The Experts below are selected from a list of 31533 Experts worldwide ranked by ideXlab platform

Boqiang Lin - One of the best experts on this subject based on the ideXlab platform.

  • Energy conservation of electrolytic aluminum industry in China
    Renewable & Sustainable Energy Reviews, 2015
    Co-Authors: Boqiang Lin
    Abstract:

    The electrolytic aluminium industry is a typical energy-intensive industry, and one of the six largest energy-consuming industries in China. The energy consumption of China’s electrolytic aluminium industry (CEAI) in 2011 accounted for 0.91% of China’s total energy consumption and 22.7% of the total energy consumption of the non-ferrous metal industry. In consideration of the bulk of energy used in the smelting process in the non-ferrous metal industry, CEAI assumes the corresponding obligation of energy conservation and emissions reduction. Using the co-integration method, the long-term equilibrium relationship among the energy consumption of CEAI, output, electricity price and average Enterprise Scale is obtained. Thereafter the Monte-Carlo simulation is used to forecast energy consumption and energy conservation potential of CEAI under different energy conservation scenarios, and conduct risk analysis. The research shows that increase in the price of electricity and Enterprise Scale is helpful to reducing the total energy consumption of CEAI. The future energy conservation potential of CEAI is large. According to the result of the analysis the energy conservation potential of CEAI in 2020 will reach 30.51Mtce under the moderate energy conservation scenario and 49.93Mtce under the advanced energy conservation scenario. Some corresponding policy suggestions are recommended in this paper.

  • energy conservation potential in china s petroleum refining industry evidence and policy implications
    Energy Conversion and Management, 2015
    Co-Authors: Boqiang Lin, Xuan Xie
    Abstract:

    China is currently the second largest petroleum refining country in the world due to rapid growth in recent years. Because the petroleum refining industry is energy-intensive, the rapid growth in petroleum refining and development caused massive energy consumption. China’s urbanization process will guarantee sustained growth of the industry for a long time. Therefore, it is necessary to study the energy conservation potential of the petroleum industry. This paper estimates the energy conservation potential of the industry by applying a cointegration model to investigate the long-run equilibrium relationship between energy consumption and some factors such as energy price, Enterprise Scale, R&D investment and ownership structure. The results show that R&D investment has the greatest reduction impact on energy intensity, and the growth of market participants (i.e. the decline of the share of state-owned companies) can improve energy efficiency of this industry. Under the advanced energy-saving scenario, the accumulated energy conservation potential will reach 230.18 million tons of coal equivalent (tce). Finally, we provide some targeted policy recommendations for industrial energy conservation.

  • a stochastic frontier analysis of energy efficiency of china s chemical industry
    Journal of Cleaner Production, 2015
    Co-Authors: Boqiang Lin, Houyin Long
    Abstract:

    As an energy-intensive industry, China's chemical industry consumed 347.1 million ton coal equivalent of energy in 2011, which was equal to the sum of the overall energy consumption of Uzbekistan and Czech Republic. Thus, it is crucial to analyze the industry's energy efficiency and energy saving potential. In this paper, we adopt the stochastic frontier analysis to study the average energy efficiency and energy saving potential of the chemical industry based on the assumption of the trans-log production function. The results show that energy price and Enterprise Scale are conducive for the improvement of energy efficiency while ownership structure has an opposite effect. The average energy efficiency in China was 0.6897 during 2005–2011, with Shanghai having the highest and Shanxi the lowest. In addition, the energy efficiency of East China was higher than that of West and Central China, and the energy efficiency gap between the Eastern and the Western regions was widening. The results also show that China's energy saving potential was 89.42 million ton coal equivalent, with Shanxi having the highest, followed by Inner Mongolia. Moreover, the energy saving potential of East China was the largest. Finally, we provide policy recommendations for energy efficiency improvement in China's chemical industry.

Karsten Schwan - One of the best experts on this subject based on the ideXlab platform.

  • Middleware for Enterprise Scale data stream management using utility-driven self-adaptive information flows
    Cluster Computing, 2007
    Co-Authors: Vibhore Kumar, Zhongtang Cai, Brian F Cooper, Greg Eisenhauer, Karsten Schwan
    Abstract:

    We consider Enterprise-wide information flows that are responsible for acquiring, processing and delivering operational information across the business units. Middleware that enables such aggregation of data-streams must not only support scalable and efficient self-management to deal with changes in the operating conditions, but should also have an embedded business-sense to appreciate the business critical nature of some updates. In this paper, we present a novel self-adaptation algorithm that has been designed to Scale efficiently for thousands of streams and aims to maximize the overall business utility attained from running middleware-based applications. The outcome is that the middleware not only deals with changing network conditions or resource requirements, but also responds appropriately to changes in business policies. An important feature of the algorithm is a hierarchical node-partitioning scheme that decentralizes reconfiguration and suitably localizes its impact. Extensive simulation experiments and benchmarks attained with actual Enterprise operational data corroborate this paper’s claims.

  • imanage policy driven self management for Enterprise Scale systems
    ACM IFIP USENIX international conference on Middleware, 2007
    Co-Authors: Vibhore Kumar, Brian F Cooper, Greg Eisenhauer, Karsten Schwan
    Abstract:

    It is obvious that big, complex Enterprise systems are hard to manage. What is not obvious is how to make them more manageable. Although there is a growing body of research into system self-management, many techniques are either too narrow, focusing on a single component rather than the entire system, or not robust enough, failing to Scale or respond to the full range of an administrator's needs. In our iManage system we have developed a policy-driven system modeling framework that aims to bridge the gap between manageable components and manageable systems. In particular, iManage provides: (1) system state-space partitioning, which divides a large system state-space into partitions that are more amenable to constructing system models and developing policies, (2) online model and policy adaptation to allow the self-management infrastructure to deal gracefully with changes in operating environment, system configuration, and workload, and (3) tractability and trust, where tractability allows an administrator to understand why the system chose a particular policy and also influence that decision, and trust allows an administrator to understand the system's confidence in a proposed, automated action. Simulations driven by scenarios given to us by our industrial collaborators demonstrate that iManage is effective both at constructing useful system models and in using those models to drive automated system management.

  • utility driven proactive management of availability in Enterprise Scale information flows
    ACM IFIP USENIX international conference on Middleware, 2006
    Co-Authors: Zhongtang Cai, Vibhore Kumar, Brian F Cooper, Greg Eisenhauer, Karsten Schwan, Robert E Strom
    Abstract:

    Enterprises rely critically on the timely and sustained delivery of information. To support this need, we augment information flow middleware with new functionality that provides high levels of availability to distributed applications while at the same time maximizing the utility end users derive from such information. Specifically, the paper presents utility-driven ‘proactive availability-management' techniques to offer (1) information flows that dynamically self-determine their availability requirement based on high-level utility specifications, (2) flows that can trade recovery time for performance based on the ‘perceived' stability of and failure predictions (early alarm) for the underlying system, and (3) methods, based on real-world case studies, to deal with both transient and non-transient failures. Utility-driven ‘proactive availability-management' is integrated into information flow middleware and used with representative applications. Experiments reported in the paper demonstrate middleware capability to self-determine availability guarantees, to offer improved performance versus a statically configured system, and to be resilient to a wide range of faults.

Vibhore Kumar - One of the best experts on this subject based on the ideXlab platform.

  • Middleware for Enterprise Scale data stream management using utility-driven self-adaptive information flows
    Cluster Computing, 2007
    Co-Authors: Vibhore Kumar, Zhongtang Cai, Brian F Cooper, Greg Eisenhauer, Karsten Schwan
    Abstract:

    We consider Enterprise-wide information flows that are responsible for acquiring, processing and delivering operational information across the business units. Middleware that enables such aggregation of data-streams must not only support scalable and efficient self-management to deal with changes in the operating conditions, but should also have an embedded business-sense to appreciate the business critical nature of some updates. In this paper, we present a novel self-adaptation algorithm that has been designed to Scale efficiently for thousands of streams and aims to maximize the overall business utility attained from running middleware-based applications. The outcome is that the middleware not only deals with changing network conditions or resource requirements, but also responds appropriately to changes in business policies. An important feature of the algorithm is a hierarchical node-partitioning scheme that decentralizes reconfiguration and suitably localizes its impact. Extensive simulation experiments and benchmarks attained with actual Enterprise operational data corroborate this paper’s claims.

  • imanage policy driven self management for Enterprise Scale systems
    ACM IFIP USENIX international conference on Middleware, 2007
    Co-Authors: Vibhore Kumar, Brian F Cooper, Greg Eisenhauer, Karsten Schwan
    Abstract:

    It is obvious that big, complex Enterprise systems are hard to manage. What is not obvious is how to make them more manageable. Although there is a growing body of research into system self-management, many techniques are either too narrow, focusing on a single component rather than the entire system, or not robust enough, failing to Scale or respond to the full range of an administrator's needs. In our iManage system we have developed a policy-driven system modeling framework that aims to bridge the gap between manageable components and manageable systems. In particular, iManage provides: (1) system state-space partitioning, which divides a large system state-space into partitions that are more amenable to constructing system models and developing policies, (2) online model and policy adaptation to allow the self-management infrastructure to deal gracefully with changes in operating environment, system configuration, and workload, and (3) tractability and trust, where tractability allows an administrator to understand why the system chose a particular policy and also influence that decision, and trust allows an administrator to understand the system's confidence in a proposed, automated action. Simulations driven by scenarios given to us by our industrial collaborators demonstrate that iManage is effective both at constructing useful system models and in using those models to drive automated system management.

  • utility driven proactive management of availability in Enterprise Scale information flows
    ACM IFIP USENIX international conference on Middleware, 2006
    Co-Authors: Zhongtang Cai, Vibhore Kumar, Brian F Cooper, Greg Eisenhauer, Karsten Schwan, Robert E Strom
    Abstract:

    Enterprises rely critically on the timely and sustained delivery of information. To support this need, we augment information flow middleware with new functionality that provides high levels of availability to distributed applications while at the same time maximizing the utility end users derive from such information. Specifically, the paper presents utility-driven ‘proactive availability-management' techniques to offer (1) information flows that dynamically self-determine their availability requirement based on high-level utility specifications, (2) flows that can trade recovery time for performance based on the ‘perceived' stability of and failure predictions (early alarm) for the underlying system, and (3) methods, based on real-world case studies, to deal with both transient and non-transient failures. Utility-driven ‘proactive availability-management' is integrated into information flow middleware and used with representative applications. Experiments reported in the paper demonstrate middleware capability to self-determine availability guarantees, to offer improved performance versus a statically configured system, and to be resilient to a wide range of faults.

Thomas Phan - One of the best experts on this subject based on the ideXlab platform.

  • a grid based approach for Enterprise Scale data mining
    Grid Computing Environments, 2007
    Co-Authors: Ramesh Natarajan, Radu Sion, Thomas Phan
    Abstract:

    We describe a grid-based approach for Enterprise-Scale data mining, which is based on leveraging parallel database technology for data storage, and on-demand compute servers for parallelism in the statistical computations. This approach is targeted towards the use of data mining in highly-automated vertical business applications, where the data is stored on one or more relational database systems, and an independent set of high-performance compute servers or a network of low-cost, commodity processors is used to improve the application performance and overall workload management. The goal of this paper is to describe an algorithmic decomposition of data mining kernels between the data storage and compute grids, which makes it possible to exploit the parallelism on the respective grids in a simple way, while minimizing the data transfer between these grids. This approach is compatible with existing standards for data mining task specification and results reporting, so that larger applications using these data mining algorithms do not have to be modified to benefit from this grid-based approach.

  • xg a data driven computation grid for Enterprise Scale mining
    Lecture Notes in Computer Science, 2005
    Co-Authors: Radu Sion, Inderpal S Narang, Ramesh Natarajan, Thomas Phan
    Abstract:

    In this paper we introduce a novel architecture for data processing, based on a functional fusion between a data and a computation layer. We show how such an architecture can be leveraged to offer significant speedups for data processing jobs such as data analysis and mining over large data sets. One novel contribution of our solution is its data-driven approach. The computation infrastructure is controlled from within the data layer. Grid compute job submission events are based within the query processor on the DBMS side and in effect controlled by the data processing job to be performed. This allows the early deployment of on-the-fly data aggregation techniques, minimizing the amount of data to be transfered to/from compute nodes and is in stark contrast to existing Grid solutions that interact with data layers mainly as external storage. We validate this in a scenario derived from a real business deployment, involving financial customer profiling using common types of data analytics (e.g., linear regression analysis). Experimental results show significant speedups. For example, using a grid of only 12 non-dedicated nodes, we observed a speedup of approximately 1000% in a scenario involving complex linear regression analysis data mining computations for commercial customer profiling.

Brian F Cooper - One of the best experts on this subject based on the ideXlab platform.

  • Middleware for Enterprise Scale data stream management using utility-driven self-adaptive information flows
    Cluster Computing, 2007
    Co-Authors: Vibhore Kumar, Zhongtang Cai, Brian F Cooper, Greg Eisenhauer, Karsten Schwan
    Abstract:

    We consider Enterprise-wide information flows that are responsible for acquiring, processing and delivering operational information across the business units. Middleware that enables such aggregation of data-streams must not only support scalable and efficient self-management to deal with changes in the operating conditions, but should also have an embedded business-sense to appreciate the business critical nature of some updates. In this paper, we present a novel self-adaptation algorithm that has been designed to Scale efficiently for thousands of streams and aims to maximize the overall business utility attained from running middleware-based applications. The outcome is that the middleware not only deals with changing network conditions or resource requirements, but also responds appropriately to changes in business policies. An important feature of the algorithm is a hierarchical node-partitioning scheme that decentralizes reconfiguration and suitably localizes its impact. Extensive simulation experiments and benchmarks attained with actual Enterprise operational data corroborate this paper’s claims.

  • imanage policy driven self management for Enterprise Scale systems
    ACM IFIP USENIX international conference on Middleware, 2007
    Co-Authors: Vibhore Kumar, Brian F Cooper, Greg Eisenhauer, Karsten Schwan
    Abstract:

    It is obvious that big, complex Enterprise systems are hard to manage. What is not obvious is how to make them more manageable. Although there is a growing body of research into system self-management, many techniques are either too narrow, focusing on a single component rather than the entire system, or not robust enough, failing to Scale or respond to the full range of an administrator's needs. In our iManage system we have developed a policy-driven system modeling framework that aims to bridge the gap between manageable components and manageable systems. In particular, iManage provides: (1) system state-space partitioning, which divides a large system state-space into partitions that are more amenable to constructing system models and developing policies, (2) online model and policy adaptation to allow the self-management infrastructure to deal gracefully with changes in operating environment, system configuration, and workload, and (3) tractability and trust, where tractability allows an administrator to understand why the system chose a particular policy and also influence that decision, and trust allows an administrator to understand the system's confidence in a proposed, automated action. Simulations driven by scenarios given to us by our industrial collaborators demonstrate that iManage is effective both at constructing useful system models and in using those models to drive automated system management.

  • utility driven proactive management of availability in Enterprise Scale information flows
    ACM IFIP USENIX international conference on Middleware, 2006
    Co-Authors: Zhongtang Cai, Vibhore Kumar, Brian F Cooper, Greg Eisenhauer, Karsten Schwan, Robert E Strom
    Abstract:

    Enterprises rely critically on the timely and sustained delivery of information. To support this need, we augment information flow middleware with new functionality that provides high levels of availability to distributed applications while at the same time maximizing the utility end users derive from such information. Specifically, the paper presents utility-driven ‘proactive availability-management' techniques to offer (1) information flows that dynamically self-determine their availability requirement based on high-level utility specifications, (2) flows that can trade recovery time for performance based on the ‘perceived' stability of and failure predictions (early alarm) for the underlying system, and (3) methods, based on real-world case studies, to deal with both transient and non-transient failures. Utility-driven ‘proactive availability-management' is integrated into information flow middleware and used with representative applications. Experiments reported in the paper demonstrate middleware capability to self-determine availability guarantees, to offer improved performance versus a statically configured system, and to be resilient to a wide range of faults.