Adaptive VDI Session Placement via User Logoff Prediction  

作　　者：Wenping Fan Puhui Meng Yu Tian Min-Ling Zhang Yao Zhang 

机构地区：[1]School of Computer Science and Engineering,Southeast University,Nanjing,210096,China [2]VMware Information Technology(China)Limited,Beijing,100190,China [3]Key Laboratory of Computer Network and Information Integration(Southeast University),Ministry of Education,Nanjing,210096,China

出　　处：《Machine Intelligence Research》2025年第1期189-200,共12页机器智能研究(英文版)

摘　　要：After the global pandemic,DaaS(desktop as a service)has become the first choice of many companies’remote working solution.As the desktops are usually deployed in the public cloud when using DaaS,customers are more cost-sensitive which boosts the requirement of proactive power management.Prior researches in this area focus on virtual desktop infrastructure(VDI)session logon behavior modeling,but for the remote desktop service host(RDSH)-shared desktop pools,logoff optimization is also important.Existing systems place sessions by round-robin or in a pre-defined order without considering their logoff time.However,these approaches usually suffer from the situation that few left sessions prevent RDSH servers from being powered-off which introduces cost waste.In this paper,we propose session placement via adaptive user logoff prediction(SODA),an innovative compound model towards proactive RDSH session placement.Specifically,an ensemble machine learning model that can predict session logoff time is combined with a statistical session placement bucket model to place RDSH sessions with similar logoff time in a more centralized manner on RDSH hosts.Consequently,the infrastructure cost-saving can be improved by reducing the resource waste introduced by those RDSH hosts with very few hanging sessions left for a long time.Experiments on real RDSH pool data demonstrate the effectiveness of the proposed proactive session placement approach against existing static placement techniques.

关 键 词：Virtual desktop infrastructure(VDI)resource management remote desktop service logoff prediction adaptive modeling session placement 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]