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- W4206247378 abstract "Driven by technologies such as IoT-enabled health care, machine learning applications at the edge, and industrial automation, mobile edge and fog computing paradigms have reinforced a general trend toward decentralized computing, where any network node can route traffic, compute tasks, and store data, possibly at the same time. In many such computing environments, there is a need to cache significant amounts of data, which may include large data sets, machine learning models, or executable code. In this work, we propose a framework for joint computation scheduling, caching, and request forwarding within such decentralized computing environments. We first characterize the stability region of a “genie-aided” computing network where data required by computation are instantly accessible, and develop a throughput optimal control policy for this model. Based on this, we develop a practically implementable distributed and adaptive algorithm, and show that it exhibits superior performance in terms of average task completion time, when compared to several baseline policies." @default.
- W4206247378 created "2022-01-26" @default.
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- W4206247378 date "2022-06-01" @default.
- W4206247378 modified "2023-09-27" @default.
- W4206247378 title "DECO: Joint Computation Scheduling, Caching, and Communication in Data-Intensive Computing Networks" @default.
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- W4206247378 doi "https://doi.org/10.1109/tnet.2021.3136157" @default.
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