A new generalized particle approach to allot resources and jobs for grid computing

This paper presents a new generalized particle approach (GPA) to optimally allot the resources and jobs for grid computing (Shuai and Zhao, 2004). The proposed GPA transforms the allocation problem of grid resources and grid jobs into the kinematics and dynamics of massive particles in a force-field. This paper discusses the construction, dynamics and properties of the GPA and corresponding algorithm. The GPA has many advantages in terms of the parallelism, multiobjective optimization, multitype coordination, and the ability to deal with a variety of complex issues, such as the autonomy, personality, congestion, failure of distinct grid facilities. The simulations have shown the effectiveness and suitability of the proposed approach for grid computing.

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

[2]  Rajkumar Buyya,et al.  A taxonomy and survey of grid resource management systems for distributed computing , 2002, Softw. Pract. Exp..

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

[4]  Sarit Kraus,et al.  Emergent Cooperative Goal-Satisfaction in Large Scale Automated-Agent Systems , 1999, Artif. Intell..

[5]  Bo Li,et al.  On network bandwidth allocation policies and feedback control algorithms for packet networks , 2000, Comput. Networks.

[6]  Frank Kelly,et al.  Charging and rate control for elastic traffic , 1997, Eur. Trans. Telecommun..

[7]  Yin-Fu Huang,et al.  A priority-based resource allocation strategy in distributed computing networks , 2001, J. Syst. Softw..

[8]  Laurent Massoulié,et al.  Bandwidth sharing: objectives and algorithms , 2002, TNET.

[9]  Sarit Kraus,et al.  Methods for Task Allocation via Agent Coalition Formation , 1998, Artif. Intell..

[10]  Athanasios V. Vasilakos,et al.  Aggregated bandwidth allocation: investigation of performance of classical constrained and genetic algorithm based optimisation techniques , 2002, Comput. Commun..

[11]  Soundararajan Chandramathi,et al.  Fuzzy-based dynamic bandwidth allocation for heterogeneous sources in ATM networks , 2003, Appl. Soft Comput..

[12]  Frank Kelly,et al.  Mathematical modeling of the Internet , 1999 .

[13]  Gianfranco Lamperti,et al.  Diagnosis of Large Active Systems , 1999, Artif. Intell..

[14]  O.R. Liu Sheng,et al.  Optimal data allocation in a bus computer network , 1990, Ninth Annual International Phoenix Conference on Computers and Communications. 1990 Conference Proceedings.

[15]  Babak Hamidzadeh,et al.  Dynamic Task Scheduling Using Online Optimization , 2000, IEEE Trans. Parallel Distributed Syst..

[16]  Ahmad B. Rad,et al.  A study of the generalised max-min fair rate allocation for ABR control in ATM , 1999, Comput. Commun..

[17]  R. F. Freund,et al.  Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems , 1999, J. Parallel Distributed Comput..

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

[19]  Hongbin Zhao,et al.  A new generalized cellular automata approach to optimization of fast packet switching , 2004, Comput. Networks.

[20]  Ian T. Foster,et al.  The anatomy of the grid: enabling scalable virtual organizations , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.

[21]  Jonathan Robinson,et al.  Hector: an agent based architecture for dynamic resource management , 1999, IEEE Concurr..

[22]  Frank Kelly,et al.  Rate control for communication networks: shadow prices, proportional fairness and stability , 1998, J. Oper. Res. Soc..