对等式协同设计系统数据一致性研究

为解决分布式协同设计系统中的异地编辑一致性及多副本同步等问题,提出基于分布式哈希表(DHT)的分布式互斥算法,给出该算法的实现方法。通过采用DHT化的优先队列解决了异地编辑一致性操作问题。将传统的“锁”算法扩展为“对等锁”,解决了多副本同步问题。实验结果表明,该算法的复杂度远低于其他算法,从而验证了该方法的有效性。     

云存储中副本自适应一致性研究

在云存储系统中,为了保证系统可用性的同时尽可能提高系统性能,降低副本同步过程中网络带宽等资源的消耗。提出了基于文件热度的副本自适应一致性方法。该方法根据文件的热度不同,动态调整副本同步的一致性策略,对于热度高的文件在副本同步过程中采用强一致性,对于热度低的文件在副本同步过程中采用最终一致性。在计算文件热度时,考虑了文件访问的时间序列并结合LRFU算法,该热度值表示了文件将来可能的访问情况,并与文件的实际访问情况一致。实验表明该方法在保证系统可用性的同时显著降低了网络带宽资源的消耗,有效地平衡了系统的可用性和性能。     

一种基于分布式环境的副本自动审视技术研究

分布式环境中副本的大量存在满足了系统安全性、可靠性、通信代价以及查询效率方面的要求,同时,副本的访问策略以及一致性维护一直也是困扰分布式数据库理论和实践的重要问题。文章在分析副本更新同步策略的基础上,提出了一种基于分布式环境的副本自动审视技术。详细阐述了该技术指导下的全局字典扩充方法,提出了基于松散一致性维护的同步更新算法和执行策略。该技术在很大程度上提升了现有DDBMS的功能,从而对分布式数据库技术的应用和推广产生了积极的作用。     

全局数据目录的动态管理和维护

针对分布式并行数据库系统DPDBS中数据库副本数、数据库分布等信息的动态性,提出全局数据目录的动态管理和维护算法。该算法将数据目录一致性检查嵌入数据库执行过程中,通过改进两阶段提交协议和采用捎带、恢复和并行处理等技术,在低开销的情况下动态地保证了全局数据信息的一致性和实时性。     

计算资源整合的网格技术实现

针对分布式并行数据库系统DPDBS中数据库副本数、数据库分布等信息的动态性，提出全局数据目录的动态管理和维护算法。该算法将数据目录一致性检查嵌入数据库执行过程中，通过改进两阶段提交协议和采用捎带、恢复和并行处理等技术，在低开销的情况下动态地保证了全局数据信息的一致性和实时性。     

基于对等模型的分布式协同设计系统

为了解决分布式协同设计系统中的信息快速检索以及多副本同步等问题，引入了对等模型，给出了该系统的功能模型、信息检索模型等。提出了DHT的对等信息检索方法，保证了用户能够在分布式协同设计系统中快速共享资源。采用基于DHT的分布式互斥等算法作为协同数据的一致性维护方法，给出了系统的具体实现方法和实例。     

弱连接网络环境中的多主机文件同步

移动网络,广域网环境下,因终端频繁的加入与离开,以及网络的不稳定,造成节点的任意分隔.为避免网络分隔对可用性的影响,支持用户对任意副本的更新,文中实现了文件自动同步服务,Robin.通过设置在线服务器作为中转节点,可支持任意数目节点的扩展,在节点维护数据的弱一致性.Robin结合了状态比对和变化监控两种更新发现机制,增强同步的实时性.为解决并发更新对文件系统完整性的破坏,以及同步文件被占用造成合并更新失败等问题,Robin采用基于操作的冲突检测与处理.为不同的更新操作设置合并依赖条件,失败处理策略.为减少更新操作,Robin将文件系统日志中的多个变化事件合并为更新操作,保存在操作队列中,并进一步消除冗余操作.     

分布式数据库中维持数据副本一致性的方法——SPL Server 7.0数据复制的原理及应用

本文探讨在松散耦合的分布式数据系统中,保持数据副本之间数据一致性问题.介绍Microsoft SQL-Server7.0中引入的基于事务日志、带有冲突解决的数据复制方法,并举例说明这种方法的应用.     

Elsa:一种面向跨区域架构的无协调分布式键值存储系统

作为具备高性能和高可伸缩性的分布式存储解决方案,键值存储系统近年来被广泛采用,例如Redis、MongoDB、Cassandra等.分布式存储系统中广泛使用的多副本机制一方面提高了系统吞吐量和可靠性,但同时也增加了系统协调和副本一致性的额外开销.对于跨域分布式系统来说,远距离的副本协调开销甚至可能成为系统的性能瓶颈,降低系统的可用性和吞吐量.提出分布式键值存储系统Elsa,这是一种面向跨区域架构的无协调键值存储系统. Elsa在保证高性能和高可拓展性的基础上,采用无冲突备份数据结构(CRDT)技术来无协调的保证副本间的强最终一致性,降低了系统节点间的协调开销.在阿里云上构建了跨4数据中心8节点的跨区域分布式环境,进行了大规模分布式性能对比实验,实验结果表明:在跨域的分布式环境下,对于高并发争用的负载, Elsa系统的性能具备明显的优势,最高达到MongoDB集群的7.37倍, Cassandra集群的1.62倍.     

自适应组件副本选择模型及算法

提出了一个基于域的自适应副本选择模型DARSM(domain based adaptive replica selection model).该模型将组件副本划分为强一致性域和弱一致性域,域间通过一致性窗口机制进行状态同步.基于DARSM模型,给出了一种基于分区加权的自适应副本选择算法PWARS(partition-weighted based adaptive replica selection,).该算法利用动态性能度量信息来选择满足时间约束和一致性约束的组件副本集合.为了适应请求一致性约束的动态变化,还提出了一致性窗口自适应重配算法CWAR(consistency window adaptive reconfiguration).通过引入的一个一致性约束的可能性模型,该算法动态地对一致性窗口进行重配,从而实现了副本一致性的自适应控制.通过在OnceAS应用服务器集群中的原型实验及性能评价,表明该方法能够明显地提高副本选择的性能.     

并行系统中时间偏移机制的典型应用算法分析

像其它许多领域一样,时间偏移机制在并行计算中也得到了充分的应用。实际上,并行计算并不能真正做到让各处理机都完全无时差地实现"并行"运算。由于各任务间存在数据依赖性,使得一些处理机不得不处于间歇等待状态,直至数据到达为止。通过一个典型的并行算法实例对时间偏移机制的作用过程作了详解,直观地描述了实现并行计算的实质,以便为用户在理解并行行为和设计并行程序时提供一些参考。     

基于CS架构的天然气远程监控系统

目前由于天然气使用异常造成的安全事故频发,而现有的天然气监测报警器在天然气监测过程中具有一定的局限性,针对上述情况,提出了搭建一个基于CS（Client-Server）框架的远程监控系统来对一定区域里所有的天然气使用节点进行远程监控的方案。设计了基于PriorityBlockingQueue（优先级阻塞队列）的线程优先调度算法确保客户端发送请求在安全时间范围内被系统接受处理,从而提高系统实时性。运用了DES加密算法对数据进行加密,提高系统的安全性。     

面向电耗与网络同步代价优化的数据副本放置研究

在数据中心放置海量数据时,每个数据常有多个副本,服务提供商需要支付巨额电费以运行存储这些数据副本的服务器。同时,为保证多个数据副本的一致性,放置在不同数据中心的副本需要通过数据中心之间的网络进行同步,从而引发高额的网络传输费用。为此,以最小化多副本数据放置代价为目标,建立数据放置问题模型,并提出一种基于数据组和数据中心划分的数据放置算法DDDP。将数据划分为多个数据组,按用户访问数据的延迟要求将数据中心划分成数据中心子集,并将每个数据组中的数据放置到能满足访问延迟要求且能最小化放置代价的数据中心子集中。仿真结果表明,相比NPR算法,DDDP算法能有效降低数据中心存储数据时的放置代价。     

流媒体直播（时移）系统的设计

随着多媒体技术的发展和宽带网络的普及，流媒体技术得到越来越广泛的应用，其中，尽管流媒体直播技术定位于提供实时流服务，然而，国内外现有直播系统均未能很好解决直播系统的实时性与用户的个性化需求之间的矛盾。针对这个问题，首先设计了一个具有完全自主知识产权的支持时移功能的流媒体直播系统，并创新地采用一种实现时移的时间计算方法。目前该系统已成功应用于上海网络电视试点的大宁模式中。     

基于节点映射与标签数据构建的链接预测方法

为改进基于局部或全局信息相似性度量方法中存在的无法全面提取网络结构信息的问题,以及基于网络表示学习的方法不能对链接的不存在性进行度量的问题,提出一种结合节点向量化方法与机器学习分类算法的Net2Vec-CLP框架。使用具有重启机制的随机游走方法获得节点环境序列,将源网络信息转换成向量表示,在此基础上生成标签数据集,使用带sigmoid核映射方法的SVM模型进行二分类预测。实验结果表明,算法在Facebook数据集上较Node2Vec方法AUC值提高了2.47%,在其它数据集上也有可观测的优势。同时,结合二分类思想的方法,其能明确度量不存在链接关系的数据。     

Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones

Pervasive computing promotes the integration of smart devices in our living spaces to develop services providing assistance to people. Such smart devices are increasingly relying on cloud-based Machine Learning, which raises questions in terms of security (data privacy), reliance (latency), and communication costs. In this context, Federated Learning (FL) has been introduced as a new machine learning paradigm enhancing the use of local devices. At the server level, FL aggregates models learned locally on distributed clients to obtain a more general model. In this way, no private data is sent over the network, and the communication cost is reduced. Unfortunately, however, the most popular federated learning algorithms have been shown not to be adapted to some highly heterogeneous pervasive computing environments. In this paper, we propose a new FL algorithm, termed FedDist, which can modify models (here, deep neural network) during training by identifying dissimilarities between neurons among the clients. This permits to account for clients’ specificity without impairing generalization. FedDist evaluated with three state-of-the-art federated learning algorithms on three large heterogeneous mobile Human Activity Recognition datasets. Results have shown the ability of FedDist to adapt to heterogeneous data and the capability of FL to deal with asynchronous situations.     

基于加密与消息认证的安全数字出版技术

为了满足安全数字出版技术的需求,提出了一种融合高级加密与消息认证算法的新型解决方案.系统通过HMAC算法做为消息认证机制,核心Hash函数采用引入私密密钥后的HMAC技术的SHA-1算法.数字出版文件的加密是系统算法的第二层,AES加密算法具有可选的密钥长度,提供优于DES及3DES算法的加密性能,通过AES算法加密的文件难于破解.同时系统中采用客户端无法更改的机器ID号作为AES算法的加密密钥,客户端密文使用时需读取ID号进行动态解密,并且解密后的文件不会存储在设备硬盘中,防止了解密文件的非法发布.     

A hybrid heuristic queue based algorithm for task assignment in mobile cloud

This paper presents a novel algorithm for task assignment in mobile cloud computing environments in order to reduce offload duration time while balancing the cloudlets’ loads. The algorithm is proposed for a two-level mobile cloud architecture, including public cloud and cloudlets. The algorithm models each cloud and cloudlet as a queue to consider cloudlets’ limited resources and study response time more accurately. Performance factors and resource limitations of cloudlets such as waiting time for clients in cloudlets can be determined using queue models. We propose a hybrid genetic algorithm (GA) - Ant Colony Optimization (ACO) algorithm to minimize mean completion time of offloaded tasks for the whole system. Simulation results confirm that the proposed hybrid heuristic algorithm has significant improvements in terms of decreasing mean completion time, total energy consumption of the mobile devices, number of dropped tasks over Queue based Random, Queue based Round Robin and Queue based weighted Round Robin assignment algorithms. Also, to prove the superiority of our queue based algorithm, it is compared with a dynamic application scheduling algorithm, HACAS, which has not considered queue in cloudlets.     

Planning in the New York city police department—Politics and process

Criminal Justice Planning has been under increasing criticism in the last few years. Most recently, in the House and Senate Hearings which preceded the enactment of the Justice Systems Improvement Act of 1979, the requirement for comprehensive planning was considered by many to be ineffectual at best and a cumbersome burden at worst. By reexamining the planning process we may be able to gain some insight into the use, misuse, and potential of planning as an organizational tool and reassess what can be realistically expected. Through a case study of the way in which the NYCPD implemented and practiced formal planning in the three decades from 1948 to 1978, three problems can be identified. It has been very difficult to operationally define the term planning which has resulted in confusion, conflict, and criticism. There is an inherent conflict between the organization's survival needs and its rational planning processes. ‘Rational’ plans may not take account of changes in the political environment or may be distorted or subverted when implemented by field personnel. It appears that an unexpectedly long time may be necessary for fundamental systemic changes to be accepted in an organization. This means that in a ‘results oriented’ organization, where a plan is expected to ‘be successful’ in one to three years, plans may be discarded before they have been allowed sufficient time to develop. In addition to examining these issues, the study identifies and describes the various phases in the life cycle of the planning unit.     

An event-based model for continuous media data on heterogeneous disk servers

Over the last few years, there has been intense work on the problem of retrieval of continuous media (CM) data from disk. However, no single unified framework exists within which such retrieval problems can be studied. In this paper, we first propose a formal model for CM data retrieval from heterogeneous disk servers. This model can be used to characterize CM data retrieval problems independently of how data is laid out on disk, and what objectives (e.g., minimize client delay, maximize buffer utilization, etc.) the system manager is interested in. We then show how using this formal model, we can neatly define what it means to optimally handle events that occur in movie-on-demand (MOD) systems. Examples of such events include new clients entering the system, old clients leaving the system, continuing clients doing pause, rewind and fast-forward operations. Multiple events may occur simultaneously and we show how such events trigger state transitions in the system. We then develop an algorithm called the QuickSOL algorithm that handles events occurring in MOD systems. This algorithm works in two phases: in the first phase, it quickly finds a way of handling as many of the events occurring at time t as possible. In the second phase, it optimizes the solution found in the first phase. The advantage is that the algorithm can be interrupted anytime after the first phase is completed. We report on experiments showing that QuickSOL works well in practice.