QoS Driven Per-Request Load-Aware Service Selection in Service Oriented Architectures

Service selection has been widely investigated by the SOA research community as an effective adaptation mechanism that allows a service broker, offering a composite service, to bind at runtime each task of the composite service to a corresponding concrete implementation, selecting it from a set of candidates which differ from one another in terms of QoS parameters. In this paper we present a load-aware per-request approach to service selection which aims to combine the relative benefits of the well known per-request and perflow approaches. Our service selection policy represents the core methodology of the Plan phase of a self-adaptive service oriented system based on the MAPE-K reference loop. Since the service broker operates in a variable and uncertain environment where the QoS levels negotiated with the service providers can fluctuate, it requires some mechanism to enforce the QoS constraints with its users. To this end, we also propose an algorithm for the Analyze phase of MAPE-K which is based on the adaptive Cusum algorithm and allows to determine whether a change in the QoS level requires a service selection replanning. We present experimental results obtained with a prototype implementation of a service broker. Our results show that the proposed load-aware approach is superior to the traditional perrequest one and combines the ability of sustaining large volume of service requests, as the perflow approach, while at the same time offering a finer customizable service selection, as the per-request approach. Furthermore, the results show that the adaptive Cusum algorithm can quickly detect changes in the execution environment and trigger a new optimization plan before the system performance degrades.     

Path selection in disaster response management based on Q-learning

Suitable rescue path selection is very important to rescue lives and reduce the loss of disasters, and has been a key issue in the field of disaster response management. In this paper, we present a path selection algorithm based on Q-learning for disaster response applications. We assume that a rescue team is an agent, which is operating in a dynamic and dangerous environment and needs to find a safe and short path in the least time. We first propose a path selection model for disaster response management, and deduce that path selection based on our model is a Markov decision process. Then, we introduce Q-learning and design strategies for action selection and to avoid cyclic path. Finally, experimental results show that our algorithm can find a safe and short path in the dynamic and dangerous environment, which can provide a specific and significant reference for practical management in disaster response applications.     

A New Subdivision Algorithm for the Bernstein Polynomial Approach to Global Optimization

In this paper,an improved algorithm is proposed for unconstrained global optimization to tackle non-convex nonlinear multivariate polynomial programming problems.The proposed algorithm is based on the Bernstein polynomial approach.Novel features of the proposed algorithm are that it uses a new rule for the selection of the subdivision point,modified rules for the selection of the subdivision direction,and a new acceleration device to avoid some unnecessary subdivisions.The performance of the proposed algorithm is numerically tested on a collection of 16 test problems.The results of the tests show the proposed algorithm to be superior to the existing Bernstein algorithm in terms of the chosen performance metrics.     

Iterative selection algorithm for service composition in distributed environments

In service oriented architecture （SOA）, service composition is a promising way to create new services. However, some technical challenges are hindering the application of service composition. One of the greatest challenges for composite service provider is to select a set of services to instantiate composite service with end- to-end quality of service （QoS） assurance across different autonomous networks and business regions. This paper presents an iterative service selection algorithm for quality driven service composition. The algorithm runs on a peer-to-peer （P2P） service execution environment--distributed intelligent service execution （DISE）, which provides scalable QoS registry, dynamic service selection and service execution services. The most significant feature of our iterative service selection algorithm is that it can work on a centralized QoS registry as well as cross decentralized ones. Network status is an optional factor in our QoS model and selection algorithm. The algorithm iteratively selects services following service execution order, so it can be applied either before service execution or at service run-time without any modification. We test our algorithm with a series of experiments on DISE. Experimental results illustrated its excellent selection and outstanding performance.     

A Multi-dimensional Trust-aware Cloud Service Selection Mechanism Based on Evidential Reasoning Approach

In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning(ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.     

A simple fault-tolerant adaptive and minimal routing approach in 3-D meshes

In this paper we propose a sufficient codition for minimal routing in 3-dimensional (3-D) meshes with faulty nodes,It is based on an early work of the author on minial routing in 2-dimensional(2-D) meshes,Unlike many traditional models that assume all the nodes know global fault distribution or just adjacent fault information,our approach is based on the concept of limited global fault information,First,we propose a fault model called faulty cube in which all faulty nodes in the system are contained in a set of faulty cubes.Fault information is then distributed to limited number of nodes while it is still sufficeint to support minimal routing.The limited fault information collcted at each node is represented by a vector caaled extended safety level.The extended safety level associated with a node can be used to determine the existence of a minimal path from this node to a given destination .Specifically,we study the existence of minimal paths at a given source node,limited distribution of fault information,minimal routing,and deadlock-free and livelock-free routing.our results show that any minimal routing that is partially adaptive can be applied in our model as long as the dstination node meets a certain conditon.We also propose a dynamic planar-adaptive routing scheme that offers better fault tolerance and adaptivity than the planar-adaptive routing scheme in 3-D meshes.Our approach is the first attempt to address adaptive and minimal routing is 3-D meshes with faulty nodes using limited fault information.     

A representative model based algorithm for maximal contractions

In this paper,we propose a representative model based algorithm to calculate maximal contractions.For a formal theory Γ and a fact set Δ,the algorithm begins by accepting all models of refutation by facts of Γ with respect to Δ and filters these models to obtain the models of R-refutation.According to the completeness of R-calculus,the relevant maximal contraction is obtained simultaneously.In order to improve the efficiency,we divide the models into different classes according to the assignments of atomic propositions and only select relevant representative models to verify the satisfiability of each proposition.The algorithm is correct,and all maximal contractions of a given problem can be calculated by it.Users could make a selection according to their requirements.A benchmark algorithm is also provided.Experiments show that the algorithm has a good performance on normal revision problems.     

Fault—Tolerant Tree—Based Multicasting in Mesh Multicomputers

We propose a fault-tolerant tree-based multicast algorithm for 2-dimensional (2-D) meshes based on the concept of the extended safety level which is a vector associated with each node to capture fault information in the neighborhood.In this approach each destination is reached through a minimum number of hops,In order to minimize the total number of traffic steps,three heuristic strategies are proposed.This approach can be easily implemented by pipelined circuit switching(PCS).A simulation study is conducted to measure the total number of traffic steps under different strategies.Our approach is the first attempt to address the faulttolerant tree-based multicast strategies.Our approach is the first attempt to address the faulttolerant tree-based multicast problem in 2-D meshes based on limited global information with a simple model and succinct information.     

Trust-Based Personalized Service Recommendation: A Network Perspective简

Recent years have witnessed a growing trend of Web services on the Interact. There is a great need of effective service recommendation mechanisms. Existing methods mainly focus on the properties of individual Web services （e.g., func- tional and non-functional properties） but largely ignore users＇ views on services, thus failing to provide personalized service recommendations. In this paper, we study the trust relationships between users and Web services using network modeling and analysis techniques. Based on the findings and the service network model we build, we then propose a collaborative filtering algorithm called Trust-Based Service Recommendation （TSR） to provide personalized service recommendations. This systematic approach for service network modeling and analysis can also be used for other service recommendation studies.     

A taxonomic framework for autonomous service management in Service-Oriented Architecture

Since Service-Oriented Architecture （SOA） reveals the black box nature of services, heterogeneity, service dynamism, and service evolvability, managing services is known to be a challenging problem. Autonomic computing （AC） is a way of designing systems that can manage themselves without direct human intervention. Hence, applying the key disciplines of AC to service management is appealing. A key task of service management is to identify probable causes for symptoms detected and to devise actuation methods that can remedy the causes. In SOA, there are a number of target elements for service remedies, and there can be a number of causes associated with each target element. However, there is not yet a comprehensive taxonomy of causes that is widely accepted. The lack of cause taxonomy results in the limited possibility of remedying the problems in an autonomic way. In this paper, we first present a meta-model, extract all target elements for service fault management, and present a computing model for autonomously managing service faults. Then we define fault taxonomy for each target element and inter-relationships among the elements. Finally, we show prototype implementation using cause taxonomy and conduct experi- ments with the prototype for validating its applicability and effectiveness.     

多因素集成的分布式应用故障诊断方法

采用主动探测的方法对分布式应用进行故障管理。针对分布式应用的特点,在主动探测的故障检测和故障诊断阶段中,分别提出基于成本效益平衡的检测集选择算法和诊断集中考虑探测成本的最小贪婪搜索算法。实验结果表明,算法在探针数量、探测时间、探测流量上都有明显的改进。     

一种动态环境下的互联网服务故障诊断算法

服务环境中的动态性会对故障诊断算法性能造成影响.为了降低这种影响,分析了服务环境中的动态性,提出多层管理模型建模服务系统:二分贝叶斯网络建立依赖模型和二元对称信道建模噪声.针对故障自动修复机制导致的动态故障集环境,在故障持续时间统计的基础上修正当前窗口内先验故障概率;针对动态模型环境,基于当前窗口内原始模型和观察症状时间建立期望模型.仿真结果显示,算法可以有效地诊断动态环境下的互联网服务故障.     

Internet服务故障管理:分层模型和算法

在基于时间窗口的Internet服务故障管理中,时间窗口大小设置不合适会给算法准确度带来影响.为了降低这种影响,分析了Internet服务故障管理中存在的问题,提出了分层故障管理模型,采用图论技术进行故障诊断,选择二分图作为各层的故障传播模型.提出了基于时间窗口的故障诊断算法--多窗口故障诊断(multi-window fault diagnosis,简称MFD),该算法通过综合考虑相邻时间窗口之间的关联关系,在一定程度上降低了因时间窗口大小设置不合适而给算法准确度带来的影响.仿真结果证明了MFD算法的有效性和效率.     

Adaptive diagnosis in distributed systems

Real-time problem diagnosis in large distributed computer systems and networks is a challenging task that requires fast and accurate inferences from potentially huge data volumes. In this paper, we propose a cost-efficient, adaptive diagnostic technique called active probing . Probes are end-to-end test transactions that collect information about the performance of a distributed system. Active probing uses probabilistic reasoning techniques combined with information-theoretic approach, and allows a fast online inference about the current system state via active selection of only a small number of most-informative tests. We demonstrate empirically that the active probing scheme greatly reduces both the number of probes (from 60% to 75% in most of our real-life applications), and the time needed for localizing the problem when compared with nonadaptive (preplanned) probing schemes. We also provide some theoretical results on the complexity of probe selection, and the effect of "noisy" probes on the accuracy of diagnosis. Finally, we discuss how to model the system's dynamics using dynamic Bayesian networks (DBNs), and an efficient approximate approach called sequential multifault; empirical results demonstrate clear advantage of such approaches over "static" techniques that do not handle system's changes.     

一种改进的WSN异常检测和定位算法研究

异常快速检测和定位对于保证无线传感器网络的有效运行具有重要作用.提出了一种改进的传感器网络异常检测和定位方法.该方法通过两个阶段的探查来收集端到端测量数据以实现异常检测和定位.第1阶段探查的目的是选择可以覆盖最大数量异常链路的探点,缩小可疑区域范围,供第2阶段探查,这一阶段的探点选择问题被建模为预算有限条件下的覆盖范围最大化问题,提出一种基于对偶线性规划的高效近似方法来求解此问题.第2阶段的目的是以最小的通信代价,定位出导致观察到的端到端异常现象的具体链路,并根据多环置信度传播算法(LBP)采预测诊断质量.在不同网络设置下展开实验,结果表明,相比于精确求解方法,提出的算法性能略有下降但运行速度更快.     

分布式网络故障监测的探针部署方法

在基于主动探针的网络故障管理中,首要的是确定监测探针的部署.不确定性会对监测探针的选取带来影响.为了降低这种影响,采用概率依赖模型为主动探针选取路由,提出了不确定环境下的监测探针部署方案.利用该方案在网络中部署探针对网络进行主动探测,能以较小的代价尽早定位网络中的故障.实验表明本文的探针选择算法可以有效节约探针开销,且...     

网络中多节点故障定位的探测路径选择算法

针对现有故障定位技术不能满足多节点故障定位的要求,尤其当网络中存在大量故障节点时,提出了一种基于主动探测的探测路径选择算法。该算法主要包括用于故障检测的贪婪路径选择算法和用于故障定位的禁忌链路搜索算法。在故障检测阶段,使用贪婪路径选择算法迭代地选择具有最小权重的探测路径覆盖网络中的节点。在故障定位阶段,使用禁忌链路搜索算法多次生成候选路径集以选择最合适的探测路径来解决多节点故障定位问题。在随机网络拓扑和真实网络拓扑上的仿真结果表明,与现有的节点故障定位算法相比,探测路径选择算法具有更高的成功定位率和更低的探测成本。     

一种基于贝叶斯网络的集成的故障定位模型

故障管理是网络管理中最基本也是最重要的功能,目的是保证网络能够连续可靠地运行。故障管理可以分为两个主要的部分:故障检测和故障定位。其中故障定位是核心与难点。文中介绍了一种新的在症状收集时结合被动测试与主动探测,集成了被动诊断对网络正常的通信的影响较小以及主动探测方法可以快速有效地标识故障的优点。在诊断时采用贝叶斯网络来表示症状与故障之间的因果关系,利用不确定推理方法进行故障定位的模型。该模型包括故障推理、逼真度验证、动作选择三个模块。     

基于分层模型的组合服务故障定位算法

在面向服务的架构中,服务之间的依赖关系具有单向性的特点。基于这种单向性依赖,提出了服务故障传播的分层模型并设计了服务的分层算法,将服务节点分层。根据分层模型设计了服务故障定位的监测探针和诊断部署,减少了监测探针的数目。在探针探测结果的基础上,提出了分层模型下基于贝叶斯网络的故障定位算法,该算法通过计算故障发生时服务的影响因子和可信度,快速定位故障。仿真结果验证了该算法可以较准确地定位组合服务中的故障,且保持较低的误报率。     

有干扰的控制系统故障可诊断性量化评估

故障可诊断性的量化指标在控制系统设计过程中极为重要.为此,我们提出了一种适用于线性动态系统的故障可诊断性量化评估方法.考虑到过程和观测噪声等干扰因素对评估结果正确性的影响,我们采用等价空间方法获取系统输入/输出与故障之间的解析冗余关系,将故障可诊断性评估问题转化为概率统计中多元分布的差异度判别问题.引入巴氏系数(Bhattacharyya coefficient,BC)对多元分布之间的差异度进行量化,通过严格的数学证明得到可诊断性量化指标,并给出具体评估流程.以卫星姿态控制系统为仿真算例,将本文所提评估方法应用于该系统;仿真结果表明:该方法能够在不依赖于任何诊断算法的前提下,定量分析故障诊断的难易程度.