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A prediction and budget-aware offloading scheme for wearable computing

机译:用于可穿戴计算的预测和预算感知卸载方案

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摘要

Wearable devices are very popular nowadays due to their computing and communication abilities. Limited computation, battery, and storage capabilities hinder the growth of emerging latency-sensitive wearable device-based applications. At present, the existing works on wearable computing suffered from higher latency, energy, and user payment due to lack of wearable device users mobility prediction, budget-aware offloading, deadline, resources availability, cooperation, and energy cost awareness. This paper proposes a prediction and budget-aware task offloading for wearable computing by investigating the wearable device users' payment, mobility, cloud, and network resource availability, and task requirements. The effectiveness of the proposed prediction and budget-aware offloading scheme for wearable computing is evaluated by comparing its results with the baseline greedy and independent resource selection scheme. The experimental results demonstrate that our proposed scheme provides a significant performance gain than the compared schemes in terms of makespan delay, user payment, utility, energy cost, and service provider profit.
机译:可穿戴设备因其计算和通信能力而非常流行。有限的计算、电池和存储能力阻碍了新兴的基于延迟敏感的可穿戴设备应用的增长。目前,由于缺乏可穿戴设备用户移动性预测、预算意识卸载、期限、资源可用性、合作和能源成本意识,现有的可穿戴计算工作存在较高的延迟、能源和用户支付问题。本文通过调查可穿戴设备用户的支付、移动性、云和网络资源可用性以及任务需求,提出了一种可穿戴计算的预测和预算感知任务卸载方法。通过与基线贪婪和独立资源选择方案的比较,评估了所提出的可穿戴计算预测和预算感知卸载方案的有效性。实验结果表明,所提方案在时延、用户支付、公用事业、能源成本和服务提供商利润等方面均比对照方案具有显著的性能提升。

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