| 研究実績の概要 |
The overall research achievements can be summarized as follows.
(1) The problem of cotask processing in multi-access edge computing (MEC) systems can be formulated as an NP-hard combinatorial problem. For this, we proposed a non-clairvoyant deep dual learning method to update the primal and dual variables (governed by two deep neural networks) iteratively.
(2) The problem of information sampling and transmission scheduling in MEC systems with serverless computing can be formulated as another NP-hard combinatorial problem. For this, we designed both offline (clairvoyant) and online (non-clairvoyant) age-efficient algorithms for the information sampling and transmission scheduling with and without the prior knowledge of the invocations of serverless functions, respectively.
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