Scalable distributed on-the-fly symbolic model checking

This paper presents a scalable method for parallel symbolic on-the-fly model checking in a distributed memory environment. Our method combines a scheme for on-the-fly model checking for safety properties with a scheme for scalable reachability analysis. We suggest an efficient, BDD-based algorithm for a distributed construction of a counterexample. The extra memory requirement for counterexample generation is evenly distributed among the processes by a memory balancing procedure. At no point during computation does the memory of a single process contain all the data. This enhances scalability. Collaboration between the parallel processes during counterexample generation reduces memory utilization for the backward step. We implemented our method on a standard, loosely- connected environment of workstations, using a high-performance model checker. Our initial performance evaluation, carried out on several large circuits, shows that our method can check models that are too large to fit in the memory of a single node. Our on-the-fly approach may find counterexamples even when the model is too large to fit in the memory of the parallel system.     

The Generalized Dimension Exchange Method for Load Balancing in k-ary n-Cubes and Variants

The generalized dimension exchange (GDE) method is a fully distributed load balancing method that operates in a relaxation fashion for multicomputers with a direct communication network. It is parameterized by an exchange parameter λ that governs the splitting of load between a pair of directly connected processors during load balancing. An optimal λ would lead to the fastest convergence of the balancing process. Previous work has resulted in the optimal λ for the binary n-cubes. In this paper, we derive the optimal lambda′s for the k-ary n-cube network and its variants-the ring, the torus, the chain, and the mesh. We establish the relationships between the optimal convergence rates of the method when applied to these structures, and conclude that the GDE method favors high-dimensional k-ary n-cubes. We also reveal the superiority of the GDE method to another relaxation-based method, the diffusion method. We further show through statistical stimulations that the optimal lambda′s do speed up the GDE balancing procedure significantly. Because of its simplicity, the method is readily implementable. We report on the implementation of the method in two data-parallel computations in which the improvement in performance due to GDE balancing is substantial.     

基于哈希算法的脏数据回写磁盘实时调优策略

针对传统数据库缓冲池脏数据回写磁盘策略实时性与安全性差的问题,提出基于Hash算法与先入先出(FIFO)双向链表的数据库缓冲池脏数据回写磁盘实时调优策略。利用基于负载的调优策略创建多个内存FIFO队列链表,通过Hash算法将数据库缓冲区内的脏数据块按最后修改时间随机分配到不同队列负载中,实现FIFO队列链表的负载均衡,并利用全局时序约束将链表队列中的脏数据块分批回写磁盘,以解决传统脏数据回写磁盘策略系统资源消耗大与宕机后数据丢失风险高的问题。实验结果证明,该策略能提高脏数据回写的实时性及安全性,降低数据丢失率。     

供动态无锁数据结构使用的资源窃取型无锁内存池

动态内存管理的问题对无锁动态数据结构的性能尤为关键,因为多线程环境下的动态内存管理涉及开销较高的同步操作。提出一种构建用于动态无锁数据结构的内存池的方法来减少动态内存使用和与之相伴的动态内存管理开销。该方法通过平衡线程的动态内存消耗来减小内存开销,利用本方法构建的内存池基于线程私有的支持节点窃取的无锁循环队列。本方法具有以下优点:a)用本方法构建的内存池是无锁的;b)能够平衡线程的堆内存消耗;c)可以方便地与动态无锁数据结构集成。实验结果显示,用该方法构造的资源窃取型内存池扩展性较强,且能够在高负载下有效降低无锁数据结构的堆内存消耗和操作执行时间;平衡算法在很大程度上决定内存消耗量,内存池在高负载下的扩展性也受到它所用的数据结构自身多线程访问性能的影响。     

可扩展网络交换调度系统的分布式迭代算法*

提出了一种最长队列优先的分布式迭代算法。与现有算法不同的是,该算法针对可扩展网络交换调度结构的特点,为处于最高优先级的调度器安排了两次迭代。其中的第一次迭代实现最长虚拟输出队列(VOQ)的查找,并且在最高优先级时隙之前的一个时隙完成,以缩短信号的处理时间。仿真结果表明,本算法与现有算法相比,在大流量的uniform流量模式下,延时性能与吞吐率获得了明显的提高;同时,该算法的硬件代价小,有效地实现了性能和复杂度的良好折中。     

基于Petri网的集群应用系统性能评价

利用随机Petri网理论给出了一个集群应用软件负载平衡系统的抽象模型,通过细化其中的本地节点处理部分对3种集群动态负载平衡的调度策略和应用系统体系结构对负载平衡系统的影响进行了分析,得到了一些对大部分应用系统的设计起到指导作用的结论.这些结论是:(1)无论是静态负载平衡还是动态负载平衡都能提高集群系统的性能,动态负载平衡会得到更好的性能;(2)在动态负载平衡算法中除了要考虑系统中最重要的等待队列--应用队列外,还要考虑数据库队列;(3)异步体系架构将任务切分到各处理子系统中,有助于将各子系统负载数据综合到负载向量中,能够更准确地衡量系统负载、提高负载平衡系统的性能,优于同步体系结构.     

支持动态负载平衡的分层消息队列模型

中间件技术为解决异构分布式环境下的负载平衡问题提供了有力的工具,但传统的消息中间件负载平衡的实现较为复杂,其动态参数繁多且容易带来额外开销。提出了一种分层消息队列模型,该模型中利用队列组管理器对分布式队列进行组管理,并提供了丰富的任务分配策略。在该模型的基础上提出动态负载平衡实现方案:通过基于队列的阈值阈长模型实时监控成员队列的负载情况,采用集中式调度进行负载信息搜集和负载平衡决策,结合负载迁移和队列组管理进行过载处理。     

基于．NET Remoting的动态负载平衡模型

在对.NET Remoting技术和传统的分布式负载平衡算法深入研究的基础上,提出一种排序队列和哈希映射矩阵相结合的负载平衡策略,给出一个分布式环境下的动态负载平衡模型。一方面可提高系统吞吐量,另一方面可缩短任务请求的响应时间。模型采用模块化设计方法,使其具有部署灵活性和容错性,并应用滑动窗口机制提高模型的负载平衡指标可信度。     

Scalable distributed Louvain algorithm for community detection in large graphs

Community detection (or clustering) in large-scale graphs is an important problem in graph mining. Communities reveal interesting organizational and functional characteristics of a network. Louvain algorithm is an efficient sequential algorithm for community detection. However, such sequential algorithms fail to scale for emerging large-scale data. Scalable parallel algorithms are necessary to process large graph datasets. In this work, we show a comparative analysis of our different parallel implementations of Louvain algorithm. We design parallel algorithms for Louvain method in shared memory and distributed memory settings. Developing distributed memory parallel algorithms is challenging because of inter-process communication and load balancing issues. We incorporate dynamic load balancing in our final algorithm DPLAL (Distributed Parallel Louvain Algorithm with Load-balancing). DPLAL overcomes the performance bottleneck of the previous algorithms and shows around 12-fold speedup scaling to a larger number of processors. We also compare the performance of our algorithm with some other prominent algorithms in the literature and get better or comparable performance . We identify the challenges in developing distributed memory algorithm and provide an optimized solution DPLAL showing performance analysis of the algorithm on large-scale real-world networks from different domains.

     

分布式文件系统元数据服务的负载均衡框架

请求负载均衡,是分布式文件系统元数据管理需要面对的核心问题.以最大化元数据服务器集群吞吐量为目标,在已有元数据管理层之上设计实现了一种分布式缓存框架,专门管理热点元数据,均衡不断变化的负载.与已有的元数据负载均衡架构相比,这种两层的负载均衡架构灵活度更高,对负载的感知能力更强,并且避免了热点元数据重新分布、迁移引起的元数据命名空间结构被破坏的情况.经观察分析,元数据尺寸小、数量大,预取错误元数据带来的代价远远小于预取错误数据带来的代价.针对元数据的以上鲜明特点,提出一种元数据预取策略和基于预取机制的元数据缓存替换算法,加强了上述分布式缓存层的性能,这种两层的元数据负载均衡框架同时考虑了缓存一致性的问题.最后,在一个真实的分布式文件系统中验证了框架及方法的有效性.     

Experimental evaluation of distributed load balancing implementations

The evaluation of load balancing algorithms for use in general-purpose distributed systems has traditionally been carried out by discrete event simulation. This paper describes a testbed which supports the artificial creation of workload and the measurement of performance for load balancing algorithm implementations based on job migration in a small distributed system. Nine load balancing algorithms are implemented and evaluated under a range of environmental conditions. The experimental results confirm the effectiveness of load balancing and indicate the superiority of the centralised non-adaptive and distributed adaptive approaches. © 1998 John Wiley & Sons, Ltd.     

A distributed self-scheduler for partially ordered tasks

Large scale scientific applications such as weather modelling and continuous simulation require the orders of magnitude performance improvement available with the new generation of parallel vector supercomputers such as the Floating Point Systems T Series. Many of these applications exhibit a high degree of parallelism, much of which can be expressed as computational tasks which are of varying size and degrees of dependence on one another, and can be partially ordered for execution. DeSPOT (A Distributed Self-Scheduler for Partially Ordered Tasks) is an algorithm for the dynamic distribution of such non-uniform tasks to achieve automatic load balancing on a distributed memory hypercube multiprocessor. This paper describes the DeSPOt algorithm and presents its performance characteristics of various test cases using result timings on the FPS T 20.     

基于一致性哈希算法和Ckafka技术的IMS电话实时录音系统

基于电路交换的传统电话录音系统因其结构复杂,存储的不便捷和非实时性的特点,已经无法满足新时代行政办公对高效,即时的通话录音需求,因此,提出一种基于电力IMS的电话实时录音系统.首先分析了电力IMS交换网中电话终端实时录音的业务需求;其次介绍了系统的实现流程,阐明了系统的关键技术：利用录音服务器对其镜像端口的SIP报文进行解析获得媒体流并解码、采用一致性哈希算法的内存数据库作为解码数据的缓存机制、利用Ckafka技术在两者之间构建实时数据通道;最后就响应时间、吞吐量、容错能力和推送的最大时延这四项指标对录音服务器进行性能分析,结果表明该系统的实时性强,吞吐量大,具备一定的容错能力,并能实现多服务器之间的负载均衡.     

Distributed Path-Based Inference in Semantic Networks

This paper presents the task model, instruction set, reasoning scheme, software infrastructure, as well as the experimental results, of a new distributed semantic network system. Unlike the synchronous and static marker passing algorithm previously used for parallel semantic network design, our system operates asynchronously, supporting knowledge sharing, dynamic load balancing and duplicate checking. To better the performance in distributed environments, the system has two collaborating components: the slave module, which performs task execution; and the host module, which interacts with the user and processes the information for the slaves. Our current implementation focuses on path-based knowledge inferences, using ANSI C and the MPICH-G2 with flex lexical analyzer and the yacc parser generator. Tests of individual components have been performed on a SUN multiprocessor server. The experiments demonstrate promising speedups.     

对象存储系统中自适应的元数据负载均衡机制

面向对象的存储系统在研究、工程以及服务领域均得到了广泛的应用.在面向对象的存储系统中,元数据的负载均衡对于提高整个系统的I/O性能具有重要的作用.现有的元数据负载均衡策略不能动态地平衡元数据的访问负载,而且自适应性以及容错特性有待提高.提出了一种自适应的分布式元数据负载均衡机制(adaptabledistributed load balancing of metadata,简称ADMLB),包含基本的负载均衡算法和分布式的增量负载均衡算法.采用基本的负载均衡算法按照服务器的性能公平地分布负载,使用分布式的负载均衡算法定时地调整负载的分布.ADMLB采取分布式的方法均衡地在元数据服务器之间分布负载,根据负载的变化自适应地进行调整,具有很好的容错特性,而且用户可以高效地定位元数据服务器.     

一种基于GPU集群的深度优先并行算法设计与实现

深度优先搜索算法在GPU集群中大型图上的简单执行,会导致线程间的负载不平衡和无法合并内存访问的情况,这使得算法的性能较低.为了明显提高算法在单个GPU和多个GPU环境下的性能,在处理数据之前通过采取一系列有效的操作来进行重新编排.提出了构造线程和数据之间映射的新技术,通过利用前缀求和及二分查找操作来达到完美的负载平衡.为了降低通信开销,对DFS各分支中需要进行交换的边集执行修剪操作.实验结果表明,算法在单个GPU上可以尽可能地实现最佳的并行性,在多GPU环境下可以最小化通信开销.在一个GPU集群中,它可以对合有数十亿节点的图有效地执行分布式DFS.     

面向同构型多处理机系统的动态负载平衡算法

为了解决同构型多处理机系统中的负载不平衡问题,提出一种分布式控制、发送者驱动的动态平衡算法,该算法利用CPU队列长度衡量处理机负载状态、利用进程执行时间选取适合被迁移的负载、利用较完备的消息机制传播处理机负载状态和负载平衡要求,适用于计算密集型任务.实验结果验证了该算法的有效性.     

A stochastic load balancing algorithm for i‐Computing

This paper presents a stochastic dynamic load balancing algorithm for Internet computing, which is a new type of distributed computing involving heterogeneous workstations from different organizations on the Internet. To realize the practical environment, we assume the system to be comprised of heterogeneous, untrusted and non‐dedicated workstations connected by a non‐dedicated network. Our algorithm uses the product of the average processing time and the queue length of system jobs as the load index. Dynamic communication delay is included in the execution cost calculation. The transfer policy and the location policy are combined in a stochastic algorithm. State information exchange is done via information feedback and mutual updating. Simulations demonstrate that our algorithm outperforms conventional approaches over a wide range of system parameters. These results are reconfirmed by empirical experiments after we have implemented the algorithms on the Distributed Java Machine global virtual machine. Copyright © 2003 John Wiley & Sons, Ltd.     

A proximity-aware load balancing in peer-to-peer-based volunteer computing systems

One of the main challenges in peer-to-peer-based volunteer computing systems is an efficient resource discovery algorithm. Load balancing is a part of resource discovery algorithm and aims to minimize the overall response time of the system. This paper introduces an analytical model based on distributed parallel queues to optimize the average response time of the system in a distributed manner. The proposed resource discovery algorithm consists of two phases. In the first phase, it selects peers in a load-balanced manner based on QoS constraints of request. In the second phase, a proximity-aware feature is applied to select the peer with minimum communication overhead among selected peers in the first phase. Two dispatching strategies are proposed for the load balancing based on stochastic analysis of routing in the distributed parallel queues. These policies adopt probabilistic and deterministic sequences to redirect requests to the capable peers in the system. Simulation results show that the proposed resource discovery algorithm improves the response time of user’s requests by a factor of 1.8 under a moderate load.     

基于实时性能动态反馈的负载均衡算法

通过分析与研究当前Web服务器集群的负载均衡技术和调度算法,提出了一种新的基于实时性能动态反馈的负载均衡算法设计,算法引入了请求量化方法、实时性能指标和准入控制机制,实验结果表明本算法具有较好的低响应延迟和高吞吐率性能。