Providing decision support for replenishment operations using a genetic algorithms based fuzzy system

Owing to the shockwaves brought by the recent financial tsunami, most enterprises are facing tremendous challenges in maintaining the good liquidity of their own companies. In order to sustain a desirable level of cash flow for expanding business, inventory needs to be well organized because unnecessary inventory that ties up the capital in the business would prevent the enterprises from making investments. Because the existing approaches to replenishment are inflexible and unsophisticated, a new customer‐based responsive replenishment system embracing online analytical processing, fuzzy logic and genetic algorithm is proposed in this paper. This system could determine accurate and realistic order quantities based on all possible and relevant variables that affect the order quantity for each item that needs to be replenished. Once the quantity has been accurately identified, the company can increase the level of customer satisfaction while minimizing stocks. Furthermore, rather than static rule repositioning, the proposed dynamic rule refining ability makes the replenishment system self‐ameliorating by using genetic algorithm to investigate the possible fuzzy rule candidates for a more accurate inventory management model. A study has been conducted in a case company for the validation of the feasibility of the proposed system. After performing a spatial analysis, the results obtained indicate that the proposed responsive replenishment system is capable of ensuring improved inventory control performance in the case company.     

SAaaS: a cloud computing service model using semantic‐based agent

Cloud computing, a common business model, provides cloud resources on demand to consumers over the Internet. However, because cloud computing lacks a uniform method of representing knowledge, which can offer customers a comprehensive solution for managing and developing cloud applications, cloud computing has low reuse potential. This work proposes a Semantic Agent as a Service (SAaaS), which was developed using Unified Modeling Language modelling. The SAaaS architecture is based on research into Cloud Computing, Semantic Web and Multi‐Agent Systems. The architecture can be combined with existing cloud service models, such as Software as a Service, Platform as a Service and Infrastructure as a Service, to design intelligent cloud computing applications. To demonstrate the efficacy of SAaaS, a Semantic‐based Project Resources Sharing Platform, an intelligent cloud computing application based on the SAaaS framework, is implemented to provide project resources on demand, consistent with the needs of project members.     

On a responsive replenishment system: a fuzzy logic approach

In today's competitive business environment, it is important that customers are able to obtain their preferred items in the shops they visit, particularly for convenience store chains such as 7–Eleven where popular items are expected to be readily available on the shelves of the stores for buyers. To minimize the cost of running such store chains, it is essential that stocks be kept to a minimum and at the same time large varieties of popular items are available for customers. In this respect, the replenishment system needs to be able to cope with the taxing demands of minimal inventory but at the same time keeping large varieties of needed items. This paper proposes a replenishment system which is able to respond to the fluctuating demands of customers and provide a timely supply of needed items in a cost–effective way. The proposed system embraces the principle of fuzzy logic which is able to deal with uncertainties by virtue of its fuzzy rules reasoning mechanism, thereby leveraging the responsiveness of the entire replenishment system for the chain stores. To validate the feasibility of the approach, a case study has been conducted in an emulated environment with promising results.     

Using a Trust Inference Model for Flexible and Controlled Information Sharing During Crises

The fluid, urgent nature of crises requires flexible, responsive information sharing. Recent studies show, however, that in business catastrophes and other kinds of crises conventional access control mechanisms favor security over flexibility. Our work addresses these seemingly contradictory needs for security and flexibility and designs a trust inference model based on fuzzy logic, a model that can be used with pervasive computing technologies using sensors and mobile devices. Drawing upon research on trust, we design a trust inference model using attributes of affiliation, task performance, and urgency; apply the model to a known crisis; discuss implementation issues; and explore issues for further research. This article is dedicated to Alan Jarman, a founding influence in the Journal of Contingencies and Crisis Management who died in Canberra 15 July 2010. Alan's quantitative, engineering background and his long standing commitment to improving crisis decision making prompted him to encourage our applying fuzzy logic to crisis information sharing. We are grateful for Alan's encouragement and advice.     

Towards an accurate evaluation of quality of cloud service in service-oriented cloud computing

Cloud computing promises to provide high quality, on-demand services with service-oriented architecture. However, cloud service typically come with various levels of services and performance characteristics, which makes Quality of Cloud Service (QoCS) high variance. Hence, it is difficult for the users to evaluate these cloud services and select them to fit their QoCS requirements. In this paper, we propose an accurate evaluation approach of QoCS in service-oriented cloud computing. We first employ fuzzy synthetic decision to evaluate cloud service providers according to cloud users’ preferences and then adopt cloud model to computing the uncertainty of cloud services based on monitored QoCS data. Finally, we obtain the evaluation results of QoCS using fuzzy logic control. The simulation results demonstrate that our proposed approach can perform an accurate evaluation of QoCS in service-oriented cloud computing.     

Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities, and length. Second, a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task scheduling. In the end, a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment. A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the PACO. Thus, the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time, system throughput, and maximizing resource utilization.     

Semantic-aware multi-tenancy authorization system for cloud architectures

Cloud computing is an emerging paradigm to offer on-demand IT services to customers. The access control to resources located in the cloud is one of the critical aspects to enable business to shift into the cloud. Some recent works provide access control models suitable for the cloud; however there are important shortages that need to be addressed in this field. This work presents a step forward in the state-of-the-art of access control for cloud computing. We describe a high expressive authorization model that enables the management of advanced features such as role-based access control (RBAC), hierarchical RBAC (hRBAC), conditional RBAC (cRBAC) and hierarchical objects (HO). The access control model takes advantage of the logic formalism provided by the Semantic Web technologies to describe both the underlying infrastructure and the authorization model, as well as the rules employed to protect the access to resources in the cloud. The access control model has been specially designed taking into account the multi-tenancy nature of this kind of environment. Moreover, a trust model that allows a fine-grained definition of what information is available for each particular tenant has been described. This enables the establishment of business alliances among cloud tenants resulting in federation and coalition agreements. The proposed model has been validated by means of a proof of concept implementation of the access control system for OpenStack with promising performance results.     

基于“云计算”的数字电视业务管理系统的研究

针对广电系统中数字电视媒体在业务的持续发展和广电系统运营维护管理方面的各种困境,综合了基于服务的SOA系统架构和"云计算"技术的商业运行模式,运用"云计算"的虚拟化、分布式、并行计算等技术,提出了基于"云计算"技术开发数字电视业务管理系统的方法,为今后广电行业实施的三网融合提供了一种新的设计思路。结合某市数字电视业务云架构的解决方案,说明"云计算"在广电系统构建数字电视业务管理系统中的应用。     

基于云计算的IPTV业务平台架构设计及应用

随着云计算技术的飞速发展,特别是虚拟化技术、分布式存储系统的引入,催生了各种各样的服务及应用.本文首先介绍云计算的关键技术,包括虚拟化技术、分布式存储系统、分布式数据库.提出基于云计算的IPTV业务平台系统架构,分析其功能、技术优势及发展趋势.引入云计算技术,使得IPTV业务平台具有高可靠性、动态负载均衡能力和良好的扩展性,满足其业务快速增长的需求.     

一种云计算数字签名技术的研究与实现

由于云计算具有可靠性高、成本低、性能高等特点,已经成为了新一代信息技术变革的核心。为了能够有效推动互联网安全架构的可持续性发展。本文提出了一种云计算数字签名技术,该技术有机结合了云计算技术与数字签名技术。首先阐述了数字签名系统中应用云计算技术的价值,其次,分析了云计算数字签名技术的实现模型,并且设计了云计算数字签名协议,最后,开展了实例分析,将云计算数字签名技术应用到移动营业厅的业务信息交易中。结果表明：所有业务均可在网上完成,那么既可大大方便用户,又可降低移动营业厅的工作量,还可确保业务信息交易的可靠性、唯一性、真实性、安全性与不可抵赖性。通过结果分析得出结论：云计算数字签名技术既可保障签名文件的完整性与真实性,又可跨平台操作签名文件,还可让数字签名模型利用互联网来对密码运算基础设施进行便捷化、可靠化地访问。     

基于云计算的校园网综合数据服务平台设计

随着高校教育信息化建设的不断深入,校园网中的各种软硬件资源越来越多,同时用户对基于"云"+"端"的服务需求日益迫切,有效整合资源,增强业务协同,就成为了校园网服务工作的重中之重。在充分考虑到高校基于"云"+"端"服务的需求基础上,综合运用云计算技术,设计了一个基于云计算的校园网综合数据服务平台。     

A Transformation-Based Approach to Business Process Management in the Cloud

Business Process Management (BPM) has gained a lot of popularity in the last two decades, since it allows organizations to manage and optimize their business processes. However, purchasing a BPM system can be an expensive investment for a company, since not only the software itself needs to be purchased, but also hardware is required on which the process engine should run, and personnel need to be hired or allocated for setting up and maintaining the hardware and the software. Cloud computing gives its users the opportunity of using computing resources in a pay-per-use manner, and perceiving these resources as unlimited. Therefore, the application of cloud computing technologies to BPM can be extremely beneficial specially for small and middle-size companies. Nevertheless, the fear of losing or exposing sensitive data by placing these data in the cloud is one of the biggest obstacles to the deployment of cloud-based solutions in organizations nowadays. In this paper we introduce a transformation-based approach that allows companies to control the parts of their business processes that should be allocated to their own premises and to the cloud, to avoid unwanted exposure of confidential data and to profit from the high performance of cloud environments. In our approach, the user annotates activities and data that should be placed in the cloud or on-premise, and an automated transformation generates the process fragments for cloud and on-premise deployment. The paper discusses the challenges of developing the transformation and presents a case study that demonstrates the applicability of the approach.     

云计算环境下Web服务集成系统的研究

作为云平台提升应用性能的一种重要手段,Web服务集成技术近年来受到了工业界和学术界的广泛关注。从云计算与Web服务集成技术的结合入手,分析设计了基于云计算的系统体系结构,并基于此提出了基于Web Ser-vices的异构数据集成方法和应用集成的总体架构。最后,给出了该系统的相关实现实例。实验表明,该系统架构的应用在降低构建成本的同时大幅提高了系统性能。     

Performance analysis based resource allocation for green cloud computing

Cloud computing has become a new computing paradigm that has huge potentials in enterprise and business. Green cloud computing is also becoming increasingly important in a world with limited energy resources and an ever-rising demand for more computational power. To maximize utilization and minimize total cost of the cloud computing infrastructure and running applications, resources need to be managed properly and virtual machines shall allocate proper host nodes to perform the computation. In this paper, we propose performance analysis based resource allocation scheme for the efficient allocation of virtual machines on the cloud infrastructure. We experimented the proposed resource allocation algorithm using CloudSim and its performance is compared with two other existing models.     

From cloud computing to cloud manufacturing

Cloud computing is changing the way industries and enterprises do their businesses in that dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a brand new opportunity for enterprises. In this paper, some of the essential features of cloud computing are briefly discussed with regard to the end-users, enterprises that use the cloud as a platform, and cloud providers themselves. Cloud computing is emerging as one of the major enablers for the manufacturing industry; it can transform the traditional manufacturing business model, help it to align product innovation with business strategy, and create intelligent factory networks that encourage effective collaboration. Two types of cloud computing adoptions in the manufacturing sector have been suggested, manufacturing with direct adoption of cloud computing technologies and cloud manufacturing—the manufacturing version of cloud computing. Cloud computing has been in some of key areas of manufacturing such as IT, pay-as-you-go business models, production scaling up and down per demand, and flexibility in deploying and customizing solutions. In cloud manufacturing, distributed resources are encapsulated into cloud services and managed in a centralized way. Clients can use cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all other stages of a product life cycle.     

Privacy-preserving data utilization in hybrid clouds

As cloud computing becomes prevalent, more and more sensitive data is being centralized into the cloud, which raises a new challenge on how to utilize the outsourced data in a privacy-preserving manner. Although searchable encryption allows for privacy-preserving keyword search over encrypted data, it could not work effectively for restricting unauthorized access to the outsourced private data. In this paper, aiming at tackling the challenge of privacy-preserving utilization of data in cloud computing, we propose a practical hybrid architecture in which a private cloud is introduced as an access interface between the data owner/user and the public cloud. Under this architecture, a data utilization system is provided to achieve both exact keyword search and fine-grained access control over encrypted data. Security and efficiency analysis for the proposed system are presented in detail. Then, further enhancements for this system are considered in two steps. (1) We show how to extend our system to support efficient fuzzy keyword search while overcoming the disadvantage of insignificant decryption in the existing privacy-preserving fuzzy keyword search scheme. (2) We demonstrate approaches to realize an outsourcing cryptographic access control mechanism and further reduce the computational cost at the data user side.     

An integrated fuzzy-GA approach for buffer management

This paper deals with a novel buffer management scheme based on evolutionary computing for shared-memory asynchronous transfer mode (ATM) switches. The philosophy behind it is adaptation of the threshold for each logical output queue to the real traffic conditions by means of a system of fuzzy inferences. The optimal fuzzy system is achieved using a systematic methodology, based on genetic algorithms (GAs), which allows the fuzzy system parameters to be derived for each switch size, offering a high degree of scalability to the fuzzy control system. Its performance is comparable to that of the push-out (PO) mechanism, which can be considered ideal from a performance viewpoint, and at any rate much better than that of threshold schemes based on conventional logic. In addition, the fuzzy threshold (FT) scheme is simple and cost-effective when implemented using VLSI technology.     

A scheduling algorithm for applications in a cloud computing system with communication changes

This paper proposes a scheduling algorithm to solve the problem of task scheduling in a cloud computing system with time‐varying communication conditions. This algorithm converts the scheduling problem with communication changes into a directed acyclic graph (DAG) scheduling problem for existing fuzzy communication task nodes, that is, the scheduling problem for a communication‐change DAG (CC‐DAG). The CC‐DAG contains both computation task nodes and communication task nodes. First, this paper proposes a weighted time‐series network bandwidth model to solve the indefinite processing time (cost) problem for a fuzzy communication task node. This model can accurately predict the processing time of a fuzzy communication task node. Second, to address the scheduling order problem for the computation task nodes, a dynamic pre‐scheduling search strategy (DPSS) is proposed. This strategy computes the essential paths for the pre‐scheduling of the computation task nodes based on the actual computation costs (times) of the computation task nodes and the predicted processing costs (times) of the fuzzy communication task nodes during the scheduling process. The computation task node with the longest essential path is scheduled first because its completion time directly influences the completion time of the task graph. Finally, we demonstrate the proposed algorithm via simulation experiments. The experimental results show that the proposed DPSS produced remarkable performance improvement rate on the total execution time that ranges between 11.5% and 21.2%. In view of the experimental results, the proposed algorithm provides better quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment than HEFT, PEFT, and CEFT algorithms.     

基于业务流程的制造云服务组合模型

为了提高云制造系统中制造云服务的组合成功率,实现组合云服务与用户业务需求的准确匹配,在对制造云服务、流程节点任务、云服务的可组合性和流程匹配进行形式化描述的基础上,提出一种基于业务流程的制造云服务组合模型。该模型由业务流程引擎、业务流程、选择逻辑、评估逻辑、监控逻辑、知识库和原子云服务集构成,在功能匹配的基础上,对候选服务的可组合性进行检查,结合负载、服务质量(QoS)和业务流程信息,选择合适的云服务,并将其挂接在业务流程上实现制造云服务的组合。对制造云服务的组合流程进行了详细描述,并给出云服务组合的实现方法。实例分析表明,该模型能够有效地选择满足业务需求的云服务实体并进行组合,从而提高制造云服务的组合成功率,保障用户制造活动的顺利进行。