A Dynamic Resource Broker and Fuzzy Logic Based Scheduling Algorithm in Grid Environment

Grid computing is a loosely couple distributed system, and it can solve complex problem with large-scale computing and storage resources. Middleware plays important role to integrate heterogeneous computing nodes. Globus Toolkit (GT) is a popular open source middleware to build grid environment. However, a job submission has lots of complicate operations in GT especially in a large scale gird. Moreover, the information discovery component of Globus Toolkit can only provide the summarized information from Grid Head instead of each computing node. Furthermore, job scheduling is another important issue in the high performance Grid computing. An appropriate scheduling algorithm can efficiently reduce the response time, turnaround time and increase the throughput. In this paper, we develop a resource broker module for GT infrastructure, which can dynamically describe and discover the resource information of computing nodes. Moreover, we design an adaptive fuzzy logic scheduler, which utilizes the fuzzy logic control technology to select the most suitable computing node in the Grid environment. For verifying the performance of the proposed scheduling algorithm, we also implement a resource broker as well as fuzzy logic scheduler based on Globus Toolkit 4. The experimental results show our algorithm can reduce the turnaround time compared with round-robin and random dispatching methods. The experiments also show that our algorithm has better speed-up ratio than round-robin and random dispatching when number of computing nodes increasing.

[1]  Ian T. Foster,et al.  Globus: a Metacomputing Infrastructure Toolkit , 1997, Int. J. High Perform. Comput. Appl..

[2]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..

[3]  Steven Tuecke,et al.  GridFTP: Protocol Extensions to FTP for the Grid , 2001 .

[4]  Soon M. Chung,et al.  Role-based access control for the open grid services architecture-data access and integration (ogsa-dai) , 2007 .

[5]  Steven Tuecke,et al.  Grid Services for Distributed System , 2002 .

[6]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[7]  Jem Treadwell,et al.  Open Grid Services Architecture , 2006, Grid-Based Problem Solving Environments.

[8]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[9]  Hu Zhigang,et al.  A scheduling algorithm aimed at time and cost for meta-tasks in grid computing using fuzzy applicability , 2005, Eighth International Conference on High-Performance Computing in Asia-Pacific Region (HPCASIA'05).

[10]  Gio Wiederhold,et al.  Scheduling under Uncertainty: Planning for the Ubiquitous Grid , 2002, COORDINATION.

[11]  Kenichi Hagihara,et al.  A comparison among grid scheduling algorithms for independent coarse-grained tasks , 2004, 2004 International Symposium on Applications and the Internet Workshops. 2004 Workshops..

[12]  Francine Berman,et al.  Overview of the Book: Grid Computing – Making the Global Infrastructure a Reality , 2003 .

[13]  Farhad Arbab,et al.  Coordination Models and Languages , 1998, Adv. Comput..

[14]  Hai Jin,et al.  An approach to grid scheduling optimization based on fuzzy association rule mining , 2005, First International Conference on e-Science and Grid Computing (e-Science'05).

[15]  Steven Tuecke,et al.  The Open Grid Services Architecture , 2004, The Grid 2, 2nd Edition.

[16]  Steven Tuecke,et al.  Enabling Scalable Virtual Organizations , 2001 .

[17]  Steven Tuecke,et al.  The Physiology of the Grid An Open Grid Services Architecture for Distributed Systems Integration , 2002 .