边缘节点自主预测需迁移的数据，提升任务处理效率。
An Ensemble Scheme for Proactive Dominant Data Migration of Pervasive Tasks at the Edge
- 融合统计模型与机器学习的集成方法预测数据迁移需求。
- 可识别关键数据资产并检测请求密度变化。
- 适合边缘计算中海量物联网任务的数据管理场景。
当前，物联网与边缘计算交汇处的数据智能管理成为研究热点。本文提出一种由自主边缘节点实施的方案，用于识别应迁移到基础设施特定位置的合适数据，从而促进请求的有效处理。目标是使节点能够理解卸载数据驱动任务的访问模式，并预测哪些数据应返回至相关任务的原始节点。这些任务依赖于原始宿主节点缺失的数据，凸显了必须访问的关键数据资产。为推断这些数据区间，采用集成方法，结合统计模型与机器学习框架。结果可识别主导数据资产并检测请求密度。通过提供相关公式，详细分析所提方法，并与文献中现有模型进行评估对比。
原文摘要 · Abstract (English)
Nowadays, a significant focus within the research community on the intelligent management of data at the confluence of the Internet of Things (IoT) and Edge Computing (EC) is observed. In this manuscript, we propose a scheme to be implemented by autonomous edge nodes concerning their identifications of the appropriate data to be migrated to particular locations within the infrastructure, thereby facilitating the effective processing of requests. Our objective is to equip nodes with the capability to comprehend the access patterns relating to offloaded data-driven tasks and to predict which data ought to be returned to the original nodes associated with those tasks. It is evident that these tasks depend on the processing of data that is absent from the original hosting nodes, thereby underscoring the essential data assets that necessitate access. To infer these data intervals, we utilize an ensemble approach that integrates a statistically oriented model and a machine learning framework. As a result, we are able to identify the dominant data assets in addition to detecting the density of the requests. A detailed analysis of the suggested method is provided by presenting the related formulations, which is also assessed and compared with models found in the relevant literature.
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