The Experts below are selected from a list of 4977 Experts worldwide ranked by ideXlab platform

Hisao Kameda - One of the best experts on this subject based on the ideXlab platform.

  • Uniqueness of Performance Variables in Static Load Balancing
    Optimal Load Balancing in Distributed Computer Systems, 1997
    Co-Authors: Hisao Kameda, Chong-gun Kim, Yongbing Zhang
    Abstract:

    Important performance optimization problems in the communication networks and distributed computer systems may include the problem of how to obtain the optimal routing (or flow assignment) policy for sending data through the communication channels and the problem of how to balance the processing of jobs among processors (i.e. Static Load Balancing). For example, an optimal Static routing policy determines the rate at which data flow through each path connecting a given pair of origin and destination points, so as to minimize the overall mean data transmission time. We can regard the combination of such rates as the solution of the optimization problem. These optimization problems have close relations to the optimal traffic assignment problems in the transportation science [Daf72, Mag84].

  • Load Balancing in Single Channel and Star Network Configurations
    Optimal Load Balancing in Distributed Computer Systems, 1997
    Co-Authors: Hisao Kameda, Chong-gun Kim, Yongbing Zhang
    Abstract:

    In this chapter, the Static Load Balancing in single channel and star network configurations is studied. Tantawi and Towsley [TT84, TT85] studied Load Balancing of single class jobs in a distributed computer system that consists of a set of heterogeneous host computers connected by single channel and star communications networks. They considered an optimal Static Load Balancing strategy which determines the optimal Load at each host so as to minimize the mean job response time, and derived two algorithms (called single-point algorithms) that determines the optimal Load at each host for given system parameters. Static Load Balancing may be useful for system sizing (e.g., allocation of resources, identification of bottleneck, sensitivity studies, etc.). The solution of optimal Static Load Balancing may help us design the system.

  • Comparison of dynamic and Static Load-Balancing strategies in heterogeneous distributed systems
    IEE Proceedings - Computers and Digital Techniques, 1997
    Co-Authors: Yongbing Zhang, Hisao Kameda, S.-l. Hung
    Abstract:

    Although dynamic Load-Balancing strategies have the potential of performing better than Static strategies, they are inevitably more complex. Their complexity and the overheads involved may negate their benefits. A heterogeneous distributed system, with computers of different processing capability but the same functionality, has been examined for two dynamic and two Static policies. The results show that both the dynamic and the Static policies provide dramatic performance improvements. However, they show that, contrary to common belief, the performance provided by the Static policies is not much inferior to that provided by the dynamic policies. Furthermore, if the overheads in Load Balancing are not negligibly small, Static policies are more stable and can offer better performance than dynamic policies.

  • a performance comparison of adaptive and Static Load Balancing in heterogeneous distributed systems
    Annual Simulation Symposium, 1995
    Co-Authors: Yongbing Zhang, Hisao Kameda, K Hakozaki, K Shimizu
    Abstract:

    This paper focuses on using simulation to compare the performances of adaptive and Static Load Balancing policies in a heterogeneous distributed system model. All the hosts (nodes) an the system are assumed to have the same function but possibly different processing capacities. The overheads and the delays for both job transfer and system state-information exchange are assumed to be nonnegligible. Simulation results show that both adaptive and Static policies improve performance dramatically, and that the performance provided by Static policies is not much inferior to that provided by adaptive policies. They also show that when overheads are nonnegligibly high at heavy system Loads, Static policies can provide performance more stable and better than that provided by adaptive policies. >

  • A decomposition algorithm for optimal Static Load Balancing in tree hierarchy network configurations
    IEEE Transactions on Parallel and Distributed Systems, 1994
    Co-Authors: Hisao Kameda
    Abstract:

    We study the Static Load Balancing problem in a distributed computer system with the tree hierarchy configuration. It is formulated as a nonlinear optimization problem. After studying the conditions that the solution to the optimization problem of the tree hierarchy network satisfies, we demonstrate that the special structure of the optimization problem leads to an interesting decomposition technique. A new effective decomposition algorithm to solve the optimization problem is presented. The proposed algorithm Is compared with two other well known algorithms: the Flow Deviation (FD) algorithm and the Dafermos-Sparrow (D-S) algorithm. It is shown that the amounts of the storage required for the proposed algorithm and the FD algorithm are O(n) for Load Balancing of an n-node system. However, the amount of the storage required for the D-S algorithm is O(n log(n)). By using numerical experiments, we show that both the proposed algorithm and the D-S algorithm have much faster convergence in terms of central processing unit (CPU) time than the FD algorithm. >

D. Giménez - One of the best experts on this subject based on the ideXlab platform.

  • Heterogeneous pipelined square-root Kalman Filter algorithm for the MMSE-OSIC problem
    The Journal of Supercomputing, 2011
    Co-Authors: F. J. Martínez-zaldívar, A. M. Vidal-maciá, D. Giménez
    Abstract:

    This paper describes a pipelined parallel algorithm for the MMSE-OSIC decoding procedure proposed in V-BLAST wireless MIMO systems, for heterogeneous networks of processors. It is based on a block version of the square-root Kalman Filter algorithm that was initially devised to solve the RLS problem. It has been parallelized in a pipelined way obtaining a good efficiency and scalability. The optimum Load Balancing for this parallel algorithm is dynamic, but we derive a Static Load Balancing scheme with good performance.

  • CLUSTER - A pipelined parallel OSIC algorithm based on the square root Kalman Filter for heterogeneous networks
    2007 IEEE International Conference on Cluster Computing, 2007
    Co-Authors: F. J. Martínez-zaldívar, A. M. Vidal-maciá, D. Giménez
    Abstract:

    This paper describes a pipelined parallel algorithm for the Ordered Successive Interference Cancellation (OSIC) decoding procedure proposed in V-BLAST wireless MIMO systems. It is based on an algorithm that solves the recursive least squares (RLS) problem, and is derived from a block version of the square root version of the Kalman Filter. It has been parallelized in a pipelined way getting a good efficiency and scalability in a heterogeneous network of computers. Although the optimum Load Balancing for this algorithm is dynamic, we derive a Static Load Balancing scheme with good results.

Robert Kessl - One of the best experts on this subject based on the ideXlab platform.

  • Probabilistic Static Load-Balancing of Parallel Mining of Frequent Sequences
    IEEE Transactions on Knowledge and Data Engineering, 2016
    Co-Authors: Robert Kessl
    Abstract:

    Frequent sequence mining is well known and well studied problem in datamining. The output of the algorithm is used in many other areas like bioinformatics, chemistry, and market basket analysis. Unfortunately, the frequent sequence mining is computationally quite expensive. In this paper, we present a novel parallel algorithm for mining of frequent sequences based on a Static Load-Balancing. The Static Load-Balancing is done by measuring the computational time using a probabilistic algorithm. For reasonable size of instance, the algorithms achieve speedups up to $\approx 3/4\cdot P$ where $P$ is the number of processors. In the experimental evaluation, we show that our method performs significantly better then the current state-of-the-art methods. The presented approach is very universal: it can be used for Static Load-Balancing of other pattern mining algorithms such as itemset/tree/graph mining algorithms.

  • Static Load Balancing of parallel mining of frequent itemsets using reservoir sampling
    Machine Learning and Data Mining in Pattern Recognition, 2011
    Co-Authors: Robert Kessl
    Abstract:

    In this paper, we present a novel method for parallelization of an arbitrary depth-first search (DFS in short) algorithm for mining of all FIs. The method is based on the so called reservoir sampling algorithm. The reservoir sampling algorithm in combination with an arbitrary DFS mining algorithm executed on a database sample takes an uniformly but not independently distributed sample of all FIs using the reservoir sampling. The sample is then used for Static Load-Balancing of the computational Load of a DFS algorithm for mining of all FIs.

  • MLDM - Static Load Balancing of parallel mining of frequent itemsets using reservoir sampling
    Machine Learning and Data Mining in Pattern Recognition, 2011
    Co-Authors: Robert Kessl
    Abstract:

    In this paper, we present a novel method for parallelization of an arbitrary depth-first search (DFS in short) algorithm for mining of all FIs. The method is based on the so called reservoir sampling algorithm. The reservoir sampling algorithm in combination with an arbitrary DFS mining algorithm executed on a database sample takes an uniformly but not independently distributed sample of all FIs using the reservoir sampling. The sample is then used for Static Load-Balancing of the computational Load of a DFS algorithm for mining of all FIs.

K Chili - One of the best experts on this subject based on the ideXlab platform.

  • a Static Load Balancing scheme for parallel xml parsing on multicore cpus
    Cluster Computing and the Grid, 2007
    Co-Authors: Yinfei Pan, Ying Zhang, K Chili
    Abstract:

    A number of techniques to improve the parsing performance of XML have been developed. Generally, however, these techniques have limited impact on the construction of a DOM tree, which can be a significant bottleneck. Meanwhile, the trend in hardware technology is toward an increasing number of cores per CPU. As we have shown in previous work, these cores can be used to parse XML in parallel, resulting in significant speedups. In this paper, we introduce a new Static partitioning and Load-Balancing mechanism. By using a Static, global approach, we reduce synchronization and Load-Balancing overhead, thus improving performance over dynamic schemes for a large class of XML documents. Our approach leverages libxm12 without modification, which reduces development effort and shows that our approach is applicable to real-world, production parsers. Our scheme works well with Sun's Niagara class of CMT architectures, and shows that multiple hardware threads can be effectively used for XML parsing.

  • CCGRID - A Static Load-Balancing Scheme for Parallel XML Parsing on Multicore CPUs
    Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07), 2007
    Co-Authors: Yinfei Pan, Ying Zhang, K Chili
    Abstract:

    A number of techniques to improve the parsing performance of XML have been developed. Generally, however, these techniques have limited impact on the construction of a DOM tree, which can be a significant bottleneck. Meanwhile, the trend in hardware technology is toward an increasing number of cores per CPU. As we have shown in previous work, these cores can be used to parse XML in parallel, resulting in significant speedups. In this paper, we introduce a new Static partitioning and Load-Balancing mechanism. By using a Static, global approach, we reduce synchronization and Load-Balancing overhead, thus improving performance over dynamic schemes for a large class of XML documents. Our approach leverages libxm12 without modification, which reduces development effort and shows that our approach is applicable to real-world, production parsers. Our scheme works well with Sun's Niagara class of CMT architectures, and shows that multiple hardware threads can be effectively used for XML parsing.

F. J. Martínez-zaldívar - One of the best experts on this subject based on the ideXlab platform.

  • Heterogeneous pipelined square-root Kalman Filter algorithm for the MMSE-OSIC problem
    The Journal of Supercomputing, 2011
    Co-Authors: F. J. Martínez-zaldívar, A. M. Vidal-maciá, D. Giménez
    Abstract:

    This paper describes a pipelined parallel algorithm for the MMSE-OSIC decoding procedure proposed in V-BLAST wireless MIMO systems, for heterogeneous networks of processors. It is based on a block version of the square-root Kalman Filter algorithm that was initially devised to solve the RLS problem. It has been parallelized in a pipelined way obtaining a good efficiency and scalability. The optimum Load Balancing for this parallel algorithm is dynamic, but we derive a Static Load Balancing scheme with good performance.

  • CLUSTER - A pipelined parallel OSIC algorithm based on the square root Kalman Filter for heterogeneous networks
    2007 IEEE International Conference on Cluster Computing, 2007
    Co-Authors: F. J. Martínez-zaldívar, A. M. Vidal-maciá, D. Giménez
    Abstract:

    This paper describes a pipelined parallel algorithm for the Ordered Successive Interference Cancellation (OSIC) decoding procedure proposed in V-BLAST wireless MIMO systems. It is based on an algorithm that solves the recursive least squares (RLS) problem, and is derived from a block version of the square root version of the Kalman Filter. It has been parallelized in a pipelined way getting a good efficiency and scalability in a heterogeneous network of computers. Although the optimum Load Balancing for this algorithm is dynamic, we derive a Static Load Balancing scheme with good results.