SAMURAI: A Streaming Multi-tenant Context-Management Architecture for Intelligent and Scalable Internet of Things Applications
SAMURAI: A Streaming Multi-tenant Context-Management Architecture for Intelligent and Scalable Internet of Things Applications
复制标题
DOI：
10.1109/ie.2014.43
复制
发表时间：
2014-06
期刊：
影响因子：
--
通讯作者：
Davy Preuveneers;Y. Berbers
中科院分区：
文献类型：
--
作者：
Davy Preuveneers;Y. Berbers
在物联网中，传感器事件的异构性和分布性是智能环境中情景感知行为的驱动因素。然而，处理事件数据通常贯穿物联网应用的业务逻辑，并将此类可重用功能作为服务提供给具有不同需求的各种客户，通常面临可扩展性问题。我们提出了Samurai，这是一个多租户流媒体上下文体系结构，集成并公开了用于复杂事件处理、机器学习、知识表示、NoSQL持久化和内存数据网格的众所周知的组件。Samurai追求两种方法来实现可伸缩性：(1)具有水平可伸缩性的分布式部署；(2)通过多租户共享资源。对于我们的体系结构的试验性评估中使用的场景，结果表明支持多租户的开销很小，具有近线性的可扩展性和灵活的弹性，可以部署每个租户的数据分区方案。
In the Internet of Things, heterogeneous and distributed streams of sensor events is a driver for context-aware behavior in intelligent environments. However, processing the event data usually cross-cuts the business logic of IoT applications and offering such reusable functionality as a service towards a variety of customers with different needs is often faced with scalability concerns. We present SAMURAI, a multi-tenant streaming context architecture that integrates and exposes well-known components for complex event processing, machine learning, knowledge representation, NoSQL persistence and in-memory data grids. SAMURAI pursues a twofold approach to achieve scalability: (1) distributed deployment with horizontal scalability, (2) shared resources through multi-tenancy. For the scenario used in the experimental evaluation of our architecture, the results show little overhead to support multi-tenancy, with near-linear scalability and flexible elasticity for deployment schemes with data partitioning per tenant.