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Recently, due to the dramatic development of network technologies and the popularity of the Internet, grid computing has become an appealing research area (Jens, Martin, & Roman, 2009; Lee, Squicciarini, & Bertino, 2009). Computational grids are the next generation of computer clusters. They aim to maximize the utilization of resources owned by a set of distributed heterogeneous systems (He, Jarvis, Spooner, Bacigalupo, Tan, & Nudd, 2005; (Sacerdoti, Katz, Massie, & Culler, 2003). Moreover, grids can be considered as the recent instances of metacomputing (Wolski, Spring, & Hayes, 1999). The primary goal of grid computing is to provide a transparent access to geographically distributed heterogeneous resources owned by different individuals or organizations (Jen & Yuan, 2009). Hence, the grid provides hardware and software infrastructure to create an illusion of a virtual supercomputer that exploit the computational power aggregated from a huge set of distributed workers (Buyya, 1999). This allows the execution of tasks whose computational requirements exceed the available local resources. However, although the notion of grid computing is simple and attractive, its practical realization poses several challenges and open problems that need to be addressed (Tsai & Hung, 2009; Creel & Goffe, 2008). These challenges include resource discovery, failure management, fault tolerance, resource heterogeneity, reliability, scalability, security, and more importantly the scheduling of incoming tasks among available grid resources (Tseng, Chin, & Wang, 2009).
Scheduling is the major puzzle in developing a grid based computing paradigm (Tseng, Chin, & Wang, 2009; Iavarasan, Thambidurai, & Mahilmannan, 2005). It involves the matching of task or application requirements with the available resources (Tseng, Chin, & Wang, 2009). Scheduling in grids can be carried out in three different phases which are; (i) resource discovery, (ii) scheduling, and (iii) executing (Li & Hadjinicolaou, 2008). However, to achieve the expected potentials of the available resources, efficient scheduling algorithms are required (Daoud, & Kharma, 2008). Unluckily, scheduling algorithms previously employed in computer clusters can’t be used in grids as they run on homogenous and guaranteed resources over the same LAN. A Scheduler used in a computer cluster only manages such cluster; hence, it owns the resources with no ability to discover new ones (Sacerdoti, Katz, Massie, & Culler, 2003). Also it assumes both the availability and stability of resources. On the other hand, scheduling in grids is significantly complicated as a result of grid heterogeneity and dynamic nature (Kiran, Hassan, Kuan, & Yee, 2009). Unlike the cluster scheduler, a grid scheduler should have the ability to discover new computing resources over multiple administrative domains (Yan, Shen, Li, & Wu, 2005). The dynamic nature of grids is a result of both the network connectivity and grid resources. The network may be unreliable as it can’t guarantee its bandwidth. Moreover, grid resources change both availability and capability over time as they may join or leave the grid without any notification (Shah, Veeravalli, & Misra, 2007; Kousalya, & Balasubramanie, 2008).