为了满足各学科、专业、年级学生的实践需求,高校公共计算机实验室需要在业务体量大且复杂的环境中完成场景快速部署及其管理。因此提出运用VDI(Virtual Desktop Infrastructure)与VOI(Virtual OS Infrastructure)技术融合的云桌面架构...为了满足各学科、专业、年级学生的实践需求,高校公共计算机实验室需要在业务体量大且复杂的环境中完成场景快速部署及其管理。因此提出运用VDI(Virtual Desktop Infrastructure)与VOI(Virtual OS Infrastructure)技术融合的云桌面架构,依据场景特性、现场网络、软硬件配置等设计多场景的桌面交付方案,为学生构建个性化的实践教学环境。展开更多
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 co...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.展开更多
文摘为了满足各学科、专业、年级学生的实践需求,高校公共计算机实验室需要在业务体量大且复杂的环境中完成场景快速部署及其管理。因此提出运用VDI(Virtual Desktop Infrastructure)与VOI(Virtual OS Infrastructure)技术融合的云桌面架构,依据场景特性、现场网络、软硬件配置等设计多场景的桌面交付方案,为学生构建个性化的实践教学环境。
文摘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.
