摘要：Problem statement: This study is for effective scheduling of grid jobs based on economy for space shared resources in Bag of tasks grid. Grid Computing aims in combining the power of heterogeneous, geographically distributed, multi-domain computational resources to provide high performance or high throughput. Approach: Space shared resources are parallel supercomputers and clusters of workstations that provides a great amount of computational power. These resources require jobs to be specified formally in terms of the amount of time (tr) and number of processors (p) needed for execution. Bag-of-Tasks (BoT) is an application consists of several uniprocessor and independent tasks that have no inter-task communications or task-dependencies. BoT is highly suitable for execution in grids. It is capable of tolerating network delays or faults and does not require formal job submission. The Explicit allocation strategy assigns the formal job parameters (p, tr) to the job requests, minimizing the overhead on the grid users to provide a formal job specification. This strategy uses adaptive heuristics to determine the parameters based on certain heuristics, in order to improve throughput. In the proposed system, explicit allocation strategy combined with Deadline and Budget Constraint (DBC) Cost Time optimization algorithm performs effective scheduling of the jobs based on the users quality of service (QoS) requirements such as deadline, budget and optimization strategy. Results: The cost-time optimization scheduling allocates the cheapest resources to ensure that the deadline can be met and computation is minimized. In case if there are two resources with the same cost, scheduling is done in any affordable resource so that the job gets executed as early as possible. Conclusion: The performance of this scheme against the existing system is evaluated using cost factor (Cfactor) and speed up ratio (Tspeedup) and this scheme is more effective than the existing system.
关键词：Grid computing; bag of tasks; explicit allocation; space-shared resource
