Abstract:
IoT services’ highly dynamic fog computing resource requirements create an imbalance between computing resource providers and consumers. However, current computing resource scheduling schemes cannot recognize dynamic resources or make decisions or manage them, resulting in inefficient resource use and lower QoS.
Cognitively balancing IoT edge computing resources is unsolved. For edge intelligence-enabled IoT, this paper proposes cognition-centric fog computing resource balancing (CFCRB). First, a cognitive balance architecture with a cognition plane includes service demand monitoring, policy processing, and cognitive fog resource knowledge storage.
Second, the fog functions structure with sensing, interaction, and learning capabilities enables knowledge-based proactive discovery and dynamic orchestration of resource sharing nodes. Finally, a distributed edge learning algorithm is proposed to build knowledge of the computing resource helper-requester balance in cognitive fogs, which is mathematically proven. Simulations show the scheme’s efficiency.
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