Xianwen Hao

Northeastern University, Boston, Massachusetts, United States

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Publications (7)0 Total impact

  • Xianwen Hao, Yu Dai, Bin Zhang, Tingwei Chen
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    ABSTRACT: This paper focuses on the task migration enabling grid workflow application rescheduling problem, presents a reduced task graph model, and implements a performance oriented rescheduling algorithm based on immune genetic algorithm. The experiment shows that, compared with Adaptive Heterogeneous Earliest Finish Time static rescheduling algorithm and the classical dynamic Max-Min scheduling algorithm, the performance advantage of the proposed rescheduling algorithm is obvious, on the one hand because of the performance contribution of global optimization and task migration, and on the other hand because of the efficiency contribution of task graph reduction and immune genetic algorithm’s convergent speed. It also shows that task migration improves grid application’s adaptability of dynamics further.
    04/2008: pages 130-135;
  • Tingwei Chen, Bin Zhang, Xianwen Hao
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    ABSTRACT: In Grid computing, an application will be decomposed into a set of dependent tasks. In the Grid environment where resources have different capability and resources are interconnected over the world, the dependence among tasks affects the scheduling strategy greatly. This paper uses a Task-Resource Assignment Graph (T-RAG) to represent a potential resource assignment plan. And a dependent tasks scheduling model based on Best Task-Resource Assignment Graph (BT-RAG) construction is proposed which maps the dependent tasks scheduling problem into a graph construction problem. The BT-RAG is obtained and such graph is the optimal scheduling plan which determines the resource assignment plan and the execution order of tasks. Finally, the task scheduling algorithm based on the proposed scheduling model is implemented. Compared with HEFT algorithm, the proposed algorithm shows better performance in the situation of a large body of data transported among tasks.
    Progress in WWW Research and Development, 10th Asia-Pacific Web Conference, APWeb 2008, Shenyang, China, April 26-28, 2008. Proceedings; 01/2008
  • Xianwen Hao, Yu Dai, Bin Zhang, Tingwei Chen
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    ABSTRACT: The dynamic and heterogeneous nature of grid resources is a great challenge to static scheduling strategies of dependent tasks. A new static scheduling model based on RT-RAG (reduced task-resource assignment graph) and its algorithm are proposed, which are more suitable for the grid heterogeneity and support unified scheduling/rescheduling process. The (re) scheduling problem based on the model is thus regarded as an optimization selection problem of RT-RAGs. An experiment shows that the static scheduling algorithm has better adaptability to grid dynamic and heterogeneity and keeps greatly its superiority up in comparison to the dynamic one and it is also better than the AHEFT algorithm based on the DAG task graph model, especially in case of great resources heterogeneity.
    Dongbei Daxue Xuebao/Journal of Northeastern University 01/2008; 29(7).
  • Tingwei Chen, Bin Zhang, Xianwen Hao
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    ABSTRACT: We study the task scheduling in a grid environment and propose a task scheduling mechanism, i. e. each and every possible task scheduling scheme is expressed as a task-resource assignment graph, thus converting the task scheduling problem into a graphically optimal selection problem. Then, to find the optimal solution quickly and accurately, a task scheduling algorithm based on immune genetic algorithm is proposed to implement the optimal selection. This algorithm takes the longest path in the task-resource assignment graph as an antigen and every task-resource assignment as a corresponding antibody. Experimental results show that the approach proposed is effective in the capability of global optimization and significantly improves the convergence rate.
    Dongbei Daxue Xuebao/Journal of Northeastern University 01/2007; 28(3).
  • Tingwei Chen, Bin Zhang, Xianwen Hao, Yu Dai
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    ABSTRACT: Task scheduling is one of the core steps to effectively exploit the capabilities of resources in the grid. The task scheduling problem is an NP-complete problem. This paper studied on the task scheduling problem in grid environment and proposed a task scheduling mechanism, which expressed each possible task scheduling scheme as a task-resource assignment graph (T-RAG) and thus mapped the task scheduling problem into a graph optimal selection problem. Moreover, in order to find the optimal solution quickly and accurately, a task scheduling algorithm based on particle swarm optimization (PSO) was proposed. This algorithm regards the longest path of the task-resource assignment graph as fitness value and encodes every task-resource assignment as a particle. Finally, the experimentation shows that the approach proposed in this paper is effective to solve task scheduling problem
    Parallel and Distributed Computing, 2006. ISPDC '06. The Fifth International Symposium on; 08/2006
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    ABSTRACT: The dynamics nature of grid environment brings challenges for applications to offer nontrivial QoS on distributed, heterogeneous resources. It’s a better way to select the suitable grid resources constrained by QoS. In this paper we propose the application QoS model and metrics as the standard of resource selection. We also give consideration of the existence of data dependence between the tasks composing an application and apply it to the QoS model. And we solve the resource selection problem efficiently using novel neural networks.
    Advances in Grid and Pervasive Computing, First International Conference, GPC 2006, Taichung, Taiwan, May 3-5, 2006, Proceedings; 01/2006
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    ABSTRACT: In this paper we presented a novel approach to semantic resource discovery in the Grid, Where different organizations may use different ontologies to describe the resource and application requirements. We do not require a central ontology for resource description and matching. The relation matrix is defined to describe relations between concepts in any two ontologies. Then, a method of rewriting resource queries based on the relation matrix is presented to solve the ontology heterogeneity problem. It rewrites the resource queries in one ontology to approximate queries in another ontology based on the relations between concepts. Machines can process this procedure automatically.
    2005 International Conference on Semantics, Knowledge and Grid (SKG 2005), 27-29 November 2005, Beijing, China; 01/2005

Publication Stats

14 Citations

Institutions

  • 2008
    • Northeastern University
      Boston, Massachusetts, United States
  • 2006
    • Northeastern University (Shenyang, China)
      • College of Information Science and Engineering
      Feng-t’ien, Liaoning, China