论文标题
S2CE：用于采矿的混合云和边缘编排，Exascale分布式流
S2CE: A Hybrid Cloud and Edge Orchestrator for Mining Exascale Distributed Streams
论文作者
论文摘要
由分布式和异质节点（例如物联网和其他设备）生成的数据的爆炸性增加，速度，多样性和准确性，在大数据处理平台和采矿技术中不断挑战最先进的状态。因此，它揭示了迫切需要解决这次预期的Exascale数据生成与从这些数据中提取见解之间的差距。为了满足这一需求，本文提出了流到Cloud＆Edge（S2CE）的流，这是同类，优化，多云和边缘编目的首个，易于配置，可扩展且可扩展。 S2CE将对在混合云和边缘设置上运行的大量和异质数据流进行机器和深度学习，同时为实用且可扩展的处理提供必要的功能：数据融合和预处理，采样和合成流的生成，云和边缘生成，云和边缘智能资源管理以及分布式处理。
The explosive increase in volume, velocity, variety, and veracity of data generated by distributed and heterogeneous nodes such as IoT and other devices, continuously challenge the state of art in big data processing platforms and mining techniques. Consequently, it reveals an urgent need to address the ever-growing gap between this expected exascale data generation and the extraction of insights from these data. To address this need, this paper proposes Stream to Cloud & Edge (S2CE), a first of its kind, optimized, multi-cloud and edge orchestrator, easily configurable, scalable, and extensible. S2CE will enable machine and deep learning over voluminous and heterogeneous data streams running on hybrid cloud and edge settings, while offering the necessary functionalities for practical and scalable processing: data fusion and preprocessing, sampling and synthetic stream generation, cloud and edge smart resource management, and distributed processing.
