.png){kind=icon}
返回
Robust Distributed Server Selection Model Against Delay Uncertainty
.png){kind=icon}
PRE
AI
DOI：10.1109/TNSM.2025.3582933.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In real-time applications under wide-area networks, providing a demanded quality of service for end users is issue. Recent studies adopt distributed processing for server selection problems to reduce data synchronization delay and total interaction delay, assuming that link delays over the distributed system are exactly known. No study has addressed the problem such a distributed server selection in properly handling the delay uncertainty. This paper proposes a robust optimization model for the distributed server selection problem against the delay uncertainty. We handle the delay uncertainty of user-server server-server links by defining two `-ellipsoidal uncertainty sets. The proposed model determines allocated servers for multiple users to minimize the weighted sum of data synchronization delay and total interaction delay over the distributed system. We formulate the proposed model as a mixed integer second order cone programming problem. We prove that the distributed server selection problem with uncertain delays is NP-complete. We compare the proposed model with baseline models, focusing on delay uncertainty and distributed processing. The numerical results show that the proposed model can achieve a lower objective value than the baseline models, indicating the benefit utilizing `-ellipsoidal uncertainty sets to handle delay uncertainty.
Keyword:
Server selection problem
distributed processing
robust optimization
robust optimization
delay uncertainty
delay uncertainty
dual theory
dual theory
dual theory
期刊
IF:
5.4
论文数:
590
被引数:
9.2K
机构
引用论文
分享收藏分享收藏A Real-Time Delay-Sensitive Communication Approach Based on Distributed Processing.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Service Mapping and Scheduling With Uncertain Processing Time in Network Function Virtualization
PREAI
分享收藏Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning苏, H.; 郑Y.; 丁J.; 金D.; 李Y. 基于知识感知强化学习的大规模城市设施选址. 见：第32届ACM国际高级地理信息系统会议（SIGSPATIAL '24）论文集, 亚特兰大, GA, 美国, 2024年10月29日-11月1日; ACM: 亚特兰大, GA, 美国, 2024; 第1-10页. [Google Scholar] [CrossRef]Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems
Proceedings of the 32nd ACM International Conference on Advances in Geographic Information SystemsIF0
PREAI
分享收藏
查看更多内容