在面向多用户的动态环境中进行基于QoS的服务选择需要面临诸多挑战,而动态的服务负载就是其中之一.当前的服务选择方法难以在多用户多业务的开放环境下应对服务执行时的负载动态变化,缺乏实时感知负载的应变能力.针对这一问题,首先,提出一种基于负载等级的服务多维QoS模型(load level based multidimensional QoS,简称LLBMQoS);在此基础上,提出了一种面向多用户的负载感知的动态服务选择模型(load-aware dynamic service selection model,简称LADSSM)以实现动态负载环境下的服务优化选择.该模型采用两阶段服务选择:在组合服务规划阶段,生成候选服务队列;在组合服务执行阶段,依据当前负载状态实现服务的动态选择;最后,仿真实验的结果表明:该模型较好地适应了多用户动态环境下的服务负载变化,能够在保证用户端到端QoS需求的前提下,及时而有效地提供效用优化的服务选择方案.
Tor is pervasively used to conceal target websites that users are visiting. A de-anonymization technique against Tor, referred to as website fingerprinting attack, aims to infer the websites accessed by Tor clients by passively analyzing the patterns of encrypted traffic at the Tor client side. However, HTTP pipeline and Tor circuit multiplexing techniques can affect the accuracy of the attack by mixing the traffic that carries web objects in a single TCP connection. In this paper, we propose a novel active website fingerprinting attack by identifying and delaying the HTTP requests at the first hop Tor node. Then, we can separate the traffic that carries distinct web objects to derive a more distinguishable traffic pattern. To fulfill this goal, two algorithms based on statistical analysis and objective function optimization are proposed to construct a general packet delay scheme. We evaluate our active attack against Tor in empirical experiments and obtain the highest accuracy of 98.64%, compared with 85.95% of passive attack. We also perform experiments in the open-world scenario. When the parameter k of k-NN classifier is set to 5, then we can obtain a true positive rate of 90.96% with a false positive rate of 3.9%.
Ming YangXiaodan GuZhen LingChangxin YinJunzhou Luo
Managing massive electric power data is a typical big data application because electric power systems generate millions or billions of status,debugging,and error records every single day.To guarantee the safety and sustainability of electric power systems,massive electric power data need to be processed and analyzed quickly to make real-time decisions.Traditional solutions typically use relational databases to manage electric power data.However,relational databases cannot efficiently process and analyze massive electric power data when the data size increases significantly.In this paper,we show how electric power data can be managed by using HBase,a distributed database maintained by Apache.Our system consists of clients,HBase database,status monitors,data migration modules,and data fragmentation modules.We evaluate the performance of our system through a series of experiments.We also show how HBase’s parameters can be tuned to improve the efficiency of our system.
Jiahui JinAibo SongHuan GongYingying XueMingyang DuFang DongJunzhou Luo
随着大数据应用的发展,保障数据无中断传输的需求日益增强.针对单点或单链路失效的情况,现有的保障数据无中断传输方法存在主/备份路径的数据传输性能较低、抵御多节点/边失效能力不强等问题.为解决以上问题,提出一种可保障数据无中断传输的按边序选环的冗余树算法CSES(circle selecting by edge sorting based redundant tree algorithm for uninterrupted data delivery),可用于构建数据传输性能优化的主/备份路径,并使数据传输具有较强的抵御多节点/边失效的能力.该算法首先根据网络拓扑构建以数据源为根节点的最小传输树,以最小化主传输路径上的转发跳数;其次,为了减少备份路径的转发跳数并提高数据传输抵御多节点/边失效的能力,对拓扑中不在最小传输树上的边进行排序,将树上根节点到边上2个端点的路径上节点数量之和较小的边排在前列.随后按序将边添加到最小传输树上以构建冗余环,并基于冗余环生成冗余枝添加到最小传输树上,最终形成以数据源为根节点的冗余树.实验结果表明,相比于其他冗余树算法,基于CSES算法构建的冗余树所生成的主/备份路径的转发跳数更少且抵御多节点/边失效的能力更强.
Cloud data centers, such as Amazon EC2, host myriad big data applications using Virtual Machines(VMs). As these applications are communication-intensive, optimizing network transfer between VMs is critical to the performance of these applications and network utilization of data centers. Previous studies have addressed this issue by scheduling network flows with coflow semantics or optimizing VM placement with traffic considerations.However, coflow scheduling and VM placement have been conducted orthogonally. In fact, these two mechanisms are mutually dependent, and optimizing these two complementary degrees of freedom independently turns out to be suboptimal. In this paper, we present VirtCO, a practical framework that jointly schedules coflows and places VMs ahead of VM launch to optimize the overall performance of data center applications. We model the joint coflow scheduling and VM placement optimization problem, and propose effective heuristics for solving it. We further implement VirtCO with OpenStack and deploy it in a testbed environment. Extensive evaluation of real-world traces shows that compared with state-of-the-art solutions, VirtCO greatly reduces the average coflow completion time by up to 36.5%. This new framework is also compatible with and readily deployable within existing data center architectures.
