A Game Theory-Based Energy-Efficient Resource Allocation Model for Distributed Systems

The Distributed Systems (DS) cloud is an efficient method for storing, processing, and transferring substantial data generated by the Internet of Things (IoT). A primary challenge in this distributed architecture is the optimal allocation of bandwidth across the geographically distributed DSs in the cloud. This research seeks to address this difficulty by maximising transfer capacity across multiple DS. The study first examines the interplay between the communications of physical systems and the data flow in DS clouds, developing a game-theoretic framework for resource distribution across clouds. This framework proposes a dynamic Resource Allocation (RA) technique and an associated technology that is flexible for Internet circumstances. This new strategy accomplishes load balancing across physical links and information transfer between various DS by considering background congestion, capacity limitations of bodily systems, and DS flows and resource demand. Real-world trace information is utilised to assess the efficacy and effectiveness of the suggested RA plan. Compared to current methods, the assessment findings indicate that the proposed strategy effectively balances the demands of physical infrastructure, reduces cloud users' reaction time, and offers exceptional flexibility.

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