  2. Baojing Chen
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  3. Shaocong Han
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  4. Huizhong Shi
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  5. Zhenwei Yang
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  6. Xing Li
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Corresponding author

Correspondence to Pei Sun .

Editor information

Editors and Affiliations

  1. Nanjing Institute of Technology, Nanjing, China

    Yuan Tian

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Tinghuai Ma

  3. King Saud University, Riyadh, Saudi Arabia

    Muhammad Khurram Khan

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Sun, P., Chen, B., Han, S., Shi, H., Yang, Z., Li, X. (2020). An Evolutionary Task Offloading Schema for Edge Computing. In: Tian, Y., Ma, T., Khan, M. (eds) Big Data and Security. ICBDS 2019. Communications in Computer and Information Science, vol 1210. Springer, Singapore. https://doi.org/10.1007/978-981-15-7530-3_40

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  • DOI: https://doi.org/10.1007/978-981-15-7530-3_40

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7529-7

  • Online ISBN: 978-981-15-7530-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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