Cognitive Balance for Fog Computing Resource in Internet of Things: An Edge Learning Approach
Siyi Liao, Jun Wu, Shahid Mumtaz, Jianhua Li, Rosario Morello, Mohsen Guizani
Abstract
Currently, the highly dynamic fog computing resource requirements introduced by the diverse services of the Internet of Things (IoT) result in an imbalance between computing resource providers and consumers. However, current computing resource scheduling schemes cannot cognize the dynamic resources available and do not possess decision-making or management capabilities, which leads to inefficient use of computing resources and a decreased quality of service (QoS). Balancing computing resources cognitively at the IoT edge remains unresolved. In this paper, a cognition-centric fog computing resource balancing (CFCRB) scheme is proposed for edge intelligence-enabled IoT. First, we propose a cognitive balance architecture with a cognition plane, which includes service demand monitoring, policy processing and knowledge storage of cognitive fog resources. Second, we propose the fog functions structure with sensing, interaction and learning functionalities, realizing the knowledge-based proactive discovery and dynamic orchestration of resource sharing nodes. Finally, a distributed edge learning algorithm is proposed to construct knowledge of the balance between computing resource helpers and requesters in cognitive fogs, which is further proved with mathematics. The simulation results indicate the efficiency of the proposed scheme.