Network management systems based on mobile agents are efficiently a better alternative than typical client/server based architectures. Centralized management models like SNMP or CMIP based management models suffer from scalability and flexibility issues which are addressed to great extent by flat bed or static mid-level manager models based on mobile agents, yet the use of mobile agents to distribute and delegate management tasks for above stated agent-based management frameworks like initial flat bed models and static mid-level managers cannot efficiently meet the demands of current networks which are growing in size and complexity. In view of the above mentioned limitations, we proposed a domain partitioned network management model based-on mobile agent & Element Management Systems in order to minimize management data flow to a centralized server. Intelligent agent allocated to specific EMS performs local network management and reports the results to the superior manager and finally the global manager performs global network management using those submitted management results. Experimental results of various scenarios of the proposed model have been presented to support the arguments given in favor of the prototype system based on mobile agents.
Resource-efficient Generative Mobile Edge Networks in 6G Era: Fundamentals, Framework and Case Study
Guangdong University of Technology（广东工业大学）；Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing（粤港澳智慧离散制造联合实验室）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Nanyang Technological University（南洋理工大学）；Sungkyunkwan University（成均馆大学）
A Personalized Preference Learning Framework for Caching in Mobile Networks
EURECOM（欧洲通信技术学院）；Korea University（高丽大学）；Yonsei University（延世大学）；Department of Communication System, EURECOM（欧洲通信技术学院通信系统系）；School of Electrical Engineering, Korea University（高丽大学电气工程学院）；Department of Electrical and Electronic Engineering, Yonsei University（延世大学电气电子工程系）；Department of Computational Science and Engineering, Yonsei University（延世大学计算科学与工程系）
CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networks
State Key Laboratory of Networking and Switching Technology（网络与交换技术国家重点实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；Singapore University of Technology and Design（新加坡科技设计大学）；School of Electronics, Electrical Engineering and Computer Science（电子、电气与计算机科学学院）；Queen’s University Belfast（贝尔法斯特女王大学）；Dept. of Electrical Engineering & Computer Science（电气与计算机科学系）；Lassonde School of Engineering（拉索nde 工程学院）；York University（约克大学）；College of Computing and Data Science（计算与数据科学学院）；Nanyang Technological University（南洋理工大学）
