一种基于用户需求的基准测试方法

科学数据管理在高性能计算中发挥着越来越重要的作用,作为构建科学数据管理的关键支撑部分,数据库应用的性能和对它的测试评估显得尤为重要.科学数据管理系统是一种新的数据库应用,目前尚没有一个针对科学数据管理应用提出的基准测试程序.文中对数据库性能测试技术和基准测试标准进行了研究,针对面向科学数据管理的数据库应用的特点,采用Set Query Benchmark对支持平台的性能作了测试.最后基于此测试案例研究,提出了一个对Set Query Benchmark改进的基于用户需求的基准测试方法--用户可定制的基准测试方法(User-defined Benchmark),对面向科学数据管理的数据库应用可以提供较好的性能度量.     

基于角色的ORACLE用户权限管理设计

基于角色的用户权限管理是目前应用最广泛的用户管理方法,ORACLE 10g提供的基于角色用户管理系统为系统的安全保障提供了基础。介绍了基于角色用户管理的原理,分析了ORACLE提供的角色和权限管理机制,并以一个图书管理系统的数据库为例,进行了角色、用户和权限的设计。测试结果表明,所设计的用户系统提供了用户访问系统的最小权限,能够有效防止用户越权访问数据。     

POI用户模型的重构与优化

Apache POI中的用户模型是目前用于处理Excel数据的最为广泛的应用技术,但用户模型存在许多明显的弊端。本文将以一个学生档案管理系统为例,对用户模型中存在的问题以及产生问题的原因进行分析,并针对这些问题,借用面向过程的设计思想对用户模型进行优化和改进。使用不同规模的数据量对改进前后的用户模型进行测试,并对测试结果进行比较和分析,最终在一定程度上解决了用户模型的弊端,提升了用户模型的性能。     

TPC-W性能测试系统的设计与实现

本文根据TPC-W测试基准的要求，设计并实现了一个基于TPC-W的数据库性能测试系统，分析了其系统设计方案，提出了关键技术的解决方法。该系统模拟真实的用户对基于DBMS应用的网上购书系统进行大用户量的并发访问测试，对比分析其测试结果和影响因素，为国产数据库DM4与国外著名数据库系统的电子商务应用比较提供了评测结果。     

高校科研管理系统的Web测试设计与实现

首先介绍了目前Web测试的现状、Web测试的必要性、Web测试的主要内容。接下来,结合高校科研管理系统的测试,分别从用户界面、功能、性能、安全性等方面对系统进行了较全面的Web测试设计,并完成了全部测试,对其他Web应用系统的测试实践具有一定的借鉴意义。     

Web应用系统的分布式负载测试框架研究

Web应用系统的性能包括服务响应时间、资源占用以及系统的稳定性等,Web应用系统在发布之前要进行功能测试、页面测试、性能测试等,其中负载测试是Web应用系统性能测试的一个重要方面。随着Web应用程序的使用越来越广泛,对其性能进行测试也变得越来越重要,而采用自动化的测试工具极大地方便了Web系统的测试。虽然目前存在多种自动化测试工具,但都还不能用于大负载测试(百万级虚拟用户访问量)。本文首先介绍了负载测试的性质及作用,然后以Apache的开源Web测试工具JMeter为基础,通过数据库技术对其分布式的Web负载测试框架进行优化,使得测试工具能真正达到百万级的负载测试。本文着重讨论了分布式的测试框架的实现问题。     

Benchmark性能测试综述

基准(Benchmark)测试是一种应用广泛、内容繁杂的测试技术,也是目前最主要的信息系统性能测试技术。文章对Benchmark测试的规范和测试方法做了归纳总结,给出了选用Benchmark测试的建议和开发Benchmark测试规范需要解决的问题。最后介绍了有代表性的Benchmark测试规范和程序集。     

云环境下性能测试研究与应用

性能测试是一种昂贵且耗时的测试活动,是评估系统性能至关重要的手段。在传统测试环境下进行性能测试存在冗余测试次数增多、性能瓶颈定位困难等问题,人们开始思考和研究如何利用当前流行的云计算技术来提高性能测试的效率。在本文中,研究了云环境下性能测试技术的最新研究成果,设计并实现了基于CloudStack云平台的自动化性能测试原型系统。实验结果表明,在云环境下的性能测试,能显著简化用户的测试工作,并具有与传统测试环境下相一致的性能测试结果。     

磁盘阵列性能测试工具研究

目前国内外有许多优秀的性能评测基准如TPC、SPC、IOMeter、IOZONE等,但它们在应用环境和功能上存在一些限制。为了解决这个问题,设计并实现了一个基于Linux分布式系统的磁盘阵列性能测试工具DAMeter,并与业内广泛使用的Windows下磁盘阵列性能测试基准IOMeter进行对比实验,证明了DAMeter工具测试结果的准确性和可靠性。并介绍了如何使用DAMeter产生不同负载进行磁盘阵列性能测试。     

云存储性能评测技术研究

随着网络和存储技术的发展, 提供可共享和便捷使用云存储服务已成为互联网的一种重大新型应用模式和发展趋势。但由于系统规模巨大且存储内容多变, 传统的性能评价及测试方法已不适用于云存储平台。提供云存储系统性能评测的思路, 建立面向不同用户、适用于不同阶段的多指标三维评测体系, 采用云测试云的方案测试不同物理位置上的节点访问性能。     

CloudRank-D: benchmarking and ranking cloud computing systems for data processing applications

With the explosive growth of information, more and more organizations are deploying private cloud systems or renting public cloud systems to process big data. However, there is no existing benchmark suite for evaluating cloud performance on the whole system level. To the best of our knowledge, this paper proposes the first benchmark suite CloudRank-D to benchmark and rank cloud computing systems that are shared for running big data applications. We analyze the limitations of previous metrics, e.g., floating point operations, for evaluating a cloud computing system, and propose two simple metrics: data processed per second and data processed per Joule as two complementary metrics for evaluating cloud computing systems. We detail the design of CloudRank-D that considers representative applications, diversity of data characteristics, and dynamic behaviors of both applications and system software platforms. Through experiments, we demonstrate the advantages of our proposed metrics. In several case studies, we evaluate two small-scale deployments of cloud computing systems using CloudRank-D.     

A mixed transaction processing and operational reporting benchmark

The importance of reporting is ever increasing in today’s fast-paced market environments and the availability of up-to-date information for reporting has become indispensable. Current reporting systems are separated from the online transaction processing systems (OLTP) with periodic updates pushed in. A pre-defined and aggregated subset of the OLTP data, however, does not provide the flexibility, detail, and timeliness needed for today’s operational reporting. As technology advances, this separation has to be re-evaluated and means to study and evaluate new trends in data storage management have to be provided. This article proposes a benchmark for combined OLTP and operational reporting, providing means to evaluate the performance of enterprise data management systems for mixed workloads of OLTP and operational reporting queries. Such systems offer up-to-date information and the flexibility of the entire data set for reporting. We describe how the benchmark provokes the conflicts that are the reason for separating the two workloads on different systems. In this article, we introduce the concepts, logical data schema, transactions and queries of the benchmark, which are entirely based on the original data sets and real workloads of existing, globally operating enterprises.     

A flexible framework for accurate simulation of cloud in-memory data stores

In-memory (transactional) data stores, also referred to as data grids, are recognized as a first-class data management technology for cloud platforms, thanks to their ability to match the elasticity requirements imposed by the pay-as-you-go cost model. On the other hand, determining how performance and reliability/availability of these systems vary as a function of configuration parameters, such as the amount of cache servers to be deployed, and the degree of in-memory replication of slices of data, is far from being a trivial task. Yet, it is an essential aspect of the provisioning process of cloud platforms, given that it has an impact on the amount of cloud resources that are planned for usage. To cope with the issue of predicting/analysing the behavior of different configurations of cloud in-memory data stores, in this article we present a flexible simulation framework offering skeleton simulation models that can be easily specialized in order to capture the dynamics of diverse data grid systems, such as those related to the specific (distributed) protocol used to provide data consistency and/or transactional guarantees. Besides its flexibility, another peculiar aspect of the framework lies in that it integrates simulation and machine-learning (black-box) techniques, the latter being used to capture the dynamics of the data-exchange layer (e.g. the message passing layer) across the cache servers. This is a relevant aspect when considering that the actual data-transport/networking infrastructure on top of which the data grid is deployed might be unknown, hence being not feasible to be modeled via white-box (namely purely simulative) approaches. We also provide an extended experimental study aimed at validating instances of simulation models supported by our framework against execution dynamics of real data grid systems deployed on top of either private or public cloud infrastructures. Particularly, our validation test-bed has been based on an industrial-grade open-source data grid, namely Infinispan by JBoss/Red-Hat, and a de-facto standard benchmark for NoSQL platforms, namely YCSB by Yahoo. The validation study has been conducted by relying on both public and private cloud systems, scaling the underlying infrastructure up to 100 (resp. 140) Virtual Machines for the public (resp. private) cloud case. Further, we provide some experimental data related to a scenario where our framework is used for on-line capacity planning and reconfiguration of the data grid system.     

一种适应数据与计算密集型任务的私有云系统实现研究*

与公有云计算相比,针对数据与计算双重密集型任务的私有云计算系统对计算效率和系统管理效率提出了更高的要求,目前的公有云计算系统显得过于复杂和繁琐,因此需要一种简便易用的能够适应数据与计算密集型任务的私有云计算系统实现。借鉴公有云计算的相关理论和实现方法,提出了一种针对数据与计算双重密集型任务的私有云计算系统实现方案。该方案通过作业文件描述用户的计算任务,确定计算任务的计算模型和计算的输入输出文件;针对私有云的特点,简化Google云计算系统的MapReduce并行处理框架,得到更加直观的数据计算模型;自动连     

Cloud Platform Datastore Support

There are many datastore systems to choose from that differ in many ways including public versus private cloud support, data management interfaces, programming languages, supported feature sets, fault tolerance, consistency guarantees, configuration, and their deployment processes. In this paper, we focus on technologies for structured data access (database/datastore systems) in cloud systems. Our goal is to simplify the use of datastore systems through automation and to facilitate their empirical evaluation using real world applications. To enable this, we provide a cloud platform abstraction layer that decouples a data access API from its implementation. Applications that use this API can use any datastore that “plugs into” our abstraction layer, thus enabling application portability. We use this layer to extend the functionality of multiple datastores without modifying the datastores directly. Specifically, we provide support for ACID transaction semantics for popular key-value stores (none of which provide this feature). We integrate this layer into the AppScale cloud platform—an open-source cloud platform that executes cloud applications written in Python, Java, and Go, over virtualized cluster resources and infrastructures-as-a-service (Eucalyptus, OpenStack, and Amazon EC2). We use this system to investigate the impact of extending disparate datastores via the application portability layer with distributed transaction support.     

面向云文件系统的隔离性度量方法研究

随着云计算的快速发展,云文件系统在云计算基础设施中扮演着越来越重要的角色。尽管目前业界已有不少面向云文件系统的性能评测工具,但大多数评测工具仅关注于传统的系统性能指标,比如IOPS和吞吐量,难以评估云文件系统在多租户环境下的性能隔离性。由于云环境I/O负载的动态性和异构性,所以准确评估云文件系统的隔离性变得更加具有挑战性。提出了一种新型的云文件系统隔离性度量模型,并在一个基准测试工具Porcupine中进行了实现。Porcupine通过模拟真实负载特征的I/O请求,实现对负载与性能的准确仿真并提高文件系统的测试效率。通过对Ceph文件系统的实验,验证了所提出的隔离性度量模型的有效性及准确性。     

Secure administration of cryptographic role-based access control for large-scale cloud storage systems

Cloud systems provide significant benefits by allowing users to store massive amount of data on demand in a cost-effective manner. Role-based access control (RBAC) is a well-known access control model which can be used to protect the security of cloud data storage. Although cryptographic RBAC schemes have been developed recently to secure data outsourcing, these schemes assume the existence of a trusted administrator managing all the users and roles, which is not realistic in large-scale systems. In this paper, we introduce a cryptographic administrative model AdC-RBAC for managing and enforcing access policies for cryptographic RBAC schemes. The AdC-RBAC model uses cryptographic techniques to ensure that the administrative tasks are performed only by authorised administrative roles. Then we propose a role-based encryption (RBE) scheme and show how the AdC-RBAC model decentralises the administrative tasks in the RBE scheme thereby making it practical for security policy management in large-scale cloud systems.     

云计算环境中数据安全存储协同模型*

针对云计算环境中来自数据中心或存储区域内部的攻击,以及适应更复杂的存储管理的需求。从分布式体系设计角度提出一个实现存储控制平面和数据存储平面融合的协同体系模型。为协同存储安全模型设计详细的多级可信管理、密钥管理和数据同步方案。实验结果表明对于大量中等文件的读写性能较好,数据安全管理造成的性能损失在可接受范围内。而且具有较好的可扩展性,能够适应大规模分布式存储系统的应用环境。     

Content-centric data and computation offloading in AI-supported fog networks for next generation IoT

Fog Computing (FC) based IoT applications are encountering a bottleneck in the data management and resource optimization due to the dynamic IoT topologies, resource-limited devices, resource diversity, mismatching service quality, and complicated service offering environments. Existing problems and emerging demands of FC based IoT applications are hard to be met by traditional IP-based Internet model. Therefore, in this paper, we focus on the Content-Centric Network (CCN) model to provide more efficient, flexible, and reliable data and resource management for fog-based IoT systems. We first propose a Deep Reinforcement Learning (DRL) algorithm that jointly considers the content type and status of fog servers for content-centric data and computation offloading. Then, we introduce a novel virtual layer called FogOrch that orchestrates the management and performance requirements of fog layer resources in an efficient manner via the proposed DRL agent. To show the feasibility of FogOrch, we develop a content-centric data offloading scheme (DRLOS) based on the DRL algorithm running on FogOrch. Through extensive simulations, we evaluate the performance of DRLOS in terms of total reward, computational workload, computation cost, and delay. The results show that the proposed DRLOS is superior to existing benchmark offloading schemes.     

Ceph分布式存储系统性能优化技术研究综述

Ceph是一个统一的分布式存储系统,可同时提供块、文件和对象3种接口的存储服务。与传统的分布式存储系统不同,它采用了无中心节点的元数据管理方式,因此具有良好的扩展性和线性增长的性能。经过十余年的发展,Ceph已被广泛地应用于云计算和大数据存储系统。作为云计算的底层平台,Ceph除了提供虚拟机的存储服务外,还可以直接提供对象存储服务和NAS文件服务。Ceph支撑着云计算系统中多种操作系统和应用的存储需求,它的性能对其上的虚拟机和应用有较大的影响,因此Ceph存储系统的性能优化一直是学术界和工业界的研究热点。文中首先介绍了Ceph的架构和特性;然后针对现有的性能优化技术,从对内部机制进行改进、面向新型硬件和基于应用的优化这3个方面进行了归纳和总结,综述了近年来Ceph存储和优化的相关研究;最后对该领域未来的工作进行了展望,以期为分布式存储系统性能优化的研究者提供有价值的参考。