网格数据库物化查询缓存机制研究*

提出了基于XML Database的网格数据库物化查询缓存机制,提高用户查询的速度,均衡网格负载。定义了网格数据库服务质量与数据质量的标准,提出了物化查询选择算法MQS,为用户提供更好的数据服务。     

数据流处理中确定性QoS的保证方法

与以往尽最大努力的查询服务提供方式不同,讨论了数据流处理中确定性QoS的保证问题以网络演算为理论基础,提出了一种数据流处理中的QoS建模和QoS保证方法.系统运行前验证所有查询在满足各自的QoS前提下的可调度性.在运行时为通过QoS可调度性验证的每个查询分配代表其QoS需求的服务曲线,从而保证各查询期望的QoS.为了提高查询处理效率,还讨论了保证QoS的批调度和查询共享.实验结果表明,该QoS保证方法能够有效地为数据流上的连续查询提供确定性的QoS保证.     

数据流QoS自适应框架调度算法的研究与实现

在数据流QoS自适应框架中,针对如何为具有截止期和重要性两个特征参数的流数据分派优先级,在讨论了了当前调度算法优缺点的基础上,提出了称为截止期-重要性优先DIF（deadline-importance first）的实时调度算法,并利用链表给出了算法的实现.实验结果表明,在所有负载条件下DIF算法相对于EDF（earliest deadline first）算法、HVF（highest value first）算法和CDF（criticalness-deadline first）算法,在性能方面具有明显改善,特别在系统过载的情况下,能够优雅地降级.     

数据流系统中卸载技术研究综述

随着数据流应用系统的快速流行,流数据管理对数据库技术提出了巨大挑战。由于数据流经常是爆发性的且数据特征可能随时变化,因此要求数据流管理系统具有很好的自适应性。当输入速率超过系统处理能力时,系统会产生过载且性能下降。为了解决这一问题,卸载技术是有效的途径之一。卸载时间、卸载地点和卸载数量是与卸载技术密切相关的三个主要问题,本文主要从这三个方面来综述和分析目前各个数据流系统所采用的卸载技术。     

基于滑动窗口的数据流连接聚集查询降载策略

基于单个数据流的滑动窗口聚集查询降载技术和数据流连接技术,提出滑动窗口模型下的数据流连接聚集查询降载策略,给出判断系统是否过载的负载方程和使过载系统恢复到轻载状态的降载算法,使降载后的查询结果同时拥有较小的相对误差和最大的元组输出率。实验结果表明,该降载策略具有较好的可行性和适应性。     

Load shedding for multi-way stream joins based on arrival order patterns

We address the problem of load shedding for continuous multi-way join queries over multiple data streams. When the arrival rates of tuples from data streams exceed the system capacity, a load shedding algorithm drops some subset of input tuples to avoid system overloads. To decide which tuples to drop among the input tuples, most existing load shedding algorithms determine the priority of each input tuple based on the frequency or some historical statistics of its join attribute value, and then drop tuples with the lowest priority. However, those value-based algorithms cannot determine the priorities of tuples properly in environments where join attribute values are unique and each join attribute value occurs at most once in each data stream. In this paper, we propose a load shedding algorithm specifically designed for such environments. The proposed load shedding algorithm determines the priority of each tuple based on the order of streams in which its join attribute value appears, rather than its join attribute value itself. Consequently, the priorities of tuples can be determined effectively in environments where join attribute values are unique and do not repeat. The experimental results show that the proposed algorithm outperforms the existing algorithms in such environments in terms of effectiveness and efficiency.     

Web server QoS models: applying scheduling rules from production planning

Most web servers, in practical use, use a queuing policy based on the Best Effort model, which employs the first-in-first-out (FIFO) scheduling rule to prioritize web requests in a single queue. This model does not provide Quality of Service (QoS). In the Differentiated Services (DiffServ) model, separate queues are introduced to differentiate QoS for separate web requests with different priorities. This paper presents web server QoS models that use a single queue, along with scheduling rules from production planning in the manufacturing domain, to differentiate QoS for classes of web service requests with different priorities. These scheduling rules are Weighted Shortest Processing Time (WSPT), Apparent Tardiness Cost (ATC), and Earliest Due Date. We conduct simulation experiments and compare the QoS performance of these scheduling rules with the FIFO scheme used in the basic Best Effort model with only one queue, and the basic DiffServ model with two separate queues. Simulation results demonstrate better QoS performance using WSPT and ATC, especially when requested services exceed the capacity of a web server.     

Performance vs. freshness in web database applications

In this work, we present a novel approach for the efficient materialization of dynamic web pages in e-commerce applications such as an online retail store with millions of items, hundreds of HTTP requests per second and tens of dynamic web page types. In such applications, user satisfaction, as measured in terms of response time (QoS) and content freshness (QoD), determines their success especially under heavy workload. The novelty of our materialization approach over existing ones is that, it considers the data dependencies between content fragments of a dynamic web page. We introduce two new semantic-based data freshness metrics that capture the content dependencies and propose two materialization algorithms that balance QoS and QoD. In our evaluation, we use a real-world experimental system that resembles an online bookstore and show that our approach outperforms existing QoS-QoD balancing approaches in terms of server-side response time (throughput), data freshness and scalability.     

一种新的基于AR* -GRNN的QoS降载管理框架

在过载环境下不是很高的降载比率很可能要丢失要分析的全部信息,故有效地获取流数据的信息是流数据挖掘的关键问题.本文建立基于AR* -GRNN的QoS降载管理框架,提高过载环境下流数据的相似性查询效率.利用离散傅立叶变换(Discrete Fourier Transform,DFT)来获取数据的特征值,运用混合预测模型(时间序列类模型(AR*)-Generalized RegressionNeural Network,AR* -GRNN)和历史的特征值来预测降载部分数据的特征值,建立自适应的降载计划,从而完成降载发生情况下的相似性查询分析.     

Quality-aware schedulers for weak consistency key-value data stores

Highly distributed NoSQL key-value data stores are rapidly becoming the favorite choice for hosting modern web applications. Such platforms rely on data partitioning, replication and relaxed consistency to achieve high levels of performance and scalability. However, these design choices often exhibit a trade-off between latency (i.e., Quality of Service (QoS)) and consistency (i.e., Quality of Data (QoD)). In this work, in addition to latency-based SLAs, we also adopt the application tolerance to data staleness as another requirement determining the end-user satisfaction and our goal is to strike a fine balance between both the QoS and QoD provided to the end-user. Towards achieving that goal, we propose a suite of quality-aware schedulers for efficiently allocating the necessary computational resources between the foreground user-queries and the background system-updates at data store nodes. This suite of schedulers features our proposed Freshness/Tardiness (FIT) mechanism, which introduces a novel selective approach for scheduling the execution of queries and updates. Our experimental results show that FIT provides significant improvements in balancing the trade-off between QoS and QoD under both the state-transfer and operation-transfer update propagation models employed in current key-value data stores.     

Semantic approximation of data stream joins

We consider the problem of approximating sliding window joins over data streams in a data stream processing system with limited resources. In our model, we deal with resource constraints by shedding load in the form of dropping tuples from the data streams. We make two main contributions. First, we define the problem space by discussing architectural models for data stream join processing and surveying suitable measures for the quality of an approximation of a set-valued query result. Second, we examine in detail a large part of this problem space. More precisely, we consider the number of generated result tuples as the quality measure and we propose optimal offline and fast online algorithms for it. In a thorough experimental study with synthetic and real data, we show the efficacy of our solutions.     

数据流系统中一种基于速率的抢占式批处理方法

针对数据流的特征，提出了一种基于速率的抢占式批处理方法。一个查询计划是一个操作符序列。文章将一个查询计划划分为不同的操作单元，并为不同的操作单元分配不同的优先级，而且这个优先级随系统因素的变化而动态改变，根据变化的优先级来动态调度操作单元，采取抢占式调度，从而提高连续查询的查询效率。实验表明该方法不但能提高系统的总体性能，而且可以减少元组的平均等待时间，大大提高了元组的输出速率。     

一种面向公平保证QoS的WiMAX二级调度方案

IEEE 802.16作为全球微波接入互操作系统技术标准,虽然定义了5类信流(分别是UGS,rtPS,ertPS,nrtPS和BE),并将服务质量支持机制引入媒体接入控制层,却没有规定相应的调度算法.为有效保证各种多媒体通信的服务质量,提出了一种基于正交频分多址接入技术和自适应调制编码机制的二级调度方案.该调度方案采用跨层设计思想,适用于PMP WiMAX网络下行链路中.一级调度器按照QoS优先级顺序调度位于不同类型缓存器的队头分组,从而满足rtPS业务的最大时延限定和nrtPS业务的最小速率要求;完成一级调度后,为满足用户速率公平性.二级调度器根据自适应调制编码信息及用户状态信息调度位于不同用户缓存器的队头分组.仿真结果表明该方案能够有效保证各种多媒体通信服务满足QoS要求并兼顾用户速率公平,同时也可获得较高的WiMAX系统吞吐量.     

流系统的降载技术

流数据连续查询及优化研究已成为当前国际数据库研究领域的一个热点。数据流的到达速率经常是不可预测的且具有很高的突发性,数据流速这种不规则的变化会引起系统负载急剧的波动。当输入速率超过系统处理能力时,系统会发生过载并且导致系统性能的恶化,降载技术是解决此问题最有效的途径之一。对降载技术中系统负载估计、降载器的最佳的放置位置、降载量的大小、降载器合并等关键问题进行了讨论。     

易变数据流的系统资源配置方法

大规模数据流管理系统往往由上层的关系查询系统和下层的流处理系统组成。当用户提交查询请求时,往往需要根据数据流的流速和分布情况动态配置系统参数。然而,由于数据流的易变性,频繁改变参数配置会降低系统性能。针对该问题,提出了OrientStream+框架。设定以用户自定义查询延迟阈值为间隔片段的微批量数据流传输机制;并利用多级别管道缓存,对相同配置的数据流进行批量处理;然后按照数据流的时间戳计算出精准查询结果;引入基于异常检测的增量学习模型,用于提高OrientStream+的预测精度。最后,在Storm上实现了该资源配置框架,并进行了大量的实验。实验结果表明,OrientStream+框架可进一步降低系统的处理延迟并提高系统的吞吐率。     

车联网数据流的多维服务质量改进异构计算最早完成时间调度算法

针对车联网中数据流分布式处理的调度问题,提出了多维服务质量(QoS)改进异构计算最早完成时间(HEFT)调度算法.首先,分别建立了车联网中数据流的分布式处理任务的带权有向无环图模型和车联网分布式计算资源的七维QoS属性带权无向拓扑结构图模型.其次,改进经典的HEFT调度算法中的列表构造方法为最高层最小后继任务优先列表构造方法; 同时,将车联网分布式计算资源的七维QoS属性进行分组、降维,转化为两维综合属性优先权:计算性能优先权和通信性能优先权,形成了两种不同用户偏好的多维QoS改进HEFT调度算法.最后,通过算例分析表明:两种不同用户偏好的多维QoS改进HEFT调度算法综合性能优于经典的HEFT调度算法和轮询调度算法.     

Enhancing router QoS through job scheduling with weighted shortest processing time-adjusted

Most routers on the Internet employ a first-in-first-out (FIFO) scheduling rule to determine the order of serving data packets. This scheduling rule does not provide quality of service (QoS) with regards to the differentiation of services for data packets with different service priorities and the enhancement of routing performance. We develop a scheduling rule called Weighted Shortest Processing Time–Adjusted (WSPT-A), which is derived from WSPT (a scheduling rule for production planning in the manufacturing domain), to enhance router QoS. We implement a QoS router model based on WSPT-A and run simulations to measure and compare the routing performance of our model with that of router models based on the FIFO and WSPT scheduling rules. The simulation results show superior QoS performance when using the router model with WSPT-A.     

Scalable and Adaptive Joins for Trajectory Data in Distributed Stream System

As a fundamental operation in LBS (location-based services), the trajectory similarity of moving objects has been extensively studied in recent years. However, due to the increasing volume of moving object trajectories and the demand of interactive query performance, the trajectory similarity queries are now required to be processed on massive datasets in a real-time manner. Existing work has proposed distributed or parallel solutions to enable large-scale trajectory similarity processing. However, those techniques cannot be directly adapted to the real-time scenario as it is likely to generate poor balancing performance when workload variance occurs on the incoming trajectory stream. In this paper, we propose a new workload partitioning framework, ART (Adaptive Framework for Real-Time Trajectory Similarity), which introduces practical algorithms to support dynamic workload assignment for RTTS (real-time trajectory similarity). Our proposal includes a processing model tailored for the RTTS scenario, a load balancing framework to maximize throughput, and an adaptive data partition manner designed to cut off unnecessary network cost. Based on this, our model can handle the large-scale trajectory similarity in an on-line scenario, which achieves scalability, effectiveness, and efficiency by a single shot. Empirical studies on synthetic data and real-world stream applications validate the usefulness of our proposal and prove the huge advantage of our approach over state-of-the-art solutions in the literature.

     

多数据流上共享窗口连接查询的降载策略

数据流模型作为一种新型的模型，在许多应用中扮演着重要的角色．基于数据流模型的查询处理技术也得到了广泛的研究．为了提高查询系统的性能，现有的研究成果主要可以划分为两类：调度优化和降低负载方法．调度优化方法通过改变元组执行次序来提高查询性能．降低负载方法在负载超出系统处理能力时，通过减少输入流量来提高吞吐率．然而，同时运用这两种方法来提高查询性能的研究工作还很少．结合共享滑动窗口查询操作的调度优化方法和降低负载方法，提出了两种在burst环境下提高查询吞吐率的策略：均匀降载策略和小窗口准确降载策略．理论分析和实验结果均证明这两种策略能显著提高系统的性能．