A Density-Based Offloading Strategy for IoT Devices in Edge Computing Systems | Zendy

Cheng Zhang | Zendy; Hailiang Zhao | Zendy; Shuiguang Deng | Zendy

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A Density-Based Offloading Strategy for IoT Devices in Edge Computing Systems

Author(s) -

Cheng Zhang,

Hailiang Zhao,

Shuiguang Deng

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2882452

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Collaboration spaces formed from edge servers can efficiently improve the quality of experience of service subscribers. In this paper, we first utilize a strategy based on the density of Internet of Things (IoT) devices and k-means algorithm to partition network of edge servers, then an algorithm for IoT devices' computation offloading decisions is proposed, i.e., whether we need to offload IoT devices' workload to edge servers, and which edge server to choose if migration is needed. The combination of locations of edge servers and the geographic distribution of various IoT devices can significantly improve the scheduling of network resources and satisfy requirements of service subscribers. We analyze and build mathematical models about whether/how to offload tasks from various IoT devices to edge servers. In order to better simulate operations of the mobile edge servers in more realistic scenarios, the input size of each IoT device is uncertain and regarded as a random variable following some probability distribution based on long-term observations. On the basis of that, an algorithm utilizing sample average approximation method is proposed to discuss whether the tasks to be executed locally or offloaded. Besides, the algorithm proposed can also help decide whether service relocation/migration is needed or not. Finally, simulation results show that our algorithm can achieve 20% of global cost less than the benchmark on a true base station dataset of Hangzhou.

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