Dai, X., Xiao, Z., Jiang, H., Alazab, M., Lui, J. C. S., Min, G., Dustdar, S., & Liu, J. (2023). Task Offloading for Cloud-Assisted Fog Computing With Dynamic Service Caching in Enterprise Management Systems. IEEE Transactions on Industrial Informatics, 19(1), 662–672. https://doi.org/10.1109/TII.2022.3186641
Augmented Intelligence of Things (AIoT); enterprise management systems (EMS); game theory; service caching; task offloading
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Abstract:
In enterprise management systems (EMS), augmented Intelligence of Things (AIoT) devices generate delay-sensitive and energy-intensive tasks for learning analytics, articulate clarifications, and immersive experiences. To guarantee effective task processing, in this work, we present a cloud-assisted fog computing framework with task offloading and service caching. In the framework, tasks make offloading decisions to determine local processing, fog processing, and cloud processing with the goal of minimal task delay and energy consumption, conditioned on dynamic service caching. To this end, we first propose a distributed task offloading algorithm based on non-cooperative game theory. Then, we adopt the 0-1 knapsack method to realize dynamic service caching. At last, we adjust the offloading decisions for the tasks offloaded to the fog server but without caching service support. Additionally, we conduct extensive experiments and the results validate the effectiveness of our proposed algorithms.
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Project (external):
National Natural Science Foundation of China Key Research and Development Project of Hunan Province of China Hunan Natural Science Foundation of China Funding Projects of Zhejiang Lab Open Research Funds from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Open Research Funds from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)
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Project ID:
Grant U20A20181 Grant 2022GK2020 Grant 2022JJ2059 Grant 2021LC0AB05 Grant GML-KF-22-22 Grant GML-KF-22-23