基于资源调用链的Web应用服务器监视诊断框架的设计与实现

随着网络技术的发展，采用多层架构的Web应用逐渐成为重要的软件发展趋势，Web应用服务器通过简化Web应用的开发管理，已经成为多层Web应用的主流支撑平台。然而由于Web应用服务器对Web应用采用透明式服务，因此二者之间的交互难以理解，难以监视和诊断Web应用的性能瓶颈。如何通过Web应用服务器的监视与诊断快速定位多层Web应用中的性能瓶颈是非常值得研究的问题。 本论文在分析现有监视与诊断技术的基础上，首先将多层Web应用中组件、服务间的复杂交互关系抽象为资源调用链，分解多层Web应用的性能瓶颈范围；其次，以比较稳定的资源服务时间代替传统的性能度量作为监视对象，并提出了一种基于服务时间标记的性能异常诊断方法；最后，基于上述工作设计实现了一种Web应用服务器性能监视与诊断框架。 目前该框架已经集成到Web应用服务器OnceAS中，系统测试结果表明，该框架可以有效地发现由于负载变化和业务逻辑设计引起的性能瓶颈

基于服务时间标记的多层Web应用性能异常侦测方法

提出一种基于服务时间标记的性能异常侦测方法,标记相对稳定的请求服务时间,并通过分析该时间变化来定位性能瓶颈以及分析可能的原因。该方法已经实现在一个典型的多层Web应用系统的支撑平台中,TPC-W测试基准的测试结果验证了其有效性

performance isolation approach for multi-tenancy web applications

提出一种面向应用级共享的多租户Web应用性能隔离方法.首先建立基于事务处理链的应用级资源管理模型,基于信号量机制给出了模型的并发控制算法,支持事务级、分阶段的多租户Web应用资源管理,并在一次事务处理的各阶段实现线程复用,避免因修改交互协议而产生的再工程代价.基于上述工作,给出多租户性能隔离算法及策略,并利用TPC-W电子商务应用验证方法的系统开销及有效性.实验结果表明,该方法可以有效降低租户资源侵占行为的影响,并避免系统过载.国家自然科学基金(批准号:61100068)|国家高技术研究发展计划(批准号:2012AA011204)|国家重点基础研究发展计划(批准号:2009CB320704)|武汉大学软件工程国家重点实验室开放基金(批准号:SKLSE20100821)资助项目Multi-tenancy enables the sharing of resources and costs across a large pool of users, thus allowing for centralization of infrastructure in locations with lower costs. Performance isolation is a key requirement for multi-tenant Web application hosting environments. This paper proposes a performance isolation approach for application-level sharing multi-tenancy Web applications. A fine-grained application-level resource management model based on the transaction-processing chain is first defined, as well as its concurrency control algorithm based on the semaphore mechanism. This model helps to manage resources usage in a transaction-level and phase-targeted way. It reuses one thread within a transaction process, and therefore, avoids the reengineering cost generated by system reconstruction. The end result is an approach that extends the current Web application hosting environments to multi-tenancy aware hosting environments. A detailed set of experiments were performed under different settings for changing workloads in the controlled TPC-W application to evaluate the effectiveness and performance overhead of our approach. Compared to the traditional coarse-grained strategy, experimental results show that the fine-grained strategy based on our approach is more efficient and agile in avoiding performance interference between multiple hosted tenants and CPU overload. Our results also show that the performance overhead of our approach is small

Filter based resource demand estimation for on-demand provision

随着按需供给资源使用模式的推广，软件的资源需求已成为资源优化控制的重要属性。监测和估算是目前常用的资源消耗获取方法，但监测工具难以在运行时准确度量短任务的资源需求，回归分析方法又因受到多元共线性和不确定性因素的影响，导致其取值精度下降。本文提出了一种基于Kalman滤波的资源需求估算方法。该方法建立了可度量属性集与不可度量的资源需求间的关联，并利用滤波过滤度量过程中的噪声，达到降低估算误差的目的。基准测试的结果表明，通过合理的设置滤波参数，本方法能够快速逼近真实值，且平均误差小于8%。 As the development of demand resource provision, resource demands of software is becoming one of the most important attributes of resource management. Measurement and estimation are widely used in fetching the demands. However, it is hard to measure the short job0s resource demands by current measurement tools, and the regression methods suffer from the well-studied problem of multicollinearity. Therefore, the estimated results are not confident. In order to improve the estimation precision, we propose a Kalman filter based approach, which can predict the unobservable attribute by observable attributes, and filter the noise existing in the measurement. At last, we test our approach with a benchmark and compare the relative errors, which can demonstrate that with the reasonable parameters, our approach can get close to the real demands quickly, and get the estimated value with the mean error less than 8%.As the development of demand resource provision, resource demands of software is becoming one of the most important attributes of resource management. Measurement and estimation are widely used in fetching the demands. However, it is hard to measure the short job's resource demands by current measurement tools, and the regression methods suffer from the well-studied problem of multicollinearity. Therefore, the estimated results are not confident. In order to improve the estimation precision, we propose a Kalman filter based approach, which can predict the unobservable attribute by observable attributes, and filter the noise existing in the measurement. At last, we test our approach with a benchmark and compare the relative errors, which can demonstrate that with the reasonable parameters, our approach can get close to the real demands quickly, and get the estimated value with the mean error less than 8%. Copyright © 2014 Acta Automatica Sinica. All rights reserved

A ROLE-BASED METHOD FOR SOLVING SINGLE POINT OF FAILURE IN VIRTUAL MACHINE RESOURCE POOL

现有的虚拟机池化管理普遍采用的是master/slave模式，存在单点失效问题。针对此问题，设计并实现一种基于角色的虚拟机池单点失效处理方法。该方法采用角色划分和选取机制自动指定主备节点，并基于序列法解决了自动选取机制中存在多个master节点冲突问题。实验表明，该方法能够实现master节点对用户透明的失效恢复。 Themaster/slavemodeisthemostpervasivemanagementpolicyforvirtualmachineresourcepoolatpresent,buthasthe problem of the single point of failure.In light of this,we design and implement a role-based method to solve the single point of failure for virtual machine resource pool.In this method,the master or backup node can be automatically activated according to the role appointment and the selection mechanism.Furthermore,a sequence method is proposed to solve the conflicts among multiple master nodes in automatic selection mechanism.Experiments show that the method can realise the recovery against the failure of transparency of master node on users.The master/slave mode is the most pervasive management policy for virtual machine resource pool at present, but has the problem of the single point of failure. In light of this,we design and implement a role-based method to solve the single point of failure for virtual machine resource pool. In this method, the master or backup node can be automatically activated according to the role appointment and the selection mechanism. Furthermore, a sequence method is proposed to solve the conflicts among multiple master nodes in automatic selection mechanism. Experiments show that the method can realise the recovery against the failure of transparency of master node on users

从红土镍矿镍铁渣中分离浸取镍铬工艺

将镍铁渣破碎、球磨后磁选富集Ni于精矿中,富集Cr于尾矿中.磁选后Ni从0.26%富集至2.57%(ω),Cr从4.55%富集至4.61%(ω).考察了H_2SO_4常压酸浸精矿时Ni的浸出规律.结果表明,在酸浸温度110℃、酸浓度220g/L、酸浸时间2 h、液固质量比5的优化酸浸条件下,Ni浸出率为91.5%.在80~120℃内,Ni浸出反应活化能为19.6kJ/mol.Ni浸出反应主要受扩散控制.用Na_2CO_3碱熔焙烧尾矿,在温度1000℃、Na_2CO_3/渣质量比0.65、时间1 h、镍铁渣尾矿粒度<74μm的优化条件下,Cr浸出率为94.1%

蛇纹石型红土矿常压酸浸实验研究

主要探讨了常压下盐酸对蛇纹石型红土镍矿进行浸出的工艺条件。考察了酸矿比、液固比、反应温度、反应时间等对蛇纹石型红土镍矿浸出的影响。通过实验得出最佳工艺条件:酸矿比为2.5∶1、液固比为5∶1、反应时间为0.5 h、反应温度为100℃。在此条件下镍、钴、铁浸出率分别为100%、100%和90%

从红土镍矿镍铁渣中分离浸取镍铬工艺

将镍铁渣破碎、球磨后磁选富集Ni于精矿中,富集Cr于尾矿中.磁选后Ni从0.26%富集至2.57%(ω),Cr从4.55%富集至4.61%(ω).考察了H_2SO_4常压酸浸精矿时Ni的浸出规律.结果表明,在酸浸温度110℃、酸浓度220g/L、酸浸时间2 h、液固质量比5的优化酸浸条件下,Ni浸出率为91.5%.在80~120℃内,Ni浸出反应活化能为19.6kJ/mol.Ni浸出反应主要受扩散控制.用Na_2CO_3碱熔焙烧尾矿,在温度1000℃、Na_2CO_3/渣质量比0.65、时间1 h、镍铁渣尾矿粒度<74μm的优化条件下,Cr浸出率为94.1%

DESIGN AND IMPLEMENTATION OF AN OSGi-ORIENTED SOFTWARE COMPONENT MONITORING APPROACH

面向 OSGi 框架的构件化软件开发方法被广泛应用。现有的 OSGi 构件监控方法仅能够监控动态服务调用所造成的资源消耗，未能考虑静态包引用的情况。针对该问题，提出一种面向方法的 OSGi 构件监控方法。该方法首先对代码进行分析，建立方法与构件间的映射关系，在方法前后插入监控逻辑以标记构件的边界；而后，跟踪线程的跨边界执行，将线程在方法中占用的 CPU 和内存计入对应的构件；同时，通过记录线程在不同构件间的转移，监测构件间的动态调用。实验结果表明，该方法能够以较低的开销准确监控 OSGi 中构件的 CPU 和内存资源消耗，以及构件间的交互行为。 OSGi framework-oriented componentised software development method is in wide use.Existing OSGi components monitoring methods can only monitor resources consumption caused by dynamic service call,but regardless the static packages importing situation.To address this issue,we propose a method-oriented OSGi component monitoring approach.First of all,this approach sets up the mapping relationship between methods and components by analysing the code,and inserts monitoring logic before and after the methods to mark the boundary of components.After that,the approach tracks the cross-border execution of each thread,adds the CPU time consumed and the memory occupied in methods by the threads to corresponding components.Meanwhile,by recording each thread transfer between components, it monitors the dynamic call between the components.Experimental results demonstrate that this approach is able to accurately monitor CPU and memory consumption of OSGi components and the interaction between the components with lower overhead.OSGi framework-oriented componentised software development method is in wide use. Existing OSGi components monitoring methods can only monitor resources consumption caused by dynamic service call, but regardless the static packages importing situation. To address this issue, we propose a method-oriented OSGi component monitoring approach. First of all, this approach sets up the mapping relationship between methods and components by analysing the code, and inserts monitoring logic before and after the methods to mark the boundary of components. After that, the approach tracks the cross-border execution of each thread, adds the CPU time consumed and the memory occupied in methods by the threads to corresponding components. Meanwhile, by recording each thread transfer between components, it monitors the dynamic call between the components. Experimental results demonstrate that this approach is able to accurately monitor CPU and memory consumption of OSGi components and the interaction between the components with lower overhead

从红土镍矿镍铁渣中分离浸取镍铬工艺

将镍铁渣破碎、球磨后磁选富集Ni于精矿中,富集Cr于尾矿中.磁选后Ni从0.26%富集至2.57%(ω),Cr从4.55%富集至4.61%(ω).考察了H_2SO_4常压酸浸精矿时Ni的浸出规律.结果表明,在酸浸温度110℃、酸浓度220g/L、酸浸时间2 h、液固质量比5的优化酸浸条件下,Ni浸出率为91.5%.在80~120℃内,Ni浸出反应活化能为19.6kJ/mol.Ni浸出反应主要受扩散控制.用Na_2CO_3碱熔焙烧尾矿,在温度1000℃、Na_2CO_3/渣质量比0.65、时间1 h、镍铁渣尾矿粒度<74μm的优化条件下,Cr浸出率为94.1%