EAaaS: Edge Analytics as a Service

作者：Xiaomin Xu, Sheng Huang, Lance Feagan, Yaoliang Chen, Yunjie Qiu, Yu Wang · 年份：2017 · DOI：10.1109/icws.2017.130 · 被引用次数：30 · 研究领域：IoT and Edge/Fog Computing、Mobile Crowdsensing and Crowdsourcing、Water Quality Monitoring Technologies

In the Internet of Things (IoT) era, with ubiquitous remote sensing devices and other diverse data sources, nearly everything can forward voluminous data continuously, in real-time, which drives demand to perform real-time analytics on uninterrupted IoT data flows. The typical resultant approach is a cloud-centered architecture providing an analytic service for real-time IoT data processing. However, a cloud-centered IoT analytic service cannot guarantee real-time responsiveness has a high-fee pay-as-you-go business model, and opens data privacy concerns. Hence, it becomes rational to shift analytic workloads to the edge and provide a management service for edge analysis. Existing work on providing edge analytics as a service encountered challenges such as lacking a lightweight way to compose IoT applications based on multiple service providers, lacking a flexible and unified way to define domain-specific analytic logic, and maintaining efficiency when processing data on a resource-limited edge. This paper presents EAaaS, a scalable analytic service for enabling real-time edge analytics in IoT scenarios. In this work, we propose a unified rule-based analytic model to ease user's programming efforts in specifying rule-based analytic logic. Moreover, we also designed and implemented a high performance edge engine to apply rule-based analytic on incoming device data streams. To simplify the access to EAaaS service, a group of RESTful web interfaces is also designed for edge anal...