Hierarchical Cloud Computing Architecture for Context-Aware IoT Services

Abstract

This paper presents a new cloud computing model for context-aware Internet of Things services. The proposed computing model is hierarchically composed of two layers: a cloud control layer (CCL) and a user control layer (UCL). The CCL manages cloud resource allocation, service scheduling, service profile, and service adaptation policy from a system performance point of view. Meanwhile, the UCL manages end-to-end service connection and service context from a user performance point of view. The proposed model can support nonuniform service binding and its real-time adaptation using meta-objects. Furthermore, it supports intelligent service-context management using a supervised and reinforcement learning-based machine learning framework. We implemented a lightweight prototype of the proposed computing model. Evaluations confirm that the proposed computing model offers enhanced performance compared with legacy uniform computing models.