GLT: Edge Gateway ELT for Data-Driven Intelligence Placement

In this paper, we introduce the notion of Data Lagoons at the Edge, for the dynamic deployment and migration of analytics tasks at runtime. We present a novel, modular architecture that decouples data ingestion from data processing at the Edge, facilitating the rapid instantiation of data processing components or their offloading to the cloud. To this end, we analyze the requirements for the use cases of Internet Of Things (IoT) Platforms and Multimedia Content Distribution Applications and we present for the former a prototype architecture, based on open source components. Our approach can support the realization of reasoning mechanisms for trade-off analysis and adaptive task relocation, based on real-time monitored measures such as delay and throughput.

[1]  Minghua Chen,et al.  Understanding Performance of Edge Content Caching for Mobile Video Streaming , 2017, IEEE Journal on Selected Areas in Communications.

[2]  Kevin Wilkinson,et al.  Data integration flows for business intelligence , 2009, EDBT '09.

[3]  Wolfgang Lehner,et al.  Frequent patterns in ETL workflows: An empirical approach , 2017, Data Knowl. Eng..

[4]  Teruo Higashino,et al.  Edge-centric Computing: Vision and Challenges , 2015, CCRV.

[5]  Mary Roth,et al.  Data Wrangling: The Challenging Yourney from the Wild to the Lake , 2015, CIDR.

[6]  Carlo Batini,et al.  Methodologies for data quality assessment and improvement , 2009, CSUR.

[7]  Tapani Ristaniemi,et al.  Learn to Cache: Machine Learning for Network Edge Caching in the Big Data Era , 2018, IEEE Wireless Communications.

[8]  Sofie Verbrugge,et al.  Multi-sided Platforms for the Internet of Things , 2018, BMSD.

[9]  Marin Litoiu,et al.  Towards Mitigation of Low and Slow Application DDoS Attacks , 2014, 2014 IEEE International Conference on Cloud Engineering.

[10]  Taleb Tarik,et al.  On-the-Fly QoE-Aware Transcoding in the Mobile Edge , 2016 .

[11]  Ilias Gerostathopoulos,et al.  Guaranteed latency applications in edge-cloud environment , 2018, ECSA.

[12]  Xinyu Yang,et al.  A Survey on the Edge Computing for the Internet of Things , 2018, IEEE Access.

[13]  Saurabh Goyal,et al.  Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things , 2017, ICML.

[14]  Christoph Quix,et al.  Metadata Extraction and Management in Data LakesWith GEMMS , 2016, Complex Syst. Informatics Model. Q..