Consensus-based linear distributed filtering

We address the consensus-based distributed linear filtering problem, where a discrete time, linear stochastic process is observed by a network of sensors. We assume that the consensus weights are known and we first provide sufficient conditions under which the stochastic process is detectable, i.e. for a specific choice of consensus weights there exists a set of filtering gains such that the dynamics of the estimation errors (without noise) is asymptotically stable. Next, we develop a distributed, sub-optimal filtering scheme based on minimizing an upper bound on a quadratic filtering cost. In the stationary case, we provide sufficient conditions under which this scheme converges; conditions expressed in terms of the convergence properties of a set of coupled Riccati equations.     

Consensus-based evaluation framework for distributed information retrieval systems

Multi-agent systems have been attacking the challenges of information retrieval tasks on distributed environment. In this paper, we propose a consensus choice selection method based framework to evaluate the performance of cooperative information retrieval tasks of the multiple agents. Thereby, two well-known measurements, precision and recall, are extended to handle consensual closeness (i.e., local and global consensus) between the sets of retrieved results. We show that in a motivating example the proposed criteria are prone to solve the rigidity problem of classical precision and recall. More importantly, the retrieved results can be ranked with respect to the consensual score, and the ranking mechanism has been verified to be more reasonable.

Consensus-based modeling using distributed feature construction with ILP

A particularly successful role for Inductive Logic Programming (ILP) is as a tool for discovering useful relational features for subsequent use in a predictive model. Conceptually, the case for using ILP to construct relational features rests on treating these features as functions, the automated discovery of which necessarily requires some form of first-order learning. Practically, there are now several reports in the literature that suggest that augmenting any existing feature with ILP-discovered relational features can substantially improve the predictive power of a model. While the approach is straightforward enough, much still needs to be done to scale it up to explore more fully the space of possible features that can be constructed by an ILP system. This is in principle, infinite and in practice, extremely large. Applications have been confined to heuristic or random selections from this space. In this paper, we address this computational difficulty by allowing features and models to be constructed in a distributed manner. That is, there is a network of computational units, each of which employs an ILP engine to construct some small number of features and then builds a (local) model. We then employ an asynchronous consensus-based algorithm, in which neighboring nodes share information and update local models. This gossip-based information exchange results in the formation of non-stationary Markov chains. For a category of models (those with convex loss functions), it can be shown (using the Supermartingale Convergence Theorem) that the algorithm will result in all nodes converging to a consensus model. In practice, it may be slow to achieve this convergence. Nevertheless, our results on synthetic and real datasets suggest that in relatively short time the “best” node in the network reaches a model whose predictive accuracy is comparable to that obtained using more computational effort in a non-distributed setting (the best node is identified as the one whose weights converge first).     

A CSO-based approach for secure data replication in cloud computing environment

The Journal of Supercomputing - Cloud computing has a significant impact on information technology solutions for both organizations and researchers. Different users share critical data over the...     

Locality-aware process placement for parallel and distributed simulation in cloud data centers

The Journal of Supercomputing - Cloud is a multi-tenant paradigm providing resources as a service. With its easily available computing infrastructure, researchers are adopting cloud for...     

An efficient multipath routing for distributed computing systems with data replication

In distributed computing environments, executing a program often requires the access of remote data files. An efficient data routing scheme is thus important for time-critical applications. To ensure a prior desired communication quality, we present a connection-oriented routing scheme, the multipath routing, which allows multiple routes to be established between the source and the destination. Based on the multipath routing scheme, the problem of finding a collection of routing paths for an application to minimize its data transmission time is addressed. Such a problem becomes a complex combinatorial one when the application accesses multiple replicated data sources. Since finding an optimal solution is computationally infeasible in practice, we thus propose a heuristic method to get a sub-optimal solution.     

Improved security of a dynamic remote data possession checking protocol for cloud storage

Cloud storage offers the users with high quality and on-demand data storage services and frees them from the burden of maintenance. However, the cloud servers are not fully trusted. Whether the data stored on cloud are intact or not becomes a major concern of the users. Recently, Chen et al. proposed a remote data possession checking protocol to address this issue. One distinctive feature of their protocol support data dynamics, meaning that users are allowed to modify, insert and delete their outsourced data without the need to re-run the whole protocol. Unfortunately, in this paper, we find that this protocol fails to achieve its purpose since it is vulnerable to forgery attack and replace attack launched by a malicious server. Specifically, we show how a malicious cloud server can deceive the user to believe that the entire file is well-maintained by using the meta-data related to the file alone, or with only part of the file and its meta-data. Then, we propose an improved protocol to fix the security flaws and formally proved that our proposal is secure under a well-known security model. In addition, our improvement keeps all the desirable features of the original protocol.     

分布式系统数据复制协议的研究

数据复制正在并行信息系统的设计中起着越来越重要的作用.特别地,为了提高性能和可用性,对集群体系结构的广泛使用经常要求复制数据.然而,维持不同拷贝的一致性带来严重的可伸缩性问题.为了克服这个局限性,基于表决的协议经常被作为一种缩减复制的总开销的方法.为了更好理解它们在实际中的性能,分析了几种表决算法,结果是基于表决的复制协议表现出不错的性能.测试表明ROWA-A(read-one/write-all-available)协议对于需要大量数据复制的应用是最好的选择.     

分布式系统中数据复制的研究与应用

维持数据的一致性是复制数据库并发执行的关键。介绍分析了MS SQL Server 2000中内置的数据复制构件以及MS SQL Server 2000的事务模型,最后结合实际的应用软件开发对数据复制的并发控制原理与措施进行了相关实现。     

可用于云制造环境的新型现场总线协议

针对云制造模式中底层物理资源属性难以接入云制造平台的问题,提出一种云现场总线协议Cloud_Field_Bus以及与之配合的多协议转换模块。以标准以太网协议为基础,优化其数据链路层及上层协议,实现海量数据信息可靠传输;多协议转换模块实现多种协议接口的透明接入。仿真实验测试Cloud_Field_Bus协议的报文循环时间、编码效率以及误码率,实验结果表明,Cloud_Field_Bus协议信息表达的高效性和传输可靠性,在一定程度上解决了云制造底层物理资源接入困难的问题。     

SCALAR: Scalable data lookup and replication protocol for mobile ad hoc networks

Data replication, as an essential service for MANETs, is used to increase data availability by creating local or nearly located copies of frequently used items, reduce communication overhead, achieve fault-tolerance and load balancing. Data replication protocols proposed for MANETs are often prone to scalability problems due to their definitions or underlying routing protocols they are based on. In particular, they exhibit poor performance when the network size is scaled up. However, scalability is an important criterion for several MANET applications. We propose a scalable and reactive data replication approach, named SCALAR, combined with a low-cost data lookup protocol. SCALAR is a virtual backbone based solution, in which the network nodes construct a connected dominating set based on network topology graph. To the best of our knowledge, SCALAR is the first work applying virtual backbone structure to operate a data lookup and replication process in MANETs. Theoretical message-complexity analysis of the proposed protocols is given. Extensive simulations are performed to analyze and compare the behavior of SCALAR, and it is shown to outperform the other solutions in terms of data accessibility, message overhead and query deepness. It is also demonstrated as an efficient solution for high-density, high-load, large-scale mobile ad hoc networks.     

An SLA-based cloud computing that facilitates resource allocation in the distributed data centers of a cloud provider

The number of cloud service users has increased worldwide, and cloud service providers have been deploying and operating data centers to serve the globally distributed cloud users. The resource capacity of a data center is limited, so distributing the load to global data centers will be effective in providing stable services. Another issue in cloud computing is the need for providers to guarantee the service level agreements (SLAs) established with consumers. Whereas various load balancing algorithms have been developed, it is necessary to avoid SLA violations (e.g., service response time) when a cloud provider allocates the load to data centers geographically distributed across the world. Considering load balancing and guaranteed SLA, therefore, this paper proposes an SLA-based cloud computing framework to facilitate resource allocation that takes into account the workload and geographical location of distributed data centers. The contributions of this paper include: (1) the design of a cloud computing framework that includes an automated SLA negotiation mechanism and a workload- and location-aware resource allocation scheme (WLARA), and (2) the implementation of an agent-based cloud testbed of the proposed framework. Using the testbed, experiments were conducted to compare the proposed schemes with related approaches. Empirical results show that the proposed WLARA performs better than other related approaches (e.g., round robin, greedy, and manual allocation) in terms of SLA violations and the provider’s profits. We also show that using the automated SLA negotiation mechanism supports providers in earning higher profits.     

A secure data sharing scheme with cheating detection based on Chaum-Pedersen protocol for cloud storage

Frontiers of Information Technology & Electronic Engineering - With the development of cloud computing technology, data can be outsourced to the cloud and conveniently shared among users....     

Coding-based replication schemes for distributed systems

Data is often replicated in distributed systems to improve availability and performance. This replication is expensive in terms of disk storage since the existing schemes generally require full files to be stored at each site. In this paper, we present schemes which significantly reduce the storage requirements in replication based systems. These schemes use the coding method suggested by Rabin to store replicated data. The first scheme that we present is a modification of the simple voting algorithm and its quorum requirements. We then show how some of the extensions of the voting algorithm can also be modified to get storage efficient schemes for managing such replication. We evaluate the availability offered by these schemes and show that the storage space required to achieve certain availability are significantly lower than the conventional schemes with full file replication. Since coding is used, these schemes also provide a high degree of data security     

云计算架构下的动态副本管理策略

为有效管理云计算环境中的数据副本,减少系统带宽消耗、最小化响应时间和平衡负载,提出了一种动态副本管理策略.通过建立文件可用性和副本数量间的关系模型来计算系统应维持的最小副本数量;基于数据被请求访问的次数和传输花费进行副本放置;数据被请求时结合节点间带宽和节点效用选择副本.实验结果表明了该策略的正确性有效性.     

Consensus-based distributed optimisation of multi-agent networks via a two level subgradient-proximal algorithm

This paper presents a consensus-based stochastic subgradient algorithm for multi-agent networks to minimise multiple convex but not necessarily differential objective functions, subject to an intersection set of multiple closed convex constraint sets. Compared with the existing results an alternative subgradient algorithm is first introduced based on two level subgradient iterations, where the first level is to minimise the component functions, and the second to enforce the iterates not oscillate from the constraint set wildly. In addition, a distributed consensus-based type of the proposed subgradient algorithm is constructed within the framework of multi-agent networks for the case when the iteration index of local objective functions and local constraint sets is not homologous. Detailed convergence analysis of the proposed algorithms is established using matrix theories and super-martingale convergence theorem. In addition, a pre-step convergence factor is obtained in this study to characterise the distance between the iterations and the optimal set, while some existing literatures only present a convergence work. Simulation results are given to demonstrate the effectiveness of the developed theoretical results.     

云数据管理的新数据模型

为解决云计算中海量数据的存储管理问题,分析了关系数据模型和NoSQL数据模型各自的特点,提出了一种新的数据模型。该模型根据数据本身的特点将数据横向切分为一组实体的集合,不同的数据实体负责处理不同的数据应用,结合了关系数据模型的可用性与NoSQL数据模型的可扩展性。通过详细定义该模型的数据结构、约束条件以及数据操作,保证了数据模型的完整性。通过一个原型系统运行实例,验证了该模型的有效性,为云数据管理提供了可行的解决途径。