John Violos, Stylianos Tsanakas, T. Theodoropoulos, Aris Leivadeas, K. Tserpes, T. Varvarigou
{"title":"边缘资源使用预测的超调GRU神经网络","authors":"John Violos, Stylianos Tsanakas, T. Theodoropoulos, Aris Leivadeas, K. Tserpes, T. Varvarigou","doi":"10.1109/ISCC53001.2021.9631548","DOIUrl":null,"url":null,"abstract":"The proliferation of Internet of Things (IoT) and edge devices constitute important an efficient orchestration of the edge computing infrastructures, calling the providers to rethink their decision making methods. The resource usage prediction can be a prominent source of information for adaptive resource allocation and task offloading. In this research, we propose a Gated Recurrent Neural Network multi-output regression model that leverage time series resource usage metrics. The edge computing infrastructures are characterized as dynamical and heterogeneous environments. This motivated us to propose the innovative Hybrid Bayesian Evolutionary Strategy (HBES) algorithm for automated adaptation of the resource usage models in order to to enhance the generality of our approach. The proposed resource usage prediction mechanism has been experimentally evaluated and compared with other state of the art methods with significant improvements in terms of RMSE and MAE.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Hypertuming GRU Neural Networks for Edge Resource Usage Prediction\",\"authors\":\"John Violos, Stylianos Tsanakas, T. Theodoropoulos, Aris Leivadeas, K. Tserpes, T. Varvarigou\",\"doi\":\"10.1109/ISCC53001.2021.9631548\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The proliferation of Internet of Things (IoT) and edge devices constitute important an efficient orchestration of the edge computing infrastructures, calling the providers to rethink their decision making methods. The resource usage prediction can be a prominent source of information for adaptive resource allocation and task offloading. In this research, we propose a Gated Recurrent Neural Network multi-output regression model that leverage time series resource usage metrics. The edge computing infrastructures are characterized as dynamical and heterogeneous environments. This motivated us to propose the innovative Hybrid Bayesian Evolutionary Strategy (HBES) algorithm for automated adaptation of the resource usage models in order to to enhance the generality of our approach. The proposed resource usage prediction mechanism has been experimentally evaluated and compared with other state of the art methods with significant improvements in terms of RMSE and MAE.\",\"PeriodicalId\":270786,\"journal\":{\"name\":\"2021 IEEE Symposium on Computers and Communications (ISCC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE Symposium on Computers and Communications (ISCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCC53001.2021.9631548\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Symposium on Computers and Communications (ISCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCC53001.2021.9631548","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hypertuming GRU Neural Networks for Edge Resource Usage Prediction
The proliferation of Internet of Things (IoT) and edge devices constitute important an efficient orchestration of the edge computing infrastructures, calling the providers to rethink their decision making methods. The resource usage prediction can be a prominent source of information for adaptive resource allocation and task offloading. In this research, we propose a Gated Recurrent Neural Network multi-output regression model that leverage time series resource usage metrics. The edge computing infrastructures are characterized as dynamical and heterogeneous environments. This motivated us to propose the innovative Hybrid Bayesian Evolutionary Strategy (HBES) algorithm for automated adaptation of the resource usage models in order to to enhance the generality of our approach. The proposed resource usage prediction mechanism has been experimentally evaluated and compared with other state of the art methods with significant improvements in terms of RMSE and MAE.