Optimal and Effective Resource Management in Edge Computing

作     者：Darpan Majumder S.Mohan Kumar 

作者机构：Department of Information Science and EngineeringNew Horizon College of Engineering Research CentreVisvesvaraya Technological University(VTU)BangaloreIndia Department of CSENagarjuna College of Engineering and TechnologyBangaloreIndia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第44卷第2期

页      面：1201-1217页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Federated learning machine learning edge computing resource management 

摘      要：Edge computing is a cloud computing extension where physical compu-ters are installed closer to the device to minimize *** task of edge data cen-ters is to include a growing abundance of applications with a small capability in comparison to conventional data *** this framework,Federated Learning was suggested to offer distributed data training strategies by the coordination of many mobile devices for the training of a popular Artificial Intelligence(AI)model without actually revealing the underlying data,which is significantly enhanced in terms of *** learning(FL)is a recently developed decentralized profound learning methodology,where customers train their localized neural network models independently using private data,and then combine a global model on the core server *** models on the edge server use very little time since the edge server is highly *** the amount of time it takes to download data from smartphone users on the edge server has a significant impact on the time it takes to complete a single cycle of FL operations.A machine learning strategic planning system that uses FL in conjunction to minimise model training time and total time utilisation,while recognising mobile appliance energy restrictions,is the focus of this *** further speed up integration and reduce the amount of data,it implements an optimization agent for the establishment of optimal aggregation policy and asylum architecture with several employees’shared *** main solutions and lessons learnt along with the prospects are *** show that our method is superior in terms of the effective and elastic use of resources.