A task‐based and resource‐aware approach to dynamically generate optimal software architecture for intelligent service robots

Intelligent service robots provide various services to users by understanding the context and goals of a user task. In order to provide more reliable services, intelligent service robots need to consider various factors, such as their surrounding environments, users' changing needs, and constrained resources. To handle these factors, most of the intelligent service robots are controlled by a task‐based control system, which generates a task plan that represents a sequence of actions, and executes those actions by invoking the corresponding functions. However, the traditional task‐based control systems lack the consideration of resource factors even though intelligent service robots have limited resources (limited computational power, memory space, and network bandwidth). Moreover, system‐specific concerns such as the relationships among functional modules are not considered during the task generation phase. Without considering both the resource conditions and interdependencies among software modules as a whole, it will be difficult to efficiently manage the functionalities that are essential to provide core services to users. In this paper, we propose a mechanism for intelligent service robots to efficiently use their resources on‐demand by separating system‐specific information from task generation. We have defined a sub‐architecture that corresponds to each action of a task plan, and provides a way of using the limited resources by minimizing redundant software components and maintaining essential components for the current action. To support the optimization of resource consumption, we have developed a two‐phase optimization process, which is composed of the topological and temporal optimization steps. We have conducted an experiment with these mechanisms for an infotainment robot, and simulated the optimization process. Results show that our approach contributed to increase the utilization rate by 20% of the robot resources. Copyright © 2011 John Wiley & Sons, Ltd.     

PLEIADES: An Internet‐based parallel/distributed system

The use of LAN‐based clusters of computers for computational purposes has been in use for several years with significant success and acceptability. The introduction of the Internet infrastructure as the interconnection medium of the cluster allows for additional flexibility and transparency of such systems. PLEIADES is an Internet‐based parallel/distributed system whose purpose is to allow users to use distant computational resources in order to form virtual clusters. In addition, PLEIADES can be used as a computational infrastructure service provider for applications in need of computational resources. PLEIADES uses a tiered architecture with particular emphasis on the existence of a middle tier, whose task is to assist in the communication between the interface and the resource management tiers. The existence of the middle tier allows for the creation of an open system that is able to easily integrate with new resource management platforms and tools. Since the use of a mature resource management system for parallel/distributed computing was a prerequisite of the PLEIADES architecture, the Condor resource management environment was used. The design and implementation characteristics of PLEIADES together with some experimental uses of the system are also presented. Copyright © 2002 John Wiley & Sons, Ltd.     

Towards an autonomic performance management approach for a cloud broker environment using a decomposition–coordination based methodology

Efficient resource allocation of computational resources to services is one of the predominant challenges in a cloud computing environment. Furthermore, the advent of cloud brokerage and federated cloud computing systems increases the complexity of cloud resource management. Cloud brokers are considered third party organizations that work as intermediaries between the service providers and the cloud providers. Cloud brokers rent different types of cloud resources from a number of cloud providers and sublet these resources to the requesting service providers. In this paper, an autonomic performance management approach is introduced that provides dynamic resource allocation capabilities for deploying a set of services over a federated cloud computing infrastructure by considering the availability as well as the demand of the cloud computing resources. A distributed control based approach is used for providing autonomic computing features to the proposed framework via a feedback-based control loop. This distributed control based approach is developed using one of the decomposition–coordination methodologies, named interaction balance, for interactive bidding of cloud computing resources. The primary goals of the proposed approach are to maintain the service level agreements, maximize the profit, and minimize the operating cost for the service providers and the cloud broker. The application of interaction balance methodology and prioritization of profit maximization for the cloud broker and the service providers during resource allocation are novel contributions of the proposed approach.     

虚拟行业联盟中的自动Web服务组合

在网络经济模式下,为了应对不断变化的市场,企业提出在行业内组建虚拟联盟的思想。Web服务的出现,使得一个行业内部的复杂任务可以通过Web服务的动态组合得以实现。本文提出虚拟行业联盟平台模型和Web服务动态组合的算法,考虑到Web服务组合的执行过程中的异常情况,给出了Web服务操作的语义等价概念。     

Pattern-based e-service modelling for virtual service enterprises

Business services are customisable, interactive processes that providers have the potential to carry out together with clients that benefit from the effects. It is understood that business service transactions are best organised by means of virtual networks, where information technology allows for configuring multiple providers and processes on a per-request basis. Existing conceptual models for virtual service enterprises (VSEs) propose business service virtualisation for flexible and agile regulation and control of coordination between multiple providers and clients. In this paper, we present an approach for realising business service virtualisation based on software service technology. In particular, we propose a service-oriented software architecture for representing virtual business service processes as e-services. E-service models specify flexible business service interactions between multiple providers and clients of virtual service enterprises and allow for regulation and enforcement of their coordination. We demonstrate the utilisation of our e-service SOA in the context of an e-science scenario, where we show how to design e-service models for the use case of virtual polymorph prediction laboratories.     

Operations Research Methods for Autonomic Resource Management

Varying service demands on next generation networks requires autonomic resource management capabilities to ensure service delivery to customers, motivating the need to develop self-managing algorithms for this purpose. In this paper, we use algorithms from the Operations Research community for this purpose. First, we adapt the Transportation Model to manage distributed resources in the next generation network infrastructure. Coupled with monitoring capabilities, the proposed scheme can automatically adjust the virtual resource allocation to optimize the costs incurred by service providers in offering services to customers. Second, we propose the use of Inventory Control to predict needs for virtual resources and to pre-order required virtual resource amounts. We compare two possible Inventory Control models to manage the virtual resources involved in service delivery. We perform extensive simulations to show the performance improvements made possible by the use of the Transportation Model and Inventory Control for autonomic resource management in next generation networks.     

可视化资源网格化方法

为提高可视化资源的使用效率,提出一种可视化资源网格化方法。研究网格环境下可视化资源共享的体系结构和工作模式以及可视化资源服务化封装方法。可视化服务利用帧缓存捕捉可视化程序运行屏幕并保存为图像文件,通过Web服务器传送至远程的客户端显示。用户通过客户端向可视化服务提出请求,该请求被封装为XML格式文件,包含可视化任务执行所需参数描述信息。以虚拟样机可视化虚拟环境的网格化为例,验证了该方法的有效性。     

Dynamic resource demand prediction and allocation in multi‐tenant service clouds

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and resource allocation methodology that can provision resources in advance, thereby minimizing the virtual machine downtime required for resource provisioning. In this paper, we present a dynamic resource demand prediction and allocation framework in multi‐tenant service clouds. The novel contribution of our proposed framework is that it classifies the service tenants as per whether their resource requirements would increase or not; based on this classification, our framework prioritizes prediction for those service tenants in which resource demand would increase, thereby minimizing the time needed for prediction. Furthermore, our approach adds the service tenants to matched virtual machines and allocates the virtual machines to physical host machines using a best‐fit heuristic approach. Performance results demonstrate how our best‐fit heuristic approach could efficiently allocate virtual machines to hosts so that the hosts are utilized to their fullest capacity. Copyright © 2016 John Wiley & Sons, Ltd.     

基于任务相关性的机务虚拟维修系统的场景管理

为解决机务虚拟维修训练系统中场景、模型一次性全部加载速度慢、内存占用量高的问题,基于任务的相关性提出一种场景管理方法。使用TF-IDF算法获取系统中包含的虚拟维修任务工卡的相似度并进行划分。工卡的相似度越高表示所描述的虚拟维修场景、维修工具、维修对象等虚拟资源相关性越强。当在场景资源加载、内存分配时,将相关性大于68%的任务工卡描述的虚拟资源利用伙伴系统进行加载分配,对于相关性小于42%的任务场景,则在伙伴系统中申请一块内存,并将这块内存划分为内存池进行加载分配。而任务相关性介于42%～68%的任务场景用双动态双链表的方法进行管理。解决了传统虚拟维修训练系统中加载资源时没有维修资源相关性分配管理的不足,分配方法没有任务针对性的局限,避免了单独划分内存块的系统分配时间。实验结果表明,改进后的分配方法减少了17%内存占用量,并提高了17.57的帧率。     

3-Hierarchical resource management model on web grid service architecture

In this paper, we developed a framework for efficient resource management within the grid service environment. For considering the grid service architecture and functions, the resource management is the most important to grid service; therefore, GridRMF (Grid Resource Management Framework) is modeled and developed in order to respond to such variable characteristics of resources as accordingly as possible. GridRMF uses the participation level of grid resource as a basis of its hierarchical management. This hierarchical management divides managing domains into two parts: VMS (Virtual Organization Management System) for virtual organization management and RMS (Resource Management System) for metadata management. VMS mediates resources according to optimal virtual organization selection mechanism, and responds to malfunctions of the virtual organization by LRM (Local Resource Manager) automatic recovery mechanism. RMS, on the other hand, responds to load balance and fault by applying resource status monitoring information into adaptive performance-based task allocation algorithm.

泛政府行业信息化数据跨域共享服务总线平台的设计与实现

对泛政府行业内网与其他网络间的跨网域的请求服务进行了研究,提出了泛政府行业信息化数据跨域共享服务总线平台的设计方案,底层硬件设计上基于单向光传输方式,实现不同网域间数据的安全隔离传输;上层系统软件通过服务总线（ESB）体系架构和定义统一请求调用接口,实现跨区域、跨网域间服务授权访问、资源授权调用和数据安全转发。整个系统由请求协议转换、安全检控组件、健康度评估、MQ数据服务、节点资源调度、限流熔断管理、通道任务管理、资源监控上报等模块组成,可实现请求服务、设备状态上报、通道任务管理、安全访问控制等功能,并根据不同业务量级设定不同应用模式,可满足跨网域移动警务请求服务数据资源的安全、高效传输需求。     

A semantic collaborative awareness model to deal with resource sharing in grids

Grid computing environments are set up mainly to encourage the shared use of different resources based on business/scientific needs. The way these resources are shared in terms of CPU cycles, storage capacity, software licenses etc., is normally dictated by the availability of these resources outside the local administration context. The Semantic Grid is the extension of Grid computing with Semantic Web-based technologies. The Semantic Grid represents grid management data in a machine-understandable format, and reasoning can handle complicated situations in virtual organization management. This paper presents the extension of the collaborative awareness model (CAM) to manage virtual organizations in Semantic Grid environments. CAM applies some theoretical principles of awareness models to promote resource interaction and management, as well as task delivery.     

A two-stage approach for task and resource management in multimedia cloud environment

In recent years, multimedia cloud computing is becoming a promising technology that can effectively process multimedia services and provide quality of service (QoS) provisioning for multimedia applications from anywhere, at any time and on any device at lower costs. However, there are two major challenges exist in this emerging computing paradigm: one is task management, which maps multimedia tasks to virtual machines, and the other is resource management, which maps virtual machines (VMs) to physical servers. In this study, we aim at providing an efficient solution that jointly addresses these challenges. In particular, a queuing based approach for task management and a heuristic algorithm for resource management are proposed. By adopting allocation deadline in each VM request, both task manager and VM allocator receive better chances to optimize the cost while satisfying the constraints on the quality of multimedia service. Various simulations were conducted to validate the efficiency of the proposed task and resource management approaches. The results showed that the proposed solutions provided better performance as compared to the existing state-of-the-art approaches.     

类电子商务模式的CNERN数据资源管理与服务信息平台

国家生态系统观测研究网络(National Ecosystam Research Network of China, CNERN)平台的数据资源管理与服务模式与开放式电子商务平台架构存在很大的相似性, CNERN 基于 B2B2C 电子商务模式构建了数据资源管理与服务信息平台,有助于改善资源使用者访问和获取数据资源的体验,改进资源提供者进行数据资源管理及数据资源服务的效率。本文分析了数据资源特点,介绍了数据资源内容,详细分析了信息平台架构、系统组成与功能, 介绍了 CNERN 信息平台如何对多方异地管理、异构存储、不同开放服务方式的数据资源进行类似电子商务模式的规范化管理及自动化服务的整体解决方案。     

基于容器技术的云计算资源自适应管理方法

云计算的发展使得越来越多的软件应用选择云平台作为部署平台。为了应对动态变化的工作负载、应用场景和服务质量目标,应用提供商希望能以一种可伸缩的方式对云计算资源进行动态调整。基于虚拟机的资源管理较为重载,难以实现细粒度的资源动态调整与混合云中跨平台的服务快速迁移。容器技术在一定程度上弥补了虚拟机的不足,然而传统的资源管理方法在诸多方面并不十分适用于容器技术。针对这一问题,提出了基于容器技术的云计算资源自适应管理方法,设计了更适用于容器的资源架构方案与资源之间的调度方式。与传统的线性建模方法不同,所提方法使用非线性函数对云计算资源进行更加精确的建模,同时用遗传算法进行参数调优,使得自适应调整响应更快、总体性能更好。所提方法还针对不同容器多维度的异构性,合理分配容器部署位置,提高物理资源利用率。此外,所提方法结合了容器技术多方面的底层特性,在分配负载等方面进行适应性调整。最后通过实验分析初步确认了所提方法的有效性。     

Development of a Hybrid Manufacturing Cloud

Cloud manufacturing is emerging as a novel business paradigm for the manufacturing industry, in which dynamically scalable and virtualised resources are provided as consumable services over the Internet. A handful of cloud manufacturing systems are proposed for different business scenarios, most of which fall into one of three deployment modes, i.e. private cloud, community cloud, and public cloud. One of the challenges in the existing solutions is that few of them are capable of adapting to changes in the business environment. In fact, different companies may have different cloud requirements in different business situations; even a company at different business stages may need different cloud modes. Nevertheless, there is limited support on migrating to different cloud modes in existing solutions. This paper proposes a Hybrid Manufacturing Cloud that allows companies to deploy different cloud modes for their periodic business goals. Three typical cloud modes, i.e. private cloud, community cloud and public cloud are supported in the system. Furthermore, it enables companies to set self-defined access rules for each resource so that unauthorised companies will not have access to the resource. This self-managed mechanism gives companies full control of their businesses and boosts their trust with enhanced privacy protection. A unified ontology is developed to enhance semantic interoperability throughout the whole process of service provision in the clouds. A Cloud Management Engine is developed to manage all the user-defined clouds, in which Semantic Web technologies are used as the main toolkit. The feasibility of this approach is verified through a group of companies, each of which has complex access requirements for their resources. In addition, a use case is carried out between customers and service providers. This way, optimal service is delivered through the proposed system.     

Toward Cloud Computing QoS Architecture: Analysis of Cloud Systems and Cloud Services

Cloud can be defined as a new computing paradigm that provides scalable, on-demand, and virtualized resources for users. In this style of computing, users can access a shared pool of computing resources which are provisioned with minimal management efforts of users. Yet there are some obstacles and concerns about the use of clouds. Guaranteeing quality of service (QoS) by service providers can be regarded as one of the main concerns for companies tending to use it. Service provisioning in clouds is based on service level agreements representing a contract negotiated between users and providers. According to this contract, if a provider cannot satisfy its agreed application requirements, it should pay penalties as compensation. In this paper, we intend to carry out a comprehensive survey on the models proposed in literature with respect to the implementation principles to address the QoS guarantee issue.      

Dynamic task assignment and resource management in cloud services by using bargaining solution

Cloud computing provides infrastructure, platform and software as services to customers. For the purpose of providing reliable and truthful service, a fair and elastic resource allocation strategy is essential from the standpoint of service customers. In this paper, we propose a game theoretic mechanism for dynamic cloud service management, including task assignment and resource allocation to provide reliable and truthful cloud services. A user utility function is first devised considering the dynamic characteristics of cloud computing. The elementary stepwise system is then applied to efficiently assign tasks to cloud servers. A resource allocation mechanism based on bargaining game solution is also adopted for fair resource allocation in terms of quality of service of requested tasks. Through numerical experiments, it is shown that the proposed mechanism guarantees better system performance than several existing methods. The experimental results show that the mechanism completes the requested tasks earlier with relatively higher utility while providing a significant level of fairness compared with existing ones. The proposed mechanism is expected to support cloud service providers in elastically managing their limited resources in a cloud computing environment in terms of quality of service. Copyright © 2013 John Wiley & Sons, Ltd.     

云工作流中基于分时虚拟机的任务层调度算法

云计算是新的一种面向市场的商业计算模式,向用户按需提供服务,云计算的商业特性使其关注向用户提供服务的服务质量。任务调度和资源分配是云计算中两个关键的技术,所使用的虚拟化技术使得其资源分配和任务调度有别于以往的并行分布式计算。目前主要的调度算法是借鉴网格环境下的调度策略,研究基于QoS的调度算法,存在执行效率较低的问题。我们对云工作流任务层调度进行深入研究,分析由底层资源虚拟化形成的虚拟机的特性,结合工作流任务的各类QoS约束,提出了基于虚拟机分时特性的任务层ACS调度算法。经过试验,我们提出的算法相比于文献[1]中的算法在对于较多并行任务的执行上存在较大的优势,能够很好的利用虚拟的分时特性,优化任务到虚拟机的调度。     

服务网格虚拟服务动态部署和自适应策略

以"服务"的形式包装网格资源已成为一种趋势,并得到网格界的一致认可.为了更加充分灵活的利用网格资源,提出了一个网格虚拟服务动态部署架构以及基于此架构的服务平衡调度算法,通过服务按需部署和实时监控,动态调整资源在不同任务间的分配,并在需要时进行任务迁移,保证应用的服务质量.实验结果表明此系统较其它系统在资源利用率、QoS命中率上都有一定的提高.