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- W4387092313 abstract "Fog computing alleviates the cloud-centric limitations of Internet of Things (IoT). However, in the dynamic landscape of fog computing, the uneven distribution of workload among fog nodes emerges as a substantial obstacle to both, data latency and network profit. To mitigate workload imbalances, data packet offloading offers a twofold benefit. The offloading fog node leverages latency satisfaction, while the recipient fog node gains a financial advantage by leasing out its available processing resources. Motivated by the aforementioned advantages, in this work, we propose a novel load-balancing method to maximize monetary gains without affecting the Quality-of-Service (QoS) constraints of the subscribed IoT users in a biased fog network. The proposed method introduces an Optimized Matching Theory (OMAT)-guided data offloading framework, employing many to many matching without externalities. The method returns a novel matching among disparate fog nodes thereby achieving uniform workload distribution. The obtained results demonstrate that the proposed method attains improved performance in terms of inverse latency, throughput, and non-matchings, when compared to existing methods in the literature." @default.
- W4387092313 created "2023-09-28" @default.
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- W4387092313 date "2023-01-01" @default.
- W4387092313 modified "2023-09-28" @default.
- W4387092313 title "A Computationally-Efficient and QoS-Aware Data Offloading Framework for Biased Fog Networks" @default.
- W4387092313 doi "https://doi.org/10.1109/tcsii.2023.3319977" @default.
- W4387092313 hasPublicationYear "2023" @default.
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