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The results show that the performance of AdaGrad optimization solver is the best when the feature dimension is 3, the number of LSTM network layers is 6, the time series length is 30\u201345, the batch size is 128, the training time is 788\u00a0s, the number of units is 250, and the number of times is 350. Compared with the traditional methods, the proposed load prediction model and power management mechanism improve the prediction accuracy by 4.21%. Compared with autoregressive integrated moving average (ARIMA) load prediction, the dynamic power management method of LSTM load prediction can reduce energy consumption by 12.5% and realize the balance between EDC system performance and energy consumption. The system can effectively meet the requirements of multi-access edge computing (MEC) for low delay, high bandwidth and high reliability, reduce unnecessary energy consumption and waste, and reduce the cost of MEC service providers in actual operation. This exploration has important reference value for promoting the energy-saving development of Internet-related industries.<\/jats:p>","DOI":"10.1007\/s40747-022-00666-0","type":"journal-article","created":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T02:02:44Z","timestamp":1647568964000},"page":"3867-3879","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Energy-saving service management technology of internet of things using edge computing and deep learning"],"prefix":"10.1007","volume":"8","author":[{"given":"Defeng","family":"Li","sequence":"first","affiliation":[]},{"given":"Mingming","family":"Lan","sequence":"additional","affiliation":[]},{"given":"Yuan","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,18]]},"reference":[{"issue":"8","key":"666_CR1","doi-asserted-by":"publisher","first-page":"1769","DOI":"10.1109\/JSAC.2019.2927065","volume":"37","author":"DA Chekired","year":"2019","unstructured":"Chekired DA, Togou MA, Khoukhi L, Ksentini A (2019) 5G-slicing-enabled scalable SDN core network: toward an ultra-low latency of autonomous driving service. 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