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<p>With the development of better communication networks and other related technologies, the IoT has become an integral part of modern IT. However, mobile devices’ limited memory, computing power, and battery life pose significant challenges to their widespread use. As an alternate, mobile cloud computing (MCC) makes good use of cloud resources to boost mobile devices’ storage and processing capabilities. This involves moving some program logic to the cloud, which improves performance and saves power. Techniques for mobility-aware offloading are necessary because device movement affects connection quality and network access. Depending on less-than-ideal mobility models, insufficient fault tolerance, inaccurate offloading, and poor task scheduling are just a few of the limitations that current mobility-aware offloading methods often face. Using fault-tolerant approaches and user mobility patterns defined by a Markov chain, this research introduces a novel decision-making framework for mobility-aware offloading. The evaluation findings show that compared to current approaches, the suggested method achieves execution speeds up to 77.35% faster and energy use down to 67.14%.</p>

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Fault-tolerant and mobility-aware loading via Markov chain in mobile cloud computing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月29日

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  • Ning Wang,
  • Ya Li,
  • Yuanbang Li,
  • Huanxin Nie

摘要

With the development of better communication networks and other related technologies, the IoT has become an integral part of modern IT. However, mobile devices’ limited memory, computing power, and battery life pose significant challenges to their widespread use. As an alternate, mobile cloud computing (MCC) makes good use of cloud resources to boost mobile devices’ storage and processing capabilities. This involves moving some program logic to the cloud, which improves performance and saves power. Techniques for mobility-aware offloading are necessary because device movement affects connection quality and network access. Depending on less-than-ideal mobility models, insufficient fault tolerance, inaccurate offloading, and poor task scheduling are just a few of the limitations that current mobility-aware offloading methods often face. Using fault-tolerant approaches and user mobility patterns defined by a Markov chain, this research introduces a novel decision-making framework for mobility-aware offloading. The evaluation findings show that compared to current approaches, the suggested method achieves execution speeds up to 77.35% faster and energy use down to 67.14%.