**10. References**


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

For the choice of an appropriate allocation-plan, two of the important considerations that came out of these experimental results are the size of the Grid and the performance metric chosen for optimization. Generally, from the results obtained from the experiments conducted in chapter, it can be concluded that if an allocation-plan tries to minimize one of the performance metrics, it tends to yield higher values of the other performance metrics. For example, <SRPsp,ATSRAorg> always gives the lowest value of tcost but it also yields one of the highest values for tms-WOH , especially for a large number of nodes. At RA, the tradeoffs associated with reducing the accuracy of the ATSRA algorithm by relaxing some of the constraints in the LP formulation have been studied. The combination of the proposed RA algorithms and TRPS policies gives rise to various allocation-plans. These allocation-plans can be used under a wide variety of system and workload parameters to maximize the use

Although the research summarized in this chapter has focused primarily on the Grid systems, the proposed BiLeG architecture can also be used in a Cloud Computing environment. Cloud Computing environments are often classified as public and private Cloud environments [3]. The private Cloud environment is better suited for the BiLeG architecture; as a private Cloud environment uses a dedicated computing infrastructure that provides hosted services to a limited number of users behind a firewall and can, thus, more easily incorporate mechanisms

The algorithms presented in this chapter are based on a dedicated resource environment. To adapt the BiLeG architecture to shared environments, more research is required. For example, in order to use it in a shared resource environment, mechanisms to accurately predict the unit communication and processing times are needed to be incorporated in the BiLeG architecture. Also, in a shared environment, investigating the impact of various types of communication models, such as many-to-one and one-to-many forms, an important

[3] Abbas A. Grid Computing: A Practical Guide to Technology and Applications, Charles

[4] Ahmad I. and Majumdar S. Policies for Efficient Allocation of Grid Resources using a Bi-

[5] Ahmad I. and Majumdar S. An adaptive high performance architecture for "processable"

[7] Allcock B., Chervenak A., Foster I., Kesselman C. and Livny M. Data Grid tools: enabling

Systems and Computer Engineering, Carleton University, 2007.

level Decision-making Architecture of "Processable" Bulk Data. Department of

bulk data transfers on a Grid. In 2nd International Conference on Broadband Networks (Broadnets). (3-7 Oct. 2005). IEEE, Boston, MA, USA, 2005, 1482-91. [6] Ahmad I. and Majumdar S. Efficient Allocation of Grid Resources Using a Bi-level

Decision-Making Architecture for "Processable" Bulk Data. On the Move to Meaningful Internet Systems 2007: CoopIS, DOA, ODBASE, GADA, and IS, ( 2007),

science on big distributed data. Journal of Physics: Conference Series, 16, 1 ( 2005),

of available resources according to a pre-determined optimization objective.

to accurately predict the computing and communication costs.

[1] http://enterprise.amd.com/Downloads/Industry/Multimedia

[2] http://www.gridtoday.com/grid/638845.html.

direction for the future research.

River Media , 2004.

1313-1321.

571-5.

**10. References** 


**4** 

*Iran* 

**A New Approach to Resource** 

**Discovery in Grid Computing** 

The grid computing systems are one of great developments in the field of engineering and computer science and provide a clear future in the global use of various optimal distributed resources (hardware and software). Therefore with expanding grid systems and the importance of finding suitable resources for users (Foster & Kesselman, 2003), while saving time and space, resource discovery algorithms are very important. If one algorithm with less traffic and without reference to unnecessary nodes in a shorter time, can find the appropriate resource for users, it will significantly increase the efficiency of the system.

There are many approaches to resource discovery, such as flooding-based and randombased. These approaches decrease the system efficiency, because the users' requests pass through many unnecessary paths and create additional traffic. Therefore it is not suitable for

Another approach is resource discovery tree using bitmap (Chang & Hu, 2010). This method decreases some disadvantages of previous methods such as unnecessary traffic and heavy load, and furthermore the cost of update is low. But in this method, users' requests are also sent to unnecessary nodes, so in a grid environment with numerous nodes and requests, the reference to unnecessary nodes will create additional and heavy traffic and decrease the

In this work, we use a weighted tree for resource discovery (Khanli & Kargar, 2011). We only use one bitmap for the identification of available resources in nodes and also resources of children and their descendant nodes. The users' request must be transformed into this bitmap. We record a footprint of resources in nodes. When a user's query reaches every node, we can use this footprint to access the directly appropriate resource without visiting additional and unnecessary nodes and no time is consumed. We compare our algorithm

We discuss the previous works about the resource discovery in Section 2. In Section 3, we explain our proposed mechanism. Section 4 details the diagrams for comparing our method

with other algorithms and show that our algorithm is very efficient.

with previous methods, and finally, Section 5 contains conclusions.

**1. Introduction** 

a grid environment with numerous nodes.

efficiency of the system.

Saeed Kargar2 and Ali Kazemi Niari2

*2Islamic Azad University, Tabriz Branch* 

Leyli Mohammad Khanli1,

*1C.S. Dept., University of Tabriz* 

