A fault detection service for wide area distributed computations

The potential for faults in distributed computing systems is a significant complicating factor for application developers. While a variety of techniques exist for detecting and correcting faults, the implementation of these techniques in a particular context can be difficult. Hence, we propose a fault detection service designed to be incorporated, in a modular fashion, into distributed computing systems, tools, or applications. This service uses well-known techniques based on unreliable fault detectors to detect and report component failure, while allowing the user to trade off timeliness of reporting against false positive rates. We describe the architecture of this service, report on experimental results that quantify its cost and accuracy, and describe its use in two applications, monitoring the status of system components of the GUSTO computational grid testbed and as part of the NetSolve network-enabled numerical solver. This revised version was published online in July 2006 with corrections to the Cover Date.     

Experimental Analysis of a Gossip-Based Service for Scalable, Distributed Failure Detection and Consensus

Gossip protocols and services provide a means by which failures can be detected in large, distributed systems in an asynchronous manner without the limits associated with reliable multicasting for group communications. Extending the gossip protocol such that a system reaches consensus on detected faults can be performed via a flat structure, or it can be hierarchically distributed across cooperating layers of nodes. In this paper, the performance of gossip services employing flat and hierarchical schemes is analyzed on an experimental testbed in terms of consensus time, resource utilization and scalability. Performance associated with a hierarchically arranged gossip scheme is analyzed with varying group sizes and is shown to scale well. Resource utilization of the gossip-style failure detection and consensus service is measured in terms of network bandwidth utilization and CPU utilization. Analytical models are developed for resource utilization and performance projections are made for large system sizes.     

QoS and Contention-Aware Multi-Resource Reservation

To provide Quality of Service (QoS) guarantee in distributed services, it is necessary to reserve multiple computing and communication resources for each service session. Meanwhile, techniques have been available for the reservation and enforcement of various types of resources. Therefore, there is a need to create an integrated framework for coordinated multi-resource reservation. One challenge in creating such a framework is the complex relation between the end-to-end application-level QoS and the corresponding end-to-end resource requirement. Furthermore, the goals of (1) providing the best end-to-end QoS for each distributed service session and (2) increasing the overall reservation success rate of all service sessions are in conflict with each other. In this paper, we present a QoS and contention-aware framework of end-to-end multi-resource reservation for distributed services. The framework assumes a reservation-enabled environment, where each type of resource can be reserved. The framework consists of (1) a component-based QoS-Resource Model, (2) a runtime system architecture for coordinated reservation, and (3) a runtime algorithm for the computation of end-to-end multi-resource reservation plans. The algorithm provides a solution to alleviating the conflict between the QoS of an individual service session and the success rate of all service sessions. More specifically, for each service session, the algorithm computes an end-to-end reservation plan, such that it guarantees the highest possible end-to-end QoS level under the current end-to-end resource availability, and requires the lowest percentage of bottleneck resource(s) among all feasible reservation plans. Our simulation results show excellent performance of this algorithm.     

Performance Evaluation of Resource Management in Cloud Computing Environments

Cloud computing is a computational model in which resource providers can offer on-demand services to clients in a transparent way. However, to be able to guarantee quality of service without limiting the number of accepted requests, providers must be able to dynamically manage the available resources so that they can be optimized. This dynamic resource management is not a trivial task, since it involves meeting several challenges related to workload modeling, virtualization, performance modeling, deployment and monitoring of applications on virtualized resources. This paper carries out a performance evaluation of a module for resource management in a cloud environment that includes handling available resources during execution time and ensuring the quality of service defined in the service level agreement. An analysis was conducted of different resource configurations to define which dimension of resource scaling has a real influence on client requests. The results were used to model and implement a simulated cloud system, in which the allocated resource can be changed on-the-fly, with a corresponding change in price. In this way, the proposed module seeks to satisfy both the client by ensuring quality of service, and the provider by ensuring the best use of resources at a fair price.     

Service platform and monitoring architecture for network function virtualization (NFV)

With the advent of cloud and virtualization technologies and the integration of various computer communication technologies, today’s computing environments can provide virtualized high quality services. The network traffic has also continuously increased with remarkable growth. Software defined networking/network function virtualization (SDN/NFV) enhancing the infrastructure agility, thus network operators and service providers are able to program their own network functions on vendor independent hardware substrate. However, in order for the SDN/NFV to realize a profit, it must provide a new resource sharing and monitoring procedures among the regionally distributed and virtualized computers. In this paper, we proposes a NFV monitoring architecture based practical measuring framework for network performance measurement. We also proposes a end-to-end connectivity support platform across a whole SDN/NFV networks has not been fully addressed.     

A Framework for Automatic Adaptation of Tunable Distributed Applications

Increased platform heterogeneity and varying resource availability in distributed systems motivate the design of resource-aware applications, which ensure a desired performance level by continuously adapting their behavior to changing resource characteristics. In this paper, we describe an application-independent adaptation framework that simplifies the design of resource-aware applications. This framework eliminates the need for adaptation decisions to be explicitly programmed into the application by relying on two novel components: (1) a tunability interface, which exposes adaptation choices in the form of alternate application configurations while encapsulating core application functionality; and (2) a virtual execution environment, which emulates application execution under diverse resource availability enabling off-line collection of information about resulting behavior. Together, these components permit automatic run-time decisions on when to adapt by continuously monitoring resource conditions and application progress, and how to adapt by dynamically choosing an application configuration most appropriate for the prescribed user preference. We evaluate the framework using an interactive distributed image visualization application and a parallel image processing application. The framework permits automatic adaptation to changes in execution environment characteristics such as available network bandwidth or data arrival pattern by choosing a different application configuration that satisfies user preferences of output quality and timeliness.     

Software architecture definition for on-demand cloud provisioning

Cloud computing is a promising paradigm for the provisioning of IT services. Cloud computing infrastructures, such as those offered by the RESERVOIR project, aim to facilitate the deployment, management and execution of services across multiple physical locations in a seamless manner. In order for service providers to meet their quality of service objectives, it is important to examine how software architectures can be described to take full advantage of the capabilities introduced by such platforms. When dealing with software systems involving numerous loosely coupled components, architectural constraints need to be made explicit to ensure continuous operation when allocating and migrating services from one host in the Cloud to another. In addition, the need for optimising resources and minimising over-provisioning requires service providers to control the dynamic adjustment of capacity throughout the entire service lifecycle. We discuss the implications for software architecture definitions of distributed applications that are to be deployed on Clouds. In particular, we identify novel primitives to support service elasticity, co-location and other requirements, propose language abstractions for these primitives and define their behavioural semantics precisely by establishing constraints on the relationship between architecture definitions and Cloud management infrastructures using a model denotational approach in order to derive appropriate service management cycles. Using these primitives and semantic definition as a basis, we define a service management framework implementation that supports on demand cloud provisioning and present a novel monitoring framework that meets the demands of Cloud based applications.     

Distributed shared arrays: A distributed virtual machine with mobility support for reconfiguration

Distributed Shared Arrays (DSA) is a distributed virtual machine that supports Java-compliant multithreaded programming with mobility support for system reconfiguration in distributed environments. The DSA programming model allows programmers to explicitly control data distribution so as to take advantage of the deep memory hierarchy, while relieving them from error-prone orchestration of communication and synchronization at run-time. The DSA system is developed as an integral component of mobility support middleware for Grid computing so that DSA-based virtual machines can be reconfigured to adapt to the varying resource supplies or demand over the course of a computation. The DSA runtime system also features a directory-based cache coherence protocol in support of replication of user-defined sharing granularity and a communication proxy mechanism for reducing network contention. System reconfiguration is achieved by a DSA service migration mechanism, which moves the DSA service and residing computational agents between physical servers for load balancing and fault resilience. We demonstrate the programmability of the model in a number of parallel applications and evaluate its performance by application benchmark programs, in particular, the impact of the coherence granularity and service migration overhead. Song Fu received the BS degreee in computer science from Nanjing University of Aeronautics and Astronautics, China, in 1999, and the MS degree in computer science from Nanjing University, China, in 2002. He is currently a PhD candidate in computer engineering at Wayne State University. His research interests include the resource management, security, and mobility issues in wide-area distributed systems. Cheng-Zhong Xu received the BS and MS degrees in computer science from Nanjing University in 1986 and 1989, respectively, and the Ph.D. degree in computer science from the University of Hong Kong in 1993. He is an Associate Professor in the Department of Electrical and Computer Engineer of Wayne State University. His research interests lie in distributed are in distributed and parallel systems, particularly in resource management for high performance cluster and grid computing and scalable and secure Internet services. He has published more than100 peer-reviewed articles in journals and conference proceedings in these areas. He is the author of the book Scalable and Secure Internet Services and Architecture (CRC Press, 2005) and a co-author of the book Load Balancing in Parallel Computers: Theory and Practice (Kluwer Academic, 1997). He serves on the editorial boards of J. of Parallel and Distributed Computing, J. of Parallel, Emergent, and Distributed Systems, J. of High Performance Computing and Networking, and J. of Computers and Applications. He was the founding program co-chair of International Workshop on Security in Systems and Networks (SSN), the general co-chair of the IFIP 2006 International Conference on Embedded and Ubiquitous Computing (EUC06), and a member of the program committees of numerous conferences. His research was supported in part by the US National Science Foundation, NASA, and Cray Research. He is a recipient of the Faculty Research Award of Wayne State University in 2000, the Presidents Award for Excellence in Teaching in 2002, and the Career Development Chair Award in 2003. He is a senior member of the IEEE. Brian A. Wims was born in Washington, DC in 1967. He received the Bachelor of Science in Electrical Engineering from GMI-EMI (now called Kettering University) in 1990; and Master of Science in Computer Engineering from Wayne State University in 1999. His research interests are primarily in the fields of parallel and distributed systems with applications in Mobile Agent technologies. From 1990–2001 he worked in various Engineering positions in General Motors, including Electrical Analysis, Software Design, and Test and Development. In 2001, he joined the General Motors IS&S department where he is currently a Project Manager in the Computer Aided Test group. Responsibilities include managing the development of test automation applications in the Electrical, EMC, and Safety Labs. Ramzi Basharahil was born in Aden, Yemen in 1972. He received the Bachelor of Science degree in Electrical Engineering from the United Arab Emirates University. He graduated top of his engineering graduated class of 1997. He obtained Master of Science degree in 2001 from Wayne State University in the Department of Electrical and Computer Engineering. His main research interests are primarily in the fields of parallel and distributed systems with applications to distributed processing across cluster of servers. From 1997 to 1998, he worked as a Teaching Assistant in the Department of Electrical Engineering at the UAE University. In 2000, he joined Internet Security Systems as a security software engineer. He later joined NetIQ Corporation in 2002 and still working since then. He is leading the security events trending and events management software development where he is involved in designing and the implementing event/log managements products.     

Best-KFF: a multi-objective preemptive resource allocation policy for cloud computing systems

Cluster Computing - Resource provisioning is a key issue in large-scale distributed systems such as cloud computing systems. Several resource provider systems utilized preemptive resource...     

雄安新区多尺度生态安全格局构建框架

维持城市所处的生态系统格局与过程,使得城市生存和发展所依赖的关键生态系统服务实现供需平衡是保障城市生态安全的根本要求和基本前提。因此,城市生态安全不仅是城市本身的问题,还涉及到城市所处的流域、区域、国家等多个不同的空间尺度,具体由支撑城市及其发展的关键生态系统服务所涉及的生态过程所决定。对生态安全概念及研究进展进行了梳理和分析,在此基础上,提出了一个城市生态安全保障的生态系统服务研究框架,将维持城市生态安全的关键生态系统服务需求分为3个层次：一是水资源供给、食物生产、水质净化等城市生存所依赖的基础资源与基本环境维持相关的服务,二是土壤保持、水源涵养、防风固沙、固碳释氧、生物多样性等生态系统稳定和可持续性维持相关的生态系统稳定和调节类服务,三是生态休闲、微气候调节、降尘减噪、文化审美等人居环境与文化类服务。进而利用该框架,从不同需求层次分别对雄安新区生态安全保障的生态系统服务需求进行了分析,结合其自然地理特征和新区建设的社会经济发展情景,从新区城市、大清河流域和京津冀区域三个尺度提出了雄安新区生态安全格局构建框架,以及相应的保障对策和建议,包括从多尺度进行生态环境调控和管理,打破行政和地域边界限制;关注各尺度各关键生态系统服务的过程,加强生态环境要素的监测和预警;重视新区在人居环境改善层次的生态系统服务需求等。     

An authorization framework for metacomputing applications

To span administrative boundaries, metacomputing systems require the integration of strong authentication and authorization methods. The problem is complicated because different components of the system may have different security policies. This paper presents a distributed model for authorization that we have integrated with the Prospero Resource Manager, a metacomputing resource allocation system developed at USC. The integration of authorization with PRM was accomplished through the specification of a policy language and the use of a Generic Authorization and Access-control API (GAA API). The language supports the specification of diverse authorization policies including ACLs, capabilities and lattice-based access controls. The GAA API provides a uniform authorization service interface for facilitating access control decisions and requesting authorization information about a particular resource. We describe a prototype of our system. This revised version was published online in July 2006 with corrections to the Cover Date.     

A successful educational collaboration between scientists and educators: microscopic explorations

The teacher's guide, Microscopic Explorations: A GEMS Festival Guide (Brady and Willard, 1998), is the result of a partnership between Great Explorations in Math and Science (GEMS), a program of the Lawrence Hall of Science (LHS), the public science and curriculum development center of the University of California, Berkeley, and the Microscopy Society of America (MSA). Microscopic Exploration supports the MSA's low-cost national outreach program and, also, reaches a very large educational community as part of the GEMS series. Some of the lessons learned through the extremely successful MSA/LHS collaboration are summarized here in hopes that they may be instructive to other scientists and educators as they launch their own partnerships and collaborations.     

Discovering likely invariants of distributed transaction systems for autonomic system management

Large amount of monitoring data can be collected from distributed systems as the observables to analyze system behaviors. However, without reasonable models to characterize systems, we can hardly interpret such monitoring data effectively for system management. In this paper, a new concept named flow intensity is introduced to measure the intensity with which internal monitoring data reacts to the volume of user requests in distributed transaction systems. We propose a novel approach to automatically model and search relationships between the flow intensities measured at various points across the system. If the modeled relationships hold all the time, they are regarded as invariants of the underlying system. Experimental results from a real system demonstrate that such invariants widely exist in distributed transaction systems. Further we discuss how such invariants can be used to characterize complex systems and support autonomic system management. Guofei Jiang received the B.S. and Ph.D. degrees in electrical and computer engineering from Beijing Institute of Technology, China, in 1993 and 1998, respectively. During 1998–2000, he was a postdoctoral fellow in computer engineering at Dartmouth College, NH. He is currently a research staff member with the Robust and Secure Systems Group in NEC Laboratories America at Princeton, NJ. During 2000–2004, he was a research scientist in the Institute for Security Technology Studies at Dartmouth College. His current research focus is on distributed system, dependable and secure computing, system and information theory. He has published over 50 technical papers in these areas. He is an associate editor of IEEE Security and Privacy magazine and has served in the program committees of many conferences. Haifeng Chen received the BEng and MEng degrees, both in automation, from Southeast University, China, in 1994 and 1997 respectively, and the PhD degree in computer engineering from Rutgers University, New Jersey, in 2004. He has worked as a researcher in the Chinese national research institute of power automation. He is currently a research staff member at NEC laboratory America, Princeton, NJ. His research interests include data mining, autonomic computing, pattern recognition and robust statistics. Kenji Yoshihira received the B.E. in EE at University of Tokyo in 1996 and designed processor chips for enterprise computer at Hitachi Ltd. for five years. He employed himself in CTO at Investoria Inc. in Japan to develop an Internet service system for financial information distribution through 2002 and received the M.S. in CS at New York University in 2004. He is currently a research staff member with the Robust and Secure Systems Group in NEC Laboratories America, inc. in NJ. His current research focus is on distributed system and autonomic computing.     

Product/Service‐Systems for a Circular Economy: The Route to Decoupling Economic Growth from Resource Consumption?

Product/service‐systems (PSS) that focus on selling service and performance instead of products are often mentioned as means to realize a circular economy (CE), in which economic growth is decoupled from resource consumption. However, a PSS is no implicit guarantee for a CE, and CE strategies do not necessarily lead to decoupling economic growth from resource consumption in absolute terms. Absolute resource decoupling only occurs when the resource use declines, irrespective of the growth rate of the economic driver. In this forum paper, we propose a two‐step framework that aims to support analyses of PSS and their potential to lead to absolute resource decoupling. In the first step, we present four PSS enablers of relative resource reduction that qualify as CE strategies. In the second step, three subsequent requirements need to be met, in order to successfully achieve absolute resource decoupling. Conditions and limitations for this accomplishment are discussed. Danish textile cases are used to exemplify the framework elements and its application. We expect that the framework will challenge the debate on the necessary conditions for CE strategies to ensure absolute resource decoupling.     

PHOENIX: A Self Adaptable Monitoring Platform for Cluster Management

Distributed systems based on cluster of workstation are more and more difficult to manage due to the increasing number of processors involved, and the complexity of associated applications. Such systems need efficient and flexible monitoring mechanisms to fulfill administration services requirements. In this paper, we present PHOENIX a distributed platform supporting both applications and operating system monitoring with a variable granularity. The granularity is defined using logical expressions to specify complex monitoring conditions. These conditions can be dynamically modified during the application execution. Observation techniques, based on an automatic probe insertion combined with a system agent to minimize the PHOENIX execution time overhead. The platform extensibility offers a suitable environment to design distributed value added services (performance monitoring, load balancing, accounting, cluster management, etc.).     

A Monitoring Sensor Management System for Grid Environments

Large distributed systems such as Computational Grids require a large amount of monitoring data be collected for a variety of tasks such as fault detection, performance analysis, performance tuning, performance prediction, and scheduling. Ensuring that all necessary monitoring is turned on and that data is being collected can be a very tedious and error-prone task. We have developed an agent-based system to automate the execution of monitoring sensors and the collection of event data.     

生态补偿权衡关系研究进展

生态补偿是以市场机制解决环境外部性问题的方法,其典型特征是通过经济激励而实现生态系统保护和减贫的双赢,因此在世界范围内得到了广泛实施。权衡关系是生态补偿理论和实践中面临的问题和困境之一。生态补偿存在4种权衡关系:生态系统服务之间的权衡、监测成本与交易成本之间的权衡、公平与效率之间的权衡以及生态系统服务供应与减贫之间的权衡。分析了权衡关系的产生源于生态系统服务产生过程的不确定性、自然和社会经济系统的耦合性以及生态补偿实施背景的异质性,并提出应该在理论基础、实践模式和评估系统3个方面加强权衡关系的研究。     

Increasing the quality of services and resource utilization in vehicular cloud computing using best host selection methods

One of the technology for increasing the safety and welfare of humans in roads is Vehicular Cloud Computing (VCC). This technology can utilize cloud computing advantages in the Vehicular Ad Hoc Network (VANET). VCC by utilizing modern equipment along with current vehicles, can play a significant role in the area of smart transportation systems. Given the potential of this technology, effective methods for managing existing resources and providing the expected service quality that is essential for such an environment are not yet available as it should. One of the most important barriers to providing such solutions seems to be resource constraints and very high dynamics in vehicles in VCC. In this article, based on virtualization and taking into account the environment with these features, we propose simple ways to manage resources better and improve the quality of service. We were able to achieve better results in simulation than previous methods by providing a flexible data structure to control the important data in the environment effectively. To illustrate the impact of the proposed methods, we compared them with some of the most important methods in this context, and we used SUMO 1.2.0 and MATLAB R2019a software to simulate them. Simulation results indicate that the proposed methods provide better results than previous methods in terms of resource efficiency, Quality of Service (QoS), and load balancing.

     

Ecological benefits assessment: A policy‐oriented alternative to regional ecological risk assessment

As the focus of many environmental and resource management decisions shifts to larger ecological units such as watersheds and ecoregions, the respective roles of ecological risk and ecological benefits must be reassessed. At larger ecological scales, risk becomes much more difficult to characterize because of the focus on the system rather than individual species. However, quantifying the monetary value of many ecological benefits is also difficult because no direct measures of their value exist, and the indirect techniques used by economists are not fully accepted in some policy‐making settings. This dilemma may be resolved by considering ecological resource/service flows, which are ecological benefits in physical rather than monetary units. By assessing the whole range of resource/service flows and the changes to each that would result from different management/control alternatives, scientists would be able to provide policy‐makers a much better basis for their decisions, and the resulting assessment would characterize changes in ecological features that are more familiar to both policy‐makers and the public.