Cognitive Balance for Fog Computing Resource in Internet of Things: An Edge Learning Approach


At the moment, there is an imbalance between the providers and consumers of computing resources as a result of the highly dynamic requirements for fog computing resources that were brought about by the numerous services offered by the Internet of Things (IoT). However, the current scheduling schemes for computing resources are unable to recognize the dynamic resources that are available, and they do not have the capabilities necessary for decision-making or management. This results in an inefficient use of computing resources and a lower quality of service (QoS). The issue of cognitively balancing computing resources at the edge of the IoT has not yet been resolved. A cognition-centric fog computing resource balancing (CFCRB) scheme is proposed in this paper for edge intelligence-enabled Internet of Things applications. To begin, we suggest an architecture for cognitive balance with a cognition plane. This architecture features service demand monitoring, policy processing, and knowledge storage of cognitive fog resources. Second, we propose a structure for fog functions that includes sensing, interaction, and learning capabilities. This will allow for knowledge-based proactive discovery and the dynamic orchestration of resource sharing nodes. In conclusion, a distributed edge learning algorithm is proposed in order to construct knowledge of the equilibrium between computing resource helpers and requesters in cognitive fogs. This algorithm's efficacy is further demonstrated through the application of mathematics. The outcomes of the simulation demonstrate that the strategy that was proposed is effective.

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