Traditional algorithms have been used in determining solutions for the grid scheduling problem. However, it has been witnessed in the recent times that the increased complexity and size of the job have been a reason f...
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Traditional algorithms have been used in determining solutions for the grid scheduling problem. However, it has been witnessed in the recent times that the increased complexity and size of the job have been a reason for the degradation in algorithm's performance. This led to the emergence of a new class of heuristic algorithms that gives optimal or near-optimal solutions to optimization problems. The proposed algorithm solves multi-objective grid scheduling problem and performs further optimization. The proposed swarm-based algorithm is a threshold-constrained Ant Colony Optimization using Shortest Job First (SJF) in initialization step. The characteristics of proposed algorithm are as follows. It minimizes the job completion (execution) time and enhances resource utilization. It supports load balancing and scalability. The performance test of algorithms is carried out using a real-time dataset, which can set benchmark results for future research. Simulation results reveal that the proposed swarm algorithm obtains promising results over other algorithms taken for comparison.
We study a new kind of on-line bin packing, motivated by a problem arising when scheduling jobs on the grid. In this bin packing problem, the set of items is given at the beginning, and variable-sized bins arrive one ...
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We study a new kind of on-line bin packing, motivated by a problem arising when scheduling jobs on the grid. In this bin packing problem, the set of items is given at the beginning, and variable-sized bins arrive one by one. We analyze the problem using both the competitive ratio and the relative worst order ratio, observing that the two measures often lead to different conclusions. A closely related problem was introduced by Zhang (Discrete Appl. Math. 72:193-197, 1997). Our main result answers a question posed in that paper in the affirmative: we give an algorithm with a competitive ratio strictly better than 2, for our problem as well as Zhang's problem. For identical bins, the new algorithm has essentially the same performance as FFD (First-Fit-Decreasing).
To achieve the ultimate success of global collaborative resource sharing in grid computing, an effective and efficient grid resource management system is necessary and it is only possible if its core component, the sc...
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To achieve the ultimate success of global collaborative resource sharing in grid computing, an effective and efficient grid resource management system is necessary and it is only possible if its core component, the scheduler, can perform scheduling in an efficient manner. scheduling tasks to resources in grid computing is a challenging task and known as a NP hard problem. In this paper, we propose a novel hybrid heuristic-based algorithm, which synergised the excellent diversification capability of Great Deluge (GD) algorithm with the powerful systematic multi-neighbourhood search strategy captured in Variable Neighbourhood Descent (VND) algorithm, to efficiently schedule independent tasks in grid computing environment with an objective of minimising the makespan. Simulation experiments have been conducted to examine the impact of hybridising GD and VND. In addition, the performance of the proposed algorithm has been evaluated and compared with some other recent meta-heuristics in the literature. The experimental simulation results show that our proposed algorithm outperforms the other algorithms in the literature and the performance improvement achieved by this hybrid strategy is effective and efficient with respect to makespan and computational time as it can obtain good quality (makespan) of solutions while obviating the drawback of requiring high computational cost from the VND. (C) 2019 Elsevier B.V. All rights reserved.
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