ADAPTIVE OFFLOADING MECHANISM IN HIERARCHICAL MULTI-CLOUD FRAMEWORK

Computer Science

This research effort performs adaptive offloading across context-aware, capability-aware, and energy-aware models in order to meet the needs and provide context adaptability. Demand-based offloading satisfies a variety of application needs and provides efficient resource allocation. These offloading strategies are only effective when they are used in conjunction with the appropriate cloud resource selection. Applications perform worse when a single kind of cloud resource has problems with availability, dependability, backhaul network congestion, and unusual connection loss during offloading. As a result, the single cloud architecture is less flexible to meet application requirements. By creating a multi-cloud framework made up of heterogeneous cloud resources most suited for the suggested offloading models, this study introduces a change in cloud architecture. To achieve context adaptability, a game-theoretic method is suggested for choosing the appropriate offloading model and cloud resource. Using this method, the interactions between the players and other entities—like user gear, the root server, and the app vendor—are studied. These interactions aid in determining the best offload option that benefits each entity individually. Based on their computation and communication impacts with other players, such entities' usefulness is made available to the participants.

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