**6. References**

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

Fig. 14. Number of links that are visited by resource discovery queries for 300 queries.

**5. Conclusions and future work** 

In the last experiment, we supposed that there are 300 queries, and we show the visited links (traffic) which are caused during resource discovery in our method and compared with flooding-based, MMO and the resource discovery tree and for different numbers of nodes. In Fig. 14, we can see the traffic caused in our method is lower than other methods

In this work, we use a tree structure in which the edges have weight. The advantage of our method is that any node in our weighted tree has a unique path, so the user's query against all of previous methods is not sent to the extra and unnecessary paths. We can directly reach

Furthermore, for resource discovery we only use one bitmap in every node which is for the storing of information about its local resources and the resources of its children and descendant. Also it preserves a footprint of resources and if we need a resource which is available in its children or descendant, we can directly and without any referring to unnecessary and extra nodes, reach the target node. This method significantly reduces the

We compare our algorithm with previous algorithms using simulations and results and show that the number of nodes visited in our resource discovery algorithm is less than that for other algorithms, and the difference would be significant with an increase in the number

In future, if we could a present a technique that could locate several heterogeneous resources (with different attributes) of a grid environment in smaller forms with a lower volume, or placed in one bitmap involving some factors, for example allocated costs for

the target node using a resource footprint which is stored in nodes.

of nodes. Also the cost of update in our proposed algorithm is low.

system traffic and increases the performance of system.

resources etc., we could improve the algorithm.


**0**

**5**

<sup>1</sup>*India*

<sup>2</sup>*United Arab Emirates*

**Task Scheduling in Grid Environment Using**

**Simulated Annealing and Genetic Algorithm**

<sup>1</sup>*Department of Computer Science and Engineering, Osmania University*

<sup>2</sup>*Medical and Health Sciences University*

Wael Abdulal1, Ahmad Jabas1, S. Ramachandram1 and Omar Al Jadaan<sup>2</sup>

Grid computing enables access to geographically and administratively dispersed networked resources and delivers functionality of those resources to individual users. Grid computing systems are about sharing computational resources, software and data at a large scale. The main issue in grid system is to achieve high performance of grid resources. It requires techniques to efficiently and adaptively allocate tasks and applications to available resources

1. **Virtualization**: The Virtualization term in grids refers to seamless integration of geographically distributed and heterogeneous systems, which enables users to use the grid services transparently. Therefore, they should not be aware of the location of

in a large scale, highly heterogeneous and dynamic environment.

In order to understand grid systems, three terms are reviewed as shown below:

Fig. 1. Two virtual organizations are formed by combining a three real organizations

**1. Introduction**

