SAMURAI: A Streaming Multi-tenant Context-Management Architecture for Intelligent and Scalable Internet of Things Applications

Abstract

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.