Grid Computing and Beyond: The Context of Dynamic Data Driven Applications Systems

The advent of Grid computing has enhanced our capabilities to model and simulate complex systems arising in scientific, engineering, and commercial applications. The premise of Grid computing has been "on-demand" availability of computational resources to an application as needed, in the same manner as electricity is provided readily through electrical power grids. The computational grid (or simply the "Grid") entails ubiquitous access to resources (local or remote), such as computation and communication resources, as well as access to storage systems and visualization systems. As Grid computing technologies mature, it behooves to look beyond the current capabilities, into more advanced future environments. The environments of interest here are the enhanced capabilities that can be created by the paradigm of dynamic data driven applications systems (DDDAS). DDDAS entails the ability to incorporate additional data into an executing application and, in reverse, the ability of applications to dynamically steer the measurement process. The DDDAS concept offers the promise of improving application models and methods, and augmenting the analysis and prediction capabilities of application simulations and the effectiveness of measurement systems. Enabling this synergistic feedback and control loop between application simulations and measurements requires novel application modeling approaches and frameworks, algorithms stable under dynamic data injection and steering conditions, and new systems software and computational infrastructure capabilities. Recent advances in complex applications and the advent of Grid computing and sensor systems are some of the technologies that make it timely to embark in developing DDDAS capabilities. DDDAS environments extend the current notion of Grid infrastructure to also include the measurement systems in an integrated and synergistic way. DDDAS environments require support and services that go beyond the current Grid services in terms of t     

Research of Adaptation of Infrastructures for WSRF in Grid Computing

1IntroductionGrid Computing is a promising emerging technologythat is growingin mindshare andrelevanceintheindus-try .Applications that take advantage of Grids are underdevelopment in both academic and commercial organiza-tions[1].GBuilderis anintegrated development platformwe worked out based on extant Grid Computing toolk-its .It encapsulates the complicated Webservice compil-ing,deploying over these infrastructures ,informs usersof intelligible information about mid-outcomes and er-rors …     

Darwin: customizable resource management for value-added networkservices

The Internet is rapidly changing from a set of wires and switches that carry packets into a sophisticated infrastructure that delivers a set of complex value-added services to end users. Services can range from bit transport all the way up to distributed value-added services like video teleconferencing, virtual private networking, data mining, and distributed interactive simulations. Before such services can be supported in a general and dynamic manner, we have to develop appropriate resource management mechanisms. These resource management mechanisms must make it possible to identify and allocate resources that meet service or application requirements, support both isolation and controlled dynamic sharing of resources across services and applications sharing physical resources, and be customizable so services and applications can tailor resource usage to optimize their performance. The Darwin project has developed a set of customizable resource management mechanisms that support value-added services. We present and motivate these mechanisms, describe their implementation in a prototype system, and describe the results of a series of proof-of-concept experiments     

Interoperability of GADU in using heterogeneous grid resources for bioinformatics applications.

Bioinformatics tools used for efficient and computationally intensive analysis of genetic sequences require large-scale computational resources to accommodate the growing data. Grid computational resources such as the Open Science Grid and TeraGrid have proved useful for scientific discovery. The genome analysis and database update system (GADU) is a high-throughput computational system developed to automate the steps involved in accessing the Grid resources for running bioinformatics applications. This paper describes the requirements for building an automated scalable system such as GADU that can run jobs on different Grids. The paper describes the resource-independent configuration of GADU using the Pegasus-based virtual data system that makes high-throughput computational tools interoperable on heterogeneous Grid resources. The paper also highlights the features implemented to make GADU a gateway to computationally intensive bioinformatics applications on the Grid. The paper will not go into the details of problems involved or the lessons learned in using individual Grid resources as it has already been published in our paper on genome analysis research environment (GNARE) and will focus primarily on the architecture that makes GADU resource independent and interoperable across heterogeneous Grid resources.     

Building Grid Portal Applications From a Web Service Component Architecture

This work describes an approach to building Grid applications based on the premise that users who wish to access and run these applications prefer to do so without becoming experts on Grid technology. We describe an application architecture based on wrapping user applications and application workflows as Web services and Web service resources. These services are visible to the users and to resource providers through a family of Grid portal components that can be used to configure, launch, and monitor complex applications in the scientific language of the end user. The applications in this model are instantiated by an application factory service. The layered design of the architecture makes it possible for an expert to configure an application factory service with a custom user interface client that may be dynamically loaded into the portal.     

R2M3：一种基于域的中间件资源管理机制

Web应用服务器需要能够同时支持多个不同的Web应用，在系统资源紧张时，需要有效的方式来解决应用之间的资源冲突并满足应用的性能需求．R2M3是一种基于域的中间件资源管理机制，它通过资源合约来维护应用之间独立的资源视图，并通过资源协调器来处理应用之间可能出现的资源冲突．R2M3提供了运行时资源管理设施来实现服务器服务能力的最大化．该资源管理机制在OnceAS中进行了原型实现，实验表明其可以有效的解决应用间的资源冲突问题．     

Resource allocation in cloud virtual machines based on empirical service traces

One of the key technologies in cloud computing is virtualization. Using virtualization, a system can optimize usage of resources, simplify management of infrastructure and software, and reduce hardware requirements. This research focuses on infrastructure as a service, resource allocation by providers for consumers, and explores the optimization of system utilization based on actual service traces of a real world cloud computing site. Before activating additional virtual machines (VM) for applications, the system examines CPU usage in the resource pools. The behavior of each VM can be estimated by monitoring the CPU usage for different types of services, and consequently, additional resources added or idle resources released. Based on historical observations of the required resources for each kind of service, the system can efficiently dispatch VMs. The proposed scheme can efficiently and effectively distribute resources to VMs for maximizing utilization of the cloud computing center. Copyright © 2013 John Wiley & Sons, Ltd.     

基于SAGE的分布式虚拟现实框架

文中借鉴了国内外的典型应用,提出了基于SAGE(Simple Adaptive Grid Engine)的分布式虚拟现实框架.该框架简单高效,且易于应用开发.通过性能和可扩展性这两项指标的测试,表明该框架在中小规模的分布式虚拟现实环境中具有良好的性能和可扩展性.     

InRout - A QoS aware route selection algorithm for industrial wireless sensor networks

Wireless sensor networks are a key enabling technology for industrial monitoring applications where the use of wireless infrastructure allows high adaptivity and low cost in terms of installation and retrofitting. To facilitate the move from the current wired designs to wireless designs, concerns regarding reliability must be satisfied. Current standardization efforts for industrial wireless systems lack specification on efficient routing protocols that mitigate reliability concerns. Consequently, this work presents the InRout route selection algorithm, where local information is shared among neighbouring nodes to enable efficient, distributed route selection while satisfying industrial application requirements and considering sensor node resource limitations. Route selection is described as a multi-armed bandit task and uses Q-learning techniques to obtain the best available solution with low overhead. A performance comparison with existing approaches demonstrates the benefits of the InRout algorithm, which satisfies typical quality of service requirements for industrial monitoring applications while considering sensor node resources. Simulation results show that InRout can provide gains ranging from 4% to 60% in the number of successfully delivered packets when compared to current approaches with much lower control overhead.     

Task Scheduling and Lightpath Establishment in Optical Grids

Data-intensive Grid applications require huge data transferring between multiple geographically separated computing nodes where computing tasks are executed. For a future WDM network to efficiently support this type of emerging applications, neither the traditional approaches to establishing lightpaths between given source destination pairs are sufficient, nor are those existing application level approaches that consider computing resources but ignore the optical layer connectivity. Instead, lightpath establishment has to be considered jointly with task scheduling to achieve best performance. In this paper, we study the optimization problems of jointly scheduling both computing resources and network resources. We first present the formulation of two optimization problems with the objectives being the minimization of the completion time of a job and minimization of the resource usage/cost to satisfy a job with a deadline. When the objective is to minimize the completion time, we devise an optimal algorithm for a special type of applications. Furthermore, we propose efficient heuristics to deal with general applications with either optimization objective and demonstrate their good performances in simulation.     

Response Time Analysis on Multicore ECUs With Shared Resources

As multiprocessor systems are increasingly used in automotive real-time environments, scheduling and synchronization analysis of these platforms receive growing attention. Upcoming multicore ECUs allow the integration of previously separated functionality for body electronics or sensor fusion onto a single unit, and allow the parallelization of complex computations over multiple cores. The application of multiple CPUs turns an ECU into a highly integrated “networked system” microcosm, in which complex interdependencies can be observed due to the use of shared resources even in partitioned scheduling. To deliver predictable performance, resource arbitration protocols are required and have been proposed in literature. This paper presents an novel analytical approach to provide the worst-case response time for real-time tasks in multiprocessor systems with shared resources. The method supports realistic, event- or time-driven task activation schemes and allows to calculate tight bounds on the estimated system performance.      

The Grid Economy

This work identifies challenges in managing resources in a Grid computing environment and proposes computational economy as a metaphor for effective management of resources and application scheduling. It identifies distributed resource management challenges and requirements of economy-based Grid systems, and discusses various representative economy-based systems, both historical and emerging, for cooperative and competitive trading of resources such as CPU cycles, storage, and network bandwidth. It presents an extensive, service-oriented Grid architecture driven by Grid economy and an approach for its realization by leveraging various existing Grid technologies. It also presents commodity and auction models for resource allocation. The use of commodity economy model for resource management and application scheduling in both computational and data grids is also presented.     

Deploying the NaradaBrokering Substrate in Aiding Efficient Web and Grid Service Interactions

NaradaBrokering has been developed as the messaging infrastructure for collaboration, peer-to-peer, and Grid applications. The value of NaradaBrokering in the context of Grid and Web Services has been clear for some time. NaradaBrokering-combined with further extensions to, and testing of, its existing capabilities - can also take advantage of the maturing of Web Service standards and specifications to build very powerful general mechanisms to deploy and integrate it with general Web Services. This paper describes a framework to integrate the NaradaBrokering substrate with Web Services.     

Commercial implications of operational risk for service-based contracts

This paper explores the relationship between operational risk management and commercial risk management applied to service-or output-based contracts. It examines the development and characteristics of service-based contracts and outlines an approach to modelling service credit exposure. Trade-offs between contract performance, service credit exposure and operational investment are discussed within the context of contract negotiation. Finally, the prime/subcontractor relationship is examined with respect to flow-down of output-and input-based specifications, together with the potential difficulties arising from an adverse combination of the two.     

Thread migration and communication minimization in DSM systems

Networks of workstations are characterized by dynamic resource capacities. Such environments can only be efficiently exploited by applications that are dynamically reconfigurable. The paper explores mechanisms and policies that enable online reconfiguration of shared memory applications through thread migration. We describe the design and preliminary performance of a distributed shared memory (DSM) system that performs online remappings of threads to nodes based on sharing behavior. Our system obtains complete sharing information through a novel correlation tracking phase that avoids the thread thrashing that characterizes previous approaches. This information is used to evaluate the communication required by a given thread mapping and to predict the resulting performance     

Application Deployment on Catallactic Grid Middleware

An architecture based on a decentralized market view integrates grid applications with catallactic middleware. A prototype application showed the concept's feasibility, as well as the middleware's effectiveness in balancing query-request workload across multiple grid services. Grid computing researchers have shown significant interest in using an economic paradigm for exchanging grid resources and services. With this approach, applications can use a market mechanism to schedule services access, thus giving them a fair, efficient way to share resources in high-demand periods. Most existing approaches rely on centralized brokers to coordinate resource access, and they're typically implemented over existing grid middleware. We propose an alternative approach based on F.A. Hayek's Catallaxy mechanism. Catallaxy's free-market, self-organizing coordination mechanisms adjust prices within the market based on the particular demands for a specific (scarce) resource     

Applying blockchain-based method to smart contract classification for CPS applications

Smart contract has been the core of blockchain systems and other blockchain-based systems since Blockchain 2.0. Various operations on blockchain are performed through the invocation and execution of smart contracts. This leads to extensive combinations between blockchain, smart contract, Internet of Things (IoT) and Cyber-Physical System (CPS) applications, and then many blockchain-based IoT or CPS applications emerge to provide multiple benefits to the economy and society. In this case, obtaining a better understanding of smart contracts will contribute to the easier operation, higher efficiency and stronger security of those blockchain-based systems and applications. Many existing studies on smart contract analysis are based on similarity calculation and smart contract classification. However, smart contract is a piece of code with special characteristics and most of smart contracts are stored without any category labels, which leads to difficulties of smart contract classification. As the back end of a blockchain-based Decentralized Application (DApp) is one or several smart contracts, DApps with labeled categories and open source codes are applied to achieve a supervised smart contract classification. A three-phase approach is proposed to categorize DApps based on various data features. In this approach, 5,659 DApps with smart contract source codes and pre-tagged categories are first obtained based on massive collected DApps and smart contracts from Ethereum, State of the DApps and DappRadar. Then feature extraction and construction methods are designed to form multi-feature vectors that could present the major characteristics of DApps. Finally, a fused classification model consisting of KNN, XGBoost and random forests is applied to the multi-feature vectors of all DApps for performing DApp classification. The experimental results show that the method is effective. In addition, some positive correlations between feature variables and categories, as well as several user behavior patterns of DApp calls, are found in this paper.     

Early order completion contract approach to minimize the impact of demand uncertainty on supply chains

Many optimization techniques have been proposed over the years to improve the performance of supply chains. Although these approaches have been shown to be effective, most of them were developed without considering uncertainties in supply chains to simplify the analysis. In fact, uncertainties can deteriorate the performance of supply chains, such as increase in total cost, or drop in fill rate, of the whole system. In distributed supply chains, participating members are not under a sole control by a central authority, the problem is even more stringent due to incomplete information sharing, or so called asymmetrical information sharing. One way to improve the system performance is to achieve coordination among participating parties through establishment of contracts. The objectives of this paper are i) to evaluate the effects of demand uncertainty in a distributed supply chain, which is modelled as an agent-based system; ii) to propose a coordination mechanism with early order completion contract to minimize the negative impacts of demand uncertainty; and iii) to present associated simulation results. Performance of the system is measured in terms of costs and fill rate. Simulation results indicate that the proposed contract approach is able to improve the performance measures of the system.     

基于信任协商的开放系统授权服务模型的设计

提出了一种第三方的授权服务模型,对现存的信任协商系统起到支撑作用.模型作为一个授权代理,在实体使用信任协商确定一个适当的资源访问策略后,可以在一个开放的系统中发布访问资源的令牌.模型的体系结构设计允许集成新的信任应用程序,也可以间接集成现存传统的应用程序.讨论模型、模型使用的通信协议的设计和实现,以及它的性能.     

网格环境下 GridShell技术研究

为了方便传统的类UNIX系统用户使用网格，论文基于shell的基本思坦，提出了网洛环境下的一种命令行解释工具——GridShell，它支持类似于UNIX操作系统中的大部分命令行命令，并增加了Grid系统必需的一些命令，如greg负责网格用户与资源登记、gappRun负责应用程序在网格中的执行。在我们的实验网格Grid Wader上完成了Grid Shell的实现，并应用Grid Shell进行了网格资源共享和并行计算的试验。