**Section 2**

**Advances in Grid Computing - Resources Management** 

18 Will-be-set-by-IN-TECH

46 Grid Computing – Technology and Applications, Widespread Coverage and New Horizons

T. Yang and A. Gerasoulis., DSC: Scheduling Parallel Tasks on an Unbounded Number of

J. C. Liou, M. A. Palis., An Efficient Task Clustering Heuristic for Scheduling DAGs

Hidehiro Kanemitsu, Gilhyon Lee, Hidenori Nakazato, Takashige Hoshiai, and Yoshiyori

C. Boeres, J. V. Filho and V. E. F. Rebello, A Cluster-based Strategy for Scheduling Task on

B. Cirou, E. Jeannot, Triplet : a Clustering Scheduling Algorithm for Heterogeneous Systems,

S. Chingchit, M. Kumar and L.N. Bhuyan, A Flexible Clustering and Scheduling Scheme for

O. Sinnen and L. A. Sousa., Communication Contention in Task Scheduling, *IEEE Trans. on*

A. Gerasoulis and T. Yang., On the Granularity and Clustering of Directed Acyclic Task Graphs, *IEEE Trans. on Parallel And Distributed Systems*, Vol. 4, No. 6, June, 1993. H. Topcuoglu, S. Hariri, M. Y. Wu. : Performance-Effective and Low-Complexity Task

J. C. Liou and M. A. Palis., A Comparison of General Approaches to Multiprocessor

*High Performance Computing (SBAC-PAD'04)*, pp.214 – 221, 2004.

*Parallel and Distributed Systems*, Vol. 16, No. 6., pp. 503-515, 2005.

*Parallel and Distributed Processing*, pp. 500 – 505, 1999.

O. Sinnen., Task Scheduling for Parallel Systems, *Wiley*, 2007.

*Systems*, Vol. 13, No. 3 pp. 260-274, 2002.

1994.

*Processing*,October, 1996.

231 – 236, 2001.

– 156, 1997.

(ISBN: 1441969349), pp. 229 – 252, 2010.

Processors, *IEEE Trans. on Parallel and Distributed Systems*, Vol. 5, No. 9 pp. 951-967,

on Multiprocessors, *Procs. of the 8th Symposium on Parallel and Distributed*

Urano, Static Task Cluster Size Determination in Homogeneous Distributed Systems, *Software Automatic Tuning: from concepts to state-of-the-art results*, Springer-Verlag

Heterogeneous Processors, *Procs. of the 16th Symposium on Computer Architecture and*

*Procs. of 2001 International Conference on Parallel Processing Workshops (ICPPW'01)*, pp.

Efficient Parallel Computation, *Procs. of the 13th International and 10th Symposium on*

Scheduling for Heterogeneous Computing, , *IEEE Trans. on Parallel and Distributed*

Scheduling, *Procs. of the 11th International Symposium on Parallel Processing*, pp. 152

**3** 

*Canada* 

**Resource Management for** 

Imran Ahmad and Shikharesh Majumdar

*Carleton University, Ottawa,* 

**Data Intensive Tasks on Grids** 

The ubiquitous Internet as well as the availability of powerful computers and high-speed network technologies as low-cost commodity components are changing the way computing is carried out. It becomes more feasible to use widely distributed computers for solving large-scale problems, which cannot often be effectively dealt without using a single existing powerful supercomputer. In terms of computations and data requirements, these problems are often resource intensive due to their size and complexity. They may also involve the use of a variety of heterogeneous resources that are not usually available in a single location. This led to the emergence of what is known as Grid computing. Grid computing enables sharing of heterogeneous distributed resources across different administrative and geographical boundaries [3]. By sharing these distributed resources, many complex distributed tasks can be performed in a cost effective way. The way the resources are allocated to tasks holds a pivotal importance for achieving satisfactory system performance [4]. To perform efficiently, the resource allocation algorithm has to take into account many factors, such as, the system and workload conditions, type of the task to be performed and

To devise more efficient allocation algorithms, it may be useful to classify the given tasks into predefined types based on similarities in their predicted resource needs or workflows. This classification of tasks into various types provides the possibility to customize the allocation algorithm according to a particular group of similar tasks. This chapter presents an effective resource management middleware developed for a type of resource-intensive tasks classified as Processable Bulk Data Transfer (PBDT) tasks. The common trait among PBDT tasks is the transfer of a very large amount of data which has to be processed in some way before it can be delivered from a source node to a set of designated sink nodes (Ahmad, I & Majumdar, S. , 2008). Typically, these tasks can be broken down into parallel sub-tasks, called jobs. Various multimedia and High Energy Physics (HEP) applications can be classified as PBDT tasks. The processing operation involved in these tasks may be as simple as applying a compression algorithm to a raw video file in a multimedia application; or, as complex as isolating information about particles pertaining to certain wavelengths in High Energy Physics (HEP) experimentations [22][25]. Performing PBDT tasks requires both computing power and large bandwidths for data transmission. To perform such resourceintensive tasks, in recent years, research has been conducted in devising effective resource

**1. Introduction** 

the requirements of the end user.
