Deep learning based recommendation methods, such as the recurrent neural network based recommendation method(RNNRec) and the gated recurrent unit(GRU) based recommendation method(GRURec), are proposed to solve the pro...Deep learning based recommendation methods, such as the recurrent neural network based recommendation method(RNNRec) and the gated recurrent unit(GRU) based recommendation method(GRURec), are proposed to solve the problem of time heterogeneous feedback recommendation. These methods out-perform several state-of-the-art methods. However, in RNNRec and GRURec, action vectors and item vectors are shared among users. The different meanings of the same action for different users are not considered. Similarly, different user preference for the same item is also ignored. To address this problem, the models of RNNRec and GRURec are modified in this paper. In the proposed methods, action vectors and item vectors are transformed into the user space for each user firstly, and then the transformed vectors are fed into the original neural networks of RNNRec and GRURec. The transformed action vectors and item vectors represent the user specified meaning of actions and the preference for items, which makes the proposed method obtain more accurate recommendation results. The experimental results on two real-life datasets indicate that the proposed method outperforms RNNRec and GRURec as well as other state-of-the-art approaches in most cases.展开更多
近年来存储行业经历了巨大的变革,以固态硬盘(solid state drive, SSD)为代表的半导体存储设备迅猛发展,在性能上显著超越了通过磁头移动寻址的机械硬盘(hard disk drive, HDD).目前支持SSD的2种协议主要包括非易失性内存主机控制器接...近年来存储行业经历了巨大的变革,以固态硬盘(solid state drive, SSD)为代表的半导体存储设备迅猛发展,在性能上显著超越了通过磁头移动寻址的机械硬盘(hard disk drive, HDD).目前支持SSD的2种协议主要包括非易失性内存主机控制器接口规范(nonvolatile memory express, NVMe)协议与串行SCSI(serial attached small computer system interface, SAS)协议,即SAS. NVMe是专为SSD设计的高性能存储协议,能够很大限度地发挥SSD的性能;而SAS协议则充分考虑数据中心的需求,在提供高可靠性与高可扩展性的同时,兼顾了系统性能与成本的平衡.相对于日益增速的存储介质,针对慢速存储设备所设计的软件栈在一次I/O过程中所耗费的时间开销愈发显著.针对该问题学界及工业界都相继提出了众多解决方案,例如Intel提出的高性能存储开发包(storage performance development kit, SPDK)通过将设备驱动实现在用户空间,并采用轮询感知I/O完成等方式大幅度缩短了NVMe SSD对应用程序的响应时间,极大地提升了整个系统的整体性能.然而之前的研究工作针对SAS SSD存储软件栈的优化非常有限,为此在用户空间实现了针对SAS SSD的软件栈优化.实验结果表明,该优化能够有效缩短存储设备对应用程序的响应时间,提高应用对存储设备的访存效率.此外,为了准确评估I/O栈中存储设备的时间开销,硬件性能测试工具HwPerfIO被提出,能够消除大部分软件开销的影响以测得更加准确的存储设备性能.展开更多
面向企业网或校园网的移动办公与存储的网盘系统有着广泛的市场需求,传统的网盘技术在性能、用户共享、安全性、可扩展性等方面存在诸多缺陷。针对这些不足,本文提出了一种基于云存储的高性能网盘系统架构:采用分布式文件系统MooseFS实...面向企业网或校园网的移动办公与存储的网盘系统有着广泛的市场需求,传统的网盘技术在性能、用户共享、安全性、可扩展性等方面存在诸多缺陷。针对这些不足,本文提出了一种基于云存储的高性能网盘系统架构:采用分布式文件系统MooseFS实现用户数据存储与访问的集群架构;在安全性方面,结合SAMBA实现用户权限管理,用户数据存储支持128 bit AES加密,SSH保证了传输链路的安全;最后,结合用户的实际需求,提供基于Web的访问方式以及客户端的同步盘模式。结果表明,系统在性能、安全性、可扩展性等多方面具有显著优势。展开更多
基金supported by the National Natural Science Foundation of China(61403350)。
文摘Deep learning based recommendation methods, such as the recurrent neural network based recommendation method(RNNRec) and the gated recurrent unit(GRU) based recommendation method(GRURec), are proposed to solve the problem of time heterogeneous feedback recommendation. These methods out-perform several state-of-the-art methods. However, in RNNRec and GRURec, action vectors and item vectors are shared among users. The different meanings of the same action for different users are not considered. Similarly, different user preference for the same item is also ignored. To address this problem, the models of RNNRec and GRURec are modified in this paper. In the proposed methods, action vectors and item vectors are transformed into the user space for each user firstly, and then the transformed vectors are fed into the original neural networks of RNNRec and GRURec. The transformed action vectors and item vectors represent the user specified meaning of actions and the preference for items, which makes the proposed method obtain more accurate recommendation results. The experimental results on two real-life datasets indicate that the proposed method outperforms RNNRec and GRURec as well as other state-of-the-art approaches in most cases.
文摘近年来存储行业经历了巨大的变革,以固态硬盘(solid state drive, SSD)为代表的半导体存储设备迅猛发展,在性能上显著超越了通过磁头移动寻址的机械硬盘(hard disk drive, HDD).目前支持SSD的2种协议主要包括非易失性内存主机控制器接口规范(nonvolatile memory express, NVMe)协议与串行SCSI(serial attached small computer system interface, SAS)协议,即SAS. NVMe是专为SSD设计的高性能存储协议,能够很大限度地发挥SSD的性能;而SAS协议则充分考虑数据中心的需求,在提供高可靠性与高可扩展性的同时,兼顾了系统性能与成本的平衡.相对于日益增速的存储介质,针对慢速存储设备所设计的软件栈在一次I/O过程中所耗费的时间开销愈发显著.针对该问题学界及工业界都相继提出了众多解决方案,例如Intel提出的高性能存储开发包(storage performance development kit, SPDK)通过将设备驱动实现在用户空间,并采用轮询感知I/O完成等方式大幅度缩短了NVMe SSD对应用程序的响应时间,极大地提升了整个系统的整体性能.然而之前的研究工作针对SAS SSD存储软件栈的优化非常有限,为此在用户空间实现了针对SAS SSD的软件栈优化.实验结果表明,该优化能够有效缩短存储设备对应用程序的响应时间,提高应用对存储设备的访存效率.此外,为了准确评估I/O栈中存储设备的时间开销,硬件性能测试工具HwPerfIO被提出,能够消除大部分软件开销的影响以测得更加准确的存储设备性能.
文摘面向企业网或校园网的移动办公与存储的网盘系统有着广泛的市场需求,传统的网盘技术在性能、用户共享、安全性、可扩展性等方面存在诸多缺陷。针对这些不足,本文提出了一种基于云存储的高性能网盘系统架构:采用分布式文件系统MooseFS实现用户数据存储与访问的集群架构;在安全性方面,结合SAMBA实现用户权限管理,用户数据存储支持128 bit AES加密,SSH保证了传输链路的安全;最后,结合用户的实际需求,提供基于Web的访问方式以及客户端的同步盘模式。结果表明,系统在性能、安全性、可扩展性等多方面具有显著优势。
