Service task partition and distribution in star topology computer grid subject to data security constraints

The paper considers grid computing systems in which the resource management systems (RMS) can divide service tasks into execution blocks (EBs) and send these blocks to different resources. In order to provide a desired level of service reliability the RMS can assign the same blocks to several independent resources for parallel execution.The data security is a crucial issue in distributed computing that affects the execution policy. By the optimal service task partition into the EBs and their distribution among resources, one can achieve the greatest possible service reliability and/or expected performance subject to data security constraints. The paper suggests an algorithm for solving this optimization problem. The algorithm is based on the universal generating function technique and on the evolutionary optimization approach. Illustrative examples are presented.     

Service reliability and performance in grid system with star topology

The paper considers grid computing systems in which the resource management systems (RMS) can divide service tasks into subtasks and send the subtasks to different resources for parallel execution. In order to provide desired level of service reliability the RMS can assign the same subtasks to several independent resources for parallel execution.The service reliability and performance indices are introduced and a fast numerical algorithm for their evaluation for arbitrary subtask distribution in grid with star architecture is presented. This algorithm is based on the universal generating function technique.Illustrative examples are presented.     

Performance and reliability of a star topology grid service with data dependency and two types of failure

The paper considers grid computing systems in which the Resource Management System (RMS) divides a service task into subtasks and then sends the subtasks to different specialized resources for execution. In order to provide the desired level of service reliability the RMS can assign the same subtasks to several independent resources for parallel execution. Because of data dependency, some subtasks cannot be executed until they have received input data that is the result of other subtasks. This imposes precedence constraints on the order of subtask execution. Usually, there are two types of failure in resources and communication channels: permanent failures and transient failures. Permanent failures render the equipment unavailable and whilst transient failures do not make the equipment unavailable they do prevent the completion of the performed actions. Service reliability and performance indices are introduced and a fast numerical algorithm for their evaluation for any given subtask distribution in a grid with a star architecture is presented. This algorithm is based on the universal generating function technique. Illustrative examples are presented.     

Optimal resource allocation on grid systems for maximizing service reliability using a genetic algorithm

Grid computing system is different from conventional distributed computing systems by its focus on large-scale resource sharing and open architecture for services. The global grid technologies and the Globus Toolkit in particular, are evolving toward an open grid service architecture (OGSA) with which a grid system provides an extensible infrastructure so that various organizations can offer their own services and integrate their resources. Hence, this paper aims at solving the problem of optimally allocating services on the grid to maximize the grid service reliability. Since no existing study has analyzed the grid service reliability, this paper develops initial modeling and evaluation algorithms to evaluate the grid service reliability. Based on the grid service reliability evaluation, we present an optimization model for the grid service allocation problem and develop a genetic algorithm (GA) to effectively solve it. A numerical example is given to show the modeling procedures and efficiency of the GAs.     

Optimal structure of fault-tolerant software systems

This paper considers software systems consisting of fault-tolerant components. These components are built from functionally equivalent but independently developed versions characterized by different reliability and execution time. Because of hardware resource constraints, the number of versions that can run simultaneously is limited. The expected system execution time and its reliability (defined as probability of obtaining the correct output within a specified time) strictly depend on parameters of software versions and sequence of their execution. The system structure optimization problem is formulated in which one has to choose software versions for each component and find the sequence of their execution in order to achieve the greatest system reliability subject to cost constraints. The versions are to be chosen from a list of available products. Each version is characterized by its reliability, execution time and cost.The suggested optimization procedure is based on an algorithm for determining system execution time distribution that uses the moment generating function approach and on the genetic algorithm. Both N-version programming and the recovery block scheme are considered within a universal model. Illustrated example is presented.     

分布式工作流执行站点多目标随机决策

针对面向服务的企业(SOE)环境下工作流在分布式执行性能方面存在的不足,提出了一种基于随机决策的动态(运行时)执行站点优化方法。该方法以扩展服务工作流网,增加软约束和硬约束作为站点优化的基础,对环境的动态性和服务随机性进行分析,针对分片的四种结构,以组合服务质量最优为目标,建立多目标动态执行站点随机线性规划模型,并进行求解。应用实例验证了该方法的可用性。与现有工作相比,所提出的算法使工作流执行性能得到了明显提高,并消除了活动并行执行中存在的资源瓶颈。     

Allocation of test times in multi-state systems for reliability growth testing

This paper generalizes a reliability growth test allocation problem to series-parallel multi-state systems. An algorithm, which determines the testing time for each system element in order to maximize the entire system reliability when total testing resources are limited, is suggested. The algorithm can handle both repairable and non-repairable multi-state systems. The Crow/AMSAA reliability growth model is used to evaluate the influence of testing time on the reliability of the elements composing the system. System reliability is defined as the ability of the system to satisfy variable demand represented by a cumulative demand curve. To evaluate multi-state system reliability, a universal generating function technique is applied. A Genetic Algorithm (GA) is used as an optimization technique. The basic GA procedures adapted to the given problem are presented. Examples of the determination of reliability growth test plans are demonstrated.     

云制造环境下板材余料资源的服务匹配方法

在云制造环境下,针对中小型企业加工后的板材余料造成极大资源浪费的问题,提出了一种基于语义相似度算法同时融入QoS(quality of service,服务质量)信息的分层次资源描述模型.首先,建立板材余料和板材加工设备资源的本体模型;然后,基于语义相似度算法对板材余料和板材加工设备的各个属性参数进行匹配,得到初选服务...     

Reliability and performance analysis of hardware–software systems with fault-tolerant software components

This paper presents an algorithm for evaluating reliability and expected execution time for systems consisting of fault-tolerant software components running on several hardware units. The components are built from functionally equivalent but independently developed versions characterized by different reliability and execution time. Different number of versions can be executed simultaneously depending on the number of available units. The system reliability is defined as the probability that the system produces a correct output in a specified time.     

Reliability and performance analysis for fault-tolerant programs consisting of versions with different characteristics

This paper presents a simple straightforward algorithm for evaluating reliability and expected execution time for software systems consisting of fault-tolerant components. The components are built from functionally equivalent but independently developed versions characterized by different reliability and performance. Both N-version programming (with parallel and sequential execution of the versions) and the recovery block scheme are considered within a universal model.     

Structure optimization of multi-state system with two failure modes

When systems with two failure modes (STFM) are considered, introducing redundant elements may either increase or decrease system reliability. Therefore the problem of system structure optimization arises. In this paper we consider systems consisting of elements characterized by different reliability and nominal performance rates. Such systems are multi-state because they can have different levels of output performance depending on the combination of elements available at the moment. The algorithm that determines the structure of multi-state STFM, which maximizes system reliability and/or expected performance is presented. In this algorithm, system elements are chosen from a list of available equipment. Reliability is defined as the probability of satisfaction of given constraints imposed on system performance in both modes.The procedure developed to solve this problem is based on the use of a universal moment generating function (UMGF) for the fast evaluation of multi-state system reliability and a genetic algorithm for optimization. Basic UMGF technique operators are developed for two different types of systems, based, respectively, on transmitting capacity and on processing time. Examples of the optimization of series–parallel structures of both types are presented.     

Performance distribution of a fault-tolerant system in the presence of failure correlation

This paper considers software systems consisting of fault-tolerant components built from functionally equivalent but independently developed modules (versions) characterized by different reliability and execution times. The components are designed using either the N-version programming method or the recovery block scheme. In our general model, we also allow the number of versions that can run simultaneously to be limited because of hardware or computation time constraints. An analytical algorithm and a numerical procedure to evaluate system execution-time distributions are presented. This algorithm takes into account the positive correlation among failures in different versions by introducing common-cause failures (mutually exclusive and independent common causes are considered). Illustrative examples are presented.     

一种基于Petri网的网络服务兼容性分析方法

针对目前基于网络服务业务过程执行语言(BPEL)的网络服务合成方法缺乏网络服务的兼容性分析机制,不能保证基于网络服务技术的业务过程正确执行的问题,建立了一种能从形式化的角度描述网络服务的接口交互情况的基于Petri网的多元工作流组合网模型,提出了一种能更快地获得网络服务可用性结果的网络服务兼容性判断算法.最后,提出了基于该算法的原型系统架构.     

Cloud Security Service for Identifying Unauthorized User Behaviour

Recently, an innovative trend like cloud computing has progressed quickly in Information Technology. For a background of distributed networks, the extensive sprawl of internet resources on the Web and the increasing number of service providers helped cloud computing technologies grow into a substantial scaled Information Technology service model. The cloud computing environment extracts the execution details of services and systems from end-users and developers. Additionally, through the system’s virtualization accomplished using resource pooling, cloud computing resources become more accessible. The attempt to design and develop a solution that assures reliable and protected authentication and authorization service in such cloud environments is described in this paper. With the help of multi-agents, we attempt to represent Open-Identity (ID) design to find a solution that would offer trustworthy and secured authentication and authorization services to software services based on the cloud. This research aims to determine how authentication and authorization services were provided in an agreeable and preventive manner. Based on attack-oriented threat model security, the evaluation works. By considering security for both authentication and authorization systems, possible security threats are analyzed by the proposed security systems.     

A new efficient algorithm for computing the imprecise reliability of monotone systems

Reliability analysis of complex systems by partial information about reliability of components and by different conditions of independence of components may be carried out by means of the imprecise probability theory which provides a unified framework (natural extension, lower and upper previsions) for computing the system reliability. However, the application of imprecise probabilities to reliability analysis meets with a complexity of optimization problems which have to be solved for obtaining the system reliability measures. Therefore, an efficient simplified algorithm to solve and decompose the optimization problems is proposed in the paper. This algorithm allows us to practically implement reliability analysis of monotone systems under partial and heterogeneous information about reliability of components and under conditions of the component independence or the lack of information about independence. A numerical example illustrates the algorithm.     

顾客服务系统的排队模型及其分析

运用排队理论,对虚拟企业售后服务部门的顾客排队网络进行了分析,确定了一种顾客具有优先权的多级顾客服务排除模型,以顾客满意度（服务合同相关条款）为条件计算出了稳定平衡状态下排队系统的主要指标,并从收益、成本和效率等方面进行优化,从而得到服务部门最优基准服务能力,对服务能力的有效安排和组织具有重要的指导意义。     

A new approach to solving problems of multi‐state system reliability optimization

Usually engineers try to achieve the required reliability level with minimal cost. The problem of total investment cost minimization, subject to reliability constraints, is well known as the reliability optimization problem. When applied to multi‐state systems (MSS), the system has many performance levels, and reliability is considered as a measure of the ability of the system to meet the demand (required performance). In this case, the outage effect will be essentially different for units with different performance rate. Therefore, the performance of system components, as well as the demand, should be taken into account. In this paper, we present a technique for solving a family of MSS reliability optimization problems, such as structure optimization, optimal expansion, maintenance optimization and optimal multistage modernization. This technique combines a universal generating function (UGF) method used for fast reliability estimation of MSS and a genetic algorithm (GA) used as an optimization engine. The UGF method provides the ability to estimate relatively quickly different MSS reliability indices for series‐parallel and bridge structures. It can be applied to MSS with different physical nature of system performance measure. The GA is a robust, universal optimization tool that uses only estimates of solution quality to determine the direction of search. Copyright © 2001 John Wiley & Sons, Ltd.     

A Parallel Strategy for the Logical‐probabilistic Calculus‐based Method to Calculate Two‐terminal Reliability

The theory of network reliability has been applied to many complicated network structures, such as computer and communication networks, piping systems, electricity networks, and traffic networks. The theory is used to evaluate the operational performance of networks that can be modeled by probabilistic graphs. Although evaluating network reliability is an Non‐deterministic Polynomial‐time hard problem, numerous solutions have been proposed. However, most of them are based on sequential computing, which under‐utilizes the benefits of multi‐core processor architectures. This paper addresses this limitation by proposing an efficient strategy for calculating the two‐terminal (terminal‐pair) reliability of a binary‐state network that uses parallel computing. Existing methods are analyzed. Then, an efficient method for calculating terminal‐pair reliability based on logical‐probabilistic calculus is proposed. Finally, a parallel version of the proposed algorithm is developed. This is the first study to implement an algorithm for estimating terminal‐pair reliability in parallel on multi‐core processor architectures. The experimental results show that the proposed algorithm and its parallel version outperform an existing sequential algorithm in terms of execution time. Copyright © 2015 John Wiley & Sons, Ltd.     

Application of genetic algorithms with dominant genes in a distributed scheduling problem in flexible manufacturing systems

Multi-factory production networks have increased in recent years. With the factories located in different geographic areas, companies can benefit from various advantages, such as closeness to their customers, and can respond faster to market changes. Products (jobs) in the network can usually be produced in more than one factory. However, each factory has its operations efficiency, capacity, and utilization level. Allocation of jobs inappropriately in a factory will produce high cost, long lead time, overloading or idling resources, etc. This makes distributed scheduling more complicated than classical production scheduling problems because it has to determine how to allocate the jobs into suitable factories, and simultaneously determine the production scheduling in each factory as well. The problem is even more complicated when alternative production routing is allowed in the factories. This paper proposed a genetic algorithm with dominant genes to deal with distributed scheduling problems, especially in a flexible manufacturing system (FMS) environment. The idea of dominant genes is to identify and record the critical genes in the chromosome and to enhance the performance of genetic search. To testify and benchmark the optimization reliability, the proposed algorithm has been compared with other approaches on several distributed scheduling problems. These comparisons demonstrate the importance of distributed scheduling and indicate the optimization reliability of the proposed algorithm.